{
  "built_at": "2026-10-19",
  "candidates": [
    {
      "abstract": "Rendering document text as images allows vision-language models to encode documents as visual tokens, which can reduce input sequence length compared with text input. This reduction in input length is particularly useful for reranking, where each query involves scoring multiple candidate documents and token savings apply to each candidate evaluation.",
      "arxiv_id": "2609.35069",
      "citations": 0,
      "curated_by": [],
      "fit": {
        "score": 6,
        "signals": [
          {
            "code": "coreTask",
            "tasks": [
              "retrieval"
            ]
          },
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-05",
      "found_by": "catalogue",
      "published": "2026-09-28",
      "repositories": [],
      "source": "https://paperswithcode.co/api/v1/papers/2609.35069",
      "tasks": [
        "retrieval",
        "text-reranking"
      ],
      "title": "RenderRank: Learning to Rerank Text with Compressed Visual Tokens",
      "verdict": null
    },
    {
      "abstract": "Autonomous research agents must acquire and reason over evidence within a limited budget. Yet relevant sources can remain uninspected or unused, leaving a gap between retrieval coverage and the evidence that informs research. The challenge is to align fast local judgments with changing evidence needs and feed their findings back into research decisions.",
      "arxiv_id": "2610.18272",
      "citations": 0,
      "curated_by": [],
      "fit": {
        "score": 5,
        "signals": [
          {
            "code": "nearTask",
            "tasks": [
              "question-answering"
            ]
          },
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-10-19",
      "found_by": "catalogue",
      "published": "2026-10-13",
      "repositories": [],
      "source": "https://paperswithcode.co/api/v1/papers/2610.18272",
      "tasks": [
        "auto-research",
        "deep-research-agents",
        "question-answering",
        "text-reranking"
      ],
      "title": "JevGPT: Thinking Fast and Slow in Autonomous Research",
      "verdict": null
    },
    {
      "abstract": "Modern information systems, including many agentic workflows, use dense retrieval to explore large amounts of unstructured data. However, dense retrieval relies on surface-level semantic similarity, which is insufficient for increasingly complex search applications.",
      "arxiv_id": "2610.05750",
      "citations": 0,
      "curated_by": [],
      "fit": {
        "score": 5,
        "signals": [
          {
            "code": "coreTask",
            "tasks": [
              "embedding-models",
              "retrieval"
            ]
          },
          {
            "code": "mechanismWeak",
            "count": 4
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "catalogue",
      "published": "2026-10-05",
      "repositories": [],
      "source": "https://paperswithcode.co/api/v1/papers/2610.05750",
      "tasks": [
        "embedding-models",
        "reinforcement-learning",
        "retrieval"
      ],
      "title": "Beyond Semantic Similarity: Performance and Costs of Agentic Retrieval for Complex Tasks",
      "verdict": null
    },
    {
      "abstract": "Retrieval-Augmented Generation (RAG) is widely used to mitigate hallucinations of Large Language Models (LLMs) by leveraging external knowledge. While effective for simple queries, traditional RAG systems struggle with large-scale, unstructured corpora where information is fragmented. Recent advances incorporate knowledge graphs to capture relational structures, enabling more comprehensive retrieval for complex, multi-hop reasoning tasks.",
      "arxiv_id": "2510.10114",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG"
      ],
      "fit": {
        "score": 5,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG"
            ]
          },
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 5
          }
        ]
      },
      "found_at": "2026-08-13",
      "published": "2025-10-11",
      "repositories": [],
      "source": "https://arxiv.org/abs/2510.10114",
      "tasks": [],
      "title": "LinearRAG: Linear Graph Retrieval Augmented Generation on Large-scale Corpora",
      "verdict": null
    },
    {
      "abstract": "Graph Retrieval-Augmented Generation (Graph RAG) effectively builds a knowledge graph (KG) to connect disparate facts across a large document corpus. However, this broad-view approach often lacks the deep structured reasoning needed for complex multi-hop question answering (QA), leading to incomplete evidence and error accumulation. To address these limitations, we propose SubQRAG, a sub-question-driven framework that enhances reasoning depth.",
      "arxiv_id": "2510.07718",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG"
      ],
      "fit": {
        "score": 5,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG"
            ]
          },
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-08-13",
      "published": "2025-10-09",
      "repositories": [],
      "source": "https://arxiv.org/abs/2510.07718",
      "tasks": [],
      "title": "SUBQRAG: Sub-Question Driven Dynamic Graph RAG",
      "verdict": null
    },
    {
      "abstract": "Large Language Models (LLMs) have impressive capabilities in text understanding and zero-shot reasoning. However, delays in knowledge updates may cause them to reason incorrectly or produce harmful results. Knowledge Graphs (KGs) provide rich and reliable contextual information for the reasoning process of LLMs by structurally organizing and connecting a wide range of entities and relations.",
      "arxiv_id": "2504.03137",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG"
      ],
      "fit": {
        "score": 5,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG"
            ]
          },
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-08-13",
      "published": "2025-04-04",
      "repositories": [],
      "source": "https://arxiv.org/abs/2504.03137",
      "tasks": [],
      "title": "LightPROF: A Lightweight Reasoning Framework for Large Language Model on Knowledge Graph",
      "verdict": null
    },
    {
      "abstract": "Graph-RAG constructs a knowledge graph from text chunks to improve retrieval in Large Language Model (LLM)-based question answering. It is particularly useful in domains such as biomedicine, law, and political science, where retrieval often requires multi-hop reasoning over proprietary documents.",
      "arxiv_id": "2502.09304",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG"
      ],
      "fit": {
        "score": 5,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG"
            ]
          },
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 4
          }
        ]
      },
      "found_at": "2026-08-13",
      "published": "2025-02-13",
      "repositories": [],
      "source": "https://arxiv.org/abs/2502.09304",
      "tasks": [],
      "title": "KET-RAG: A Cost-Efficient Multi-Granular Indexing Framework for Graph-RAG",
      "verdict": null
    },
    {
      "abstract": "Research question answering requires accurate retrieval and contextual understanding of scientific literature. However, current Retrieval-Augmented Generation (RAG) methods often struggle to balance complex document relationships with precise information retrieval.",
      "arxiv_id": "2501.15067",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG"
      ],
      "fit": {
        "score": 5,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG"
            ]
          },
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-08-13",
      "published": "2025-01-25",
      "repositories": [],
      "source": "https://arxiv.org/abs/2501.15067",
      "tasks": [],
      "title": "CG-RAG: Research Question Answering by Citation Graph Retrieval-Augmented LLMs",
      "verdict": null
    },
    {
      "abstract": "Large language models with retrieval-augmented generation encounter a pivotal challenge in intricate retrieval tasks, e.g., multi-hop question answering, which requires the model to navigate across multiple documents and generate comprehensive responses based on fragmented information. To tackle this challenge, we introduce a novel Knowledge Graph-based RAG framework with a hierarchical knowledge retriever, termed KG-Retriever.",
      "arxiv_id": "2412.05547",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG",
        "Graph-RAG survey list"
      ],
      "fit": {
        "score": 5,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG",
              "Graph-RAG survey list"
            ]
          },
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "curated",
      "published": "2024-12-07",
      "repositories": [],
      "source": "https://arxiv.org/abs/2412.05547",
      "tasks": [],
      "title": "KG-Retriever: Efficient Knowledge Indexing for Retrieval-Augmented Large Language Models",
      "verdict": null
    },
    {
      "abstract": "Large language models (LLMs) have demonstrated impressive reasoning abilities, but they still struggle with faithful reasoning due to knowledge gaps and hallucinations. To address these issues, knowledge graphs (KGs) have been utilized to enhance LLM reasoning through their structured knowledge. However, existing KG-enhanced methods, either retrieval-based or agent-based, encounter difficulties in accurately retrieving knowledge and efficiently traversing KGs at scale.",
      "arxiv_id": "2410.13080",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG"
      ],
      "fit": {
        "score": 5,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG"
            ]
          },
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-08-13",
      "published": "2024-10-16",
      "repositories": [],
      "source": "https://arxiv.org/abs/2410.13080",
      "tasks": [],
      "title": "Graph-constrained Reasoning: Faithful Reasoning on Knowledge Graphs with Large Language Models",
      "verdict": null
    },
    {
      "abstract": "The ability to form, retrieve, and reason about memories in response to stimuli serves as the cornerstone for general intelligence - shaping entities capable of learning, adaptation, and intuitive insight. Large Language Models (LLMs) have proven their ability, given the proper memories or context, to reason and respond meaningfully to stimuli.",
      "arxiv_id": "2409.15566",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG",
        "Graph-RAG survey list"
      ],
      "fit": {
        "score": 5,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG",
              "Graph-RAG survey list"
            ]
          },
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 4
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "curated",
      "published": "2024-09-23",
      "repositories": [],
      "source": "https://arxiv.org/abs/2409.15566",
      "tasks": [],
      "title": "GEM-RAG: Graphical Eigen Memories For Retrieval Augmented Generation",
      "verdict": null
    },
    {
      "abstract": "Strong multi-hop retrieval should be practical to deploy without expensive corpus preparation or complex database infrastructure. We present SAG2, an embedding-free retrieval architecture built around a single SQLite database, BM25, and contextual evidence decisions from JEV. SAG2 follows the query-time association principle of SAG (SQL-Retrieval Augmented Generation), while replacing its event-entity and dense indexes with a lexical index over original passages.",
      "arxiv_id": "2610.22588",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 4,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismStrong",
            "count": 8
          }
        ]
      },
      "found_at": "2026-10-19",
      "found_by": "archive",
      "published": "2026-10-15",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.22588",
      "tasks": [],
      "title": "SAG2: Embedding-Free Multi-Hop Retrieval with BM25 and JEV",
      "verdict": null
    },
    {
      "abstract": "Long-context inference and Retrieval-Augmented Generation (RAG) handle evidence selection at vastly different scales, from a single long prompt to an entire corpus. We ask whether a single model-internal mechanism can select evidence across this range. We introduce UNifying REtrieval And Long-Context with a Single Model (UNREAL), a model-native evidence selection framework to span corpus retrieval and long-context inference.",
      "arxiv_id": "2610.08463",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 4,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismStrong",
            "count": 7
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-10-06",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.08463",
      "tasks": [],
      "title": "UNREAL: Unifying Retrieval and Long-Context with a Single Model",
      "verdict": null
    },
    {
      "abstract": "Retrieval-augmented generation (RAG) is a widely used approach for grounding large language models (LLMs) in external knowledge. However, configuring a pipeline is an expensive hyperparameter optimization problem over many interacting choices, from chunking and embedding model to reranking and generation.",
      "arxiv_id": "2610.08452",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 4,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismStrong",
            "count": 6
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-10-06",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.08452",
      "tasks": [],
      "title": "Agentic AutoRAG: RAG Pipeline Optimization through Reasoning-Driven Agents",
      "verdict": null
    },
    {
      "abstract": "Long-context reasoning in large language models incurs substantial computation and memory costs. Visual text compression (VTC) reduces input length by rendering text as images, but fixed-resolution rendering creates a compression-performance trade-off: low DPI saves tokens at the expense of legibility, whereas high DPI spends tokens on irrelevant content.",
      "arxiv_id": "2609.36651",
      "citations": 0,
      "curated_by": [],
      "fit": {
        "score": 4,
        "signals": [
          {
            "code": "nearTask",
            "tasks": [
              "reasoning"
            ]
          },
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-05",
      "found_by": "catalogue",
      "published": "2026-09-29",
      "repositories": [],
      "source": "https://paperswithcode.co/api/v1/papers/2609.36651",
      "tasks": [
        "long-context",
        "reasoning"
      ],
      "title": "FocusVTC: Efficient and High-Performance Visual Text Compression with Adaptive Resolution",
      "verdict": null
    },
    {
      "abstract": "Cancer registrars, including Oncology Data Specialists (ODSs), must interpret complex and frequently updated coding and staging standards. We developed CRISS (Cancer Registry Intelligent Support System), a retrieval-augmented generation (RAG) conversational assistant that provides rapid, citation-supported access to registry guidance.",
      "arxiv_id": "2609.29075",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 4,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismStrong",
            "count": 6
          }
        ]
      },
      "found_at": "2026-09-28",
      "found_by": "archive",
      "published": "2026-09-24",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.29075",
      "tasks": [],
      "title": "CRISS: A Retrieval-Augmented AI Chatbot for Assisting Cancer Registrars",
      "verdict": null
    },
    {
      "abstract": "An advanced retrieval-augmented generation pipeline is typically assembled from three or four independently operated systems: an approximate nearest-neighbour index, a full-text search engine, a graph database, and a relational document store. Each contributes its own deployment surface, configuration model, and failure modes, and the integration logic that binds them is written anew in every project.",
      "arxiv_id": "2609.18248",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 4,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismStrong",
            "count": 6
          }
        ]
      },
      "found_at": "2026-09-21",
      "found_by": "archive",
      "published": "2026-09-16",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.18248",
      "tasks": [],
      "title": "Quanta: A Self-Contained Python Library for Hybrid Retrieval over Quantised Embeddings, Lexical Indexes, and Knowledge Graphs",
      "verdict": null
    },
    {
      "abstract": "Retrieval-Augmented Generation (RAG) has made dense retrieval over large document collections a standard building block. Organizations increasingly outsource vector indexes to untrusted clouds, exposing proprietary corpora and user queries. Cryptographic protection is challenging because each query searches corpus-scale state, causing computation, correlated randomness, and communication to grow with the corpus.",
      "arxiv_id": "2609.03376",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 4,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismStrong",
            "count": 7
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-09-03",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.03376",
      "tasks": [],
      "title": "Spruce: Scalable Private Outsourced Retrieval Using Compact Embeddings",
      "verdict": null
    },
    {
      "abstract": "Recent optimization studies on Retrieval-Augmented Generation (RAG) have exploited chunk-level KV cache reuse to avoid processing long retrieved contexts for higher efficiency, while significant information redundancy and noise still remain in the coarse-grained chunks. This paper optimizes the Pareto frontier under low prefill latency constraints while maximizing accuracy by proposing CoinRAG (Contextualized Information Nugget KV Cache Reuse for Long-Context RAG).",
      "arxiv_id": "2608.07458",
      "citations": 0,
      "curated_by": [],
      "fit": {
        "score": 4,
        "signals": [
          {
            "code": "nearTask",
            "tasks": [
              "question-answering"
            ]
          },
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-08-13",
      "published": "2026-08-07",
      "repositories": [],
      "source": "https://paperswithcode.co/api/v1/papers/2608.07458",
      "tasks": [
        "question-answering"
      ],
      "title": "CoinRAG: Contextualized Information Nugget KV Cache Reuse for Long-Context RAG",
      "verdict": null
    },
    {
      "abstract": "Multi-step retrieval-augmented generation (RAG) has become a widely adopted strategy for enhancing large language models (LLMs) on tasks that demand global comprehension and intensive reasoning. Although many RAG systems incorporate a working memory to consolidate information, existing designs primarily function as a passive storage for isolated facts.",
      "arxiv_id": "2512.23959",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG"
      ],
      "fit": {
        "score": 4,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG"
            ]
          },
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-08-13",
      "published": "2025-12-30",
      "repositories": [],
      "source": "https://arxiv.org/abs/2512.23959",
      "tasks": [],
      "title": "HGMEM: Hypergraph-based Working Memory to Improve Multi-step RAG for Long-Context Complex Relational Modeling",
      "verdict": null
    },
    {
      "abstract": "Graph-based retrieval-augmented generation (Graph-based RAG) has demonstrated significant potential in enhancing Large Language Models (LLMs) with structured knowledge. However, existing methods face three critical challenges: Inaccurate Graph Construction, caused by LLM hallucination; Poor Reasoning Ability, caused by failing to generate explicit reasons telling LLM why certain chunks were selected; and Inadequate Answering, which only partially answers the query due to the…",
      "arxiv_id": "2511.05549",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG"
      ],
      "fit": {
        "score": 4,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG"
            ]
          },
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-08-13",
      "published": "2025-11-02",
      "repositories": [],
      "source": "https://arxiv.org/abs/2511.05549",
      "tasks": [],
      "title": "AGRAG: Advanced Graph-based Retrieval-Augmented Generation for LLMs",
      "verdict": null
    },
    {
      "abstract": "Building effective knowledge graphs (KGs) for Retrieval-Augmented Generation (RAG) is pivotal for advancing question answering (QA) systems. However, its effectiveness is hindered by a fundamental disconnect: the knowledge graph (KG) construction process is decoupled from its downstream application, yielding suboptimal graph structures.",
      "arxiv_id": "2510.15339",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG"
      ],
      "fit": {
        "score": 4,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG"
            ]
          },
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-08-13",
      "published": "2025-10-17",
      "repositories": [],
      "source": "https://arxiv.org/abs/2510.15339",
      "tasks": [],
      "title": "AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction",
      "verdict": null
    },
    {
      "abstract": "Current knowledge-enhanced large language models (LLMs) rely on static, pre-constructed knowledge bases that suffer from coverage gaps and temporal obsolescence, limiting their effectiveness in dynamic information environments. We present Agentic-KGR, a novel framework enabling co-evolution between LLMs and knowledge graphs (KGs) through multi-round reinforcement learning (RL).",
      "arxiv_id": "2510.09156",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG"
      ],
      "fit": {
        "score": 4,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG"
            ]
          },
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-08-13",
      "published": "2025-10-10",
      "repositories": [],
      "source": "https://arxiv.org/abs/2510.09156",
      "tasks": [],
      "title": "Agentic-KGR: Co-evolutionary Knowledge Graph Construction through Multi-Agent Reinforcement Learning",
      "verdict": null
    },
    {
      "abstract": "Graph Retrieval-Augmented Generation (GraphRAG) enhances factual reasoning in LLMs by structurally modeling knowledge through graph-based representations. However, existing GraphRAG approaches face two core limitations: shallow retrieval that fails to surface all critical evidence, and inefficient utilization of pre-constructed structural graph data, which hinders effective reasoning from complex queries.",
      "arxiv_id": "2509.22009",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG"
      ],
      "fit": {
        "score": 4,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG"
            ]
          },
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-08-13",
      "published": "2025-09-26",
      "repositories": [],
      "source": "https://arxiv.org/abs/2509.22009",
      "tasks": [],
      "title": "GraphSearch: An Agentic Deep Searching Workflow for Graph Retrieval-Augmented Generation",
      "verdict": null
    },
    {
      "abstract": "Graph retrieval-augmented generation (GraphRAG) has effectively enhanced large language models in complex reasoning by organizing fragmented knowledge into explicitly structured graphs. Prior efforts have been made to improve either graph construction or graph retrieval in isolation, yielding suboptimal performance, especially when domain shifts occur.",
      "arxiv_id": "2508.19855",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG"
      ],
      "fit": {
        "score": 4,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG"
            ]
          },
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-08-13",
      "published": "2025-08-27",
      "repositories": [],
      "source": "https://arxiv.org/abs/2508.19855",
      "tasks": [],
      "title": "Youtu-GraphRAG: Vertically Unified Agents for Graph Retrieval-Augmented Complex Reasoning",
      "verdict": null
    },
    {
      "abstract": "Standard Retrieval-Augmented Generation (RAG) relies on chunk-based retrieval, whereas GraphRAG advances this approach by graph-based knowledge representation. However, existing graph-based RAG approaches are constrained by binary relations, as each edge in an ordinary graph connects only two entities, limiting their ability to represent the n-ary relations (n >= 2) in real-world knowledge.",
      "arxiv_id": "2503.21322",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG",
        "Graph-RAG survey list"
      ],
      "fit": {
        "score": 4,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG",
              "Graph-RAG survey list"
            ]
          },
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "curated",
      "published": "2025-03-27",
      "repositories": [],
      "source": "https://arxiv.org/abs/2503.21322",
      "tasks": [],
      "title": "HyperGraphRAG: Retrieval-Augmented Generation via Hypergraph-Structured Knowledge Representation",
      "verdict": null
    },
    {
      "abstract": "Recent advances in graph learning have paved the way for innovative retrieval-augmented generation (RAG) systems that leverage the inherent relational structures in graph data. However, many existing approaches suffer from rigid, fixed settings and significant engineering overhead, limiting their adaptability and scalability.",
      "arxiv_id": "2503.19314",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG",
        "Graph-RAG survey list"
      ],
      "fit": {
        "score": 4,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG",
              "Graph-RAG survey list"
            ]
          },
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "curated",
      "published": "2025-03-25",
      "repositories": [],
      "source": "https://arxiv.org/abs/2503.19314",
      "tasks": [],
      "title": "RGL: A Graph-Centric, Modular Framework for Efficient Retrieval-Augmented Generation on Graphs",
      "verdict": null
    },
    {
      "abstract": "Despite notable advancements in Retrieval-Augmented Generation (RAG) systems that expand large language model (LLM) capabilities through external retrieval, these systems often struggle to meet the complex and diverse needs of real-world industrial applications. The reliance on retrieval alone proves insufficient for extracting deep, domain-specific knowledge performing in logical reasoning from specialized corpora.",
      "arxiv_id": "2501.11551",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG"
      ],
      "fit": {
        "score": 4,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG"
            ]
          },
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-08-13",
      "published": "2025-01-20",
      "repositories": [],
      "source": "https://arxiv.org/abs/2501.11551",
      "tasks": [],
      "title": "PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generation",
      "verdict": null
    },
    {
      "abstract": "Given a semi-structured knowledge base (SKB), where text documents are interconnected by relations, how can we effectively retrieve relevant information to answer user questions? Retrieval-Augmented Generation (RAG) retrieves documents to assist large language models (LLMs) in question answering; while Graph RAG (GRAG) uses structured knowledge bases as its knowledge source.",
      "arxiv_id": "2412.16311",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG"
      ],
      "fit": {
        "score": 4,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG"
            ]
          },
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-08-13",
      "published": "2024-12-20",
      "repositories": [],
      "source": "https://arxiv.org/abs/2412.16311",
      "tasks": [],
      "title": "HybGRAG: Hybrid Retrieval-Augmented Generation on Textual and Relational Knowledge Bases",
      "verdict": null
    },
    {
      "abstract": "Large Language Models (LLMs) have shown remarkable reasoning capabilities on complex tasks, but they still suffer from out-of-date knowledge, hallucinations, and opaque decision-making. In contrast, Knowledge Graphs (KGs) can provide explicit and editable knowledge for LLMs to alleviate these issues. Existing paradigm of KG-augmented LLM manually predefines the breadth of exploration space and requires flawless navigation in KGs.",
      "arxiv_id": "2410.23875",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG"
      ],
      "fit": {
        "score": 4,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG"
            ]
          },
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-08-13",
      "published": "2024-10-31",
      "repositories": [],
      "source": "https://arxiv.org/abs/2410.23875",
      "tasks": [],
      "title": "Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs",
      "verdict": null
    },
    {
      "abstract": "Fake news threatens democracy and exacerbates the polarization and divisions in society; therefore, accurately detecting online misinformation is the foundation of addressing this issue. We present CrediRAG, the first fake news detection model that combines language models with access to a rich external political knowledge base with a dense social network to detect fake news across social media at scale.",
      "arxiv_id": "2410.12061",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG",
        "Graph-RAG survey list"
      ],
      "fit": {
        "score": 4,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG",
              "Graph-RAG survey list"
            ]
          },
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "curated",
      "published": "2024-10-15",
      "repositories": [],
      "source": "https://arxiv.org/abs/2410.12061",
      "tasks": [],
      "title": "CrediRAG: Network-Augmented Credibility-Based Retrieval for Misinformation Detection in Reddit",
      "verdict": null
    },
    {
      "abstract": "Embodied Everyday Task is a popular task in the embodied AI community, requiring agents to make a sequence of actions based on natural language instructions and visual observations. Traditional learning-based approaches face two challenges. Firstly, natural language instructions often lack explicit task planning. Secondly, extensive training is required to equip models with knowledge of the task environment.",
      "arxiv_id": "2409.11279",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG",
        "Graph-RAG survey list"
      ],
      "fit": {
        "score": 4,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG",
              "Graph-RAG survey list"
            ]
          },
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "curated",
      "published": "2024-09-17",
      "repositories": [],
      "source": "https://arxiv.org/abs/2409.11279",
      "tasks": [],
      "title": "P-RAG: Progressive Retrieval Augmented Generation For Planning on Embodied Everyday Task",
      "verdict": null
    },
    {
      "abstract": "Naive Retrieval-Augmented Generation (RAG) focuses on individual documents during retrieval and, as a result, falls short in handling networked documents which are very popular in many applications such as citation graphs, social media, and knowledge graphs.",
      "arxiv_id": "2405.16506",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG"
      ],
      "fit": {
        "score": 4,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG"
            ]
          },
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-08-13",
      "published": "2024-05-26",
      "repositories": [],
      "source": "https://arxiv.org/abs/2405.16506",
      "tasks": [],
      "title": "GRAG: Graph Retrieval-Augmented Generation",
      "verdict": null
    },
    {
      "abstract": "Graph retrieval-augmented generation (GraphRAG) uses document-derived knowledge graphs to assemble evidence for large language model (LLM) answers. Its security depends on both the seed chunks retrieved for a query and the graph evidence assembled from them, yet attacks on this evidence path remain underexplored. We introduce GraphRAG-LTM, a Layered Threat Modeling framework that tests three nested levels of attacker access.",
      "arxiv_id": "2610.19549",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-10-19",
      "found_by": "archive",
      "published": "2026-10-14",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.19549",
      "tasks": [],
      "title": "GraphRAG-LTM: A Layered Threat Modeling Framework for Attacks on Graph Retrieval-Augmented Generation",
      "verdict": null
    },
    {
      "abstract": "Long-term memory remains a fundamental bottleneck for large language model (LLM) agents across sessions. Existing systems typically adopt a write-then-retrieve paradigm, storing context-dependent dialogue fragments and retrieving them via nearest-neighbor search in a continuous embedding space. This design yields unstable access units and systematically misses logically related but lexically distant evidence.",
      "arxiv_id": "2610.19472",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-10-19",
      "found_by": "archive",
      "published": "2026-10-14",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.19472",
      "tasks": [],
      "title": "MemCode: Discrete Semantic Representations for Long-Term Agent Memory",
      "verdict": null
    },
    {
      "abstract": "This paper presents AutoSec-RAN, a specification-grounded large language model (LLM) framework for automated security triage of Open Radio Access Network (O-RAN) testing. Unlike approaches relying on static rule sets or privileged access to raw packet captures, AutoSec-RAN fine-tunes Mistral-7B-Instruct through two Low-Rank Adaptation (LoRA) stages over a curated corpus of 69 O-RAN, 3GPP, and adjacent standards documents, then selectively augments inference with retrieval-aug…",
      "arxiv_id": "2610.15995",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 4
          }
        ]
      },
      "found_at": "2026-10-19",
      "found_by": "archive",
      "published": "2026-10-11",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.15995",
      "tasks": [],
      "title": "AutoSec-RAN: Specification-Grounded LLM Fine-Tuning for Automated Security Analysis of O-RAN",
      "verdict": null
    },
    {
      "abstract": "Retrieval-Augmented Generation (RAG) has become a cornerstone in software engineering for enhancing Large Language Models (LLMs) with domain-specific knowledge. However, adapting retrievers to evolving code repositories remains challenging due to the noise and redundancy inherent in massive code corpora. Standard fine-tuning on the full corpus is computationally expensive and often leads to sub-optimal performance due to negative transfer from low-quality samples.",
      "arxiv_id": "2610.11727",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-10-08",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.11727",
      "tasks": [],
      "title": "MAP4CS: A Multi-dimensional Data Pruning Framework for Efficient Code Retriever Fine-tuning",
      "verdict": null
    },
    {
      "abstract": "Retrieval-augmented generation (RAG) grounds language models in external corpora. Agentic RAG enables iterative search, yet exposes the model to isolated chunks without document structure, making it difficult to distinguish relevant evidence from chunks that merely resemble the query. Structure-aware methods such as PageIndex navigate document structure but cannot scale to the structures of large corpora, which do not fit in the LLM context.",
      "arxiv_id": "2610.11370",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-10-08",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.11370",
      "tasks": [],
      "title": "RIT-RAG: Navigating Document Corpora with Retrieval-Induced Trees",
      "verdict": null
    },
    {
      "abstract": "We present BEACON-SP, an ontology-grounded Graph Retrieval-Augmented Generation (GraphRAG) framework for clinician-facing decision support in behavioral health settings such as suicide prevention, where effective assessment requires integrating heterogeneous clinical, behavioral, social, and temporal evidence.",
      "arxiv_id": "2610.09026",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 4
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-10-06",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.09026",
      "tasks": [],
      "title": "BEACON-SP: Ontology-Grounded GraphRAG Framework for Clinical Suicide Risk Assessment",
      "verdict": null
    },
    {
      "abstract": "Recent advancements in large language models have introduced new capabilities for reasoning over structured data, particularly through program-aided tools that can analyze tables. However, many existing methods address single-table scenarios or assume that the relevant tables are already provided. In practice, users often issue complex data exploration queries over entire databases, where relevant information may be distributed across multiple relations.",
      "arxiv_id": "2610.07622",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-10-06",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.07622",
      "tasks": [],
      "title": "DBRAG: Multi-Table Retrieval-Augmented Generation for Complex Database Queries",
      "verdict": null
    },
    {
      "abstract": "Long-term memory systems for large language models (LLMs) have gained popularity for extending reasoning capabilities across applications. Current memory systems rely on LLM agents to organize and consolidate memory, resulting in costly, inefficient write operations. To address this limitation, we propose MemFit, a long-term memory system for conversational agents that reduces the cost and latency of memory operations.",
      "arxiv_id": "2610.00872",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-10-01",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.00872",
      "tasks": [],
      "title": "MemFit: Efficient Long-Term Agentic Memory",
      "verdict": null
    },
    {
      "abstract": "While hypergraph-based Retrieval-Augmented Generation (RAG) effectively captures higher-order multi-entity correlations, existing paradigms treat extracted hyperedges as isolated factual assertions. This structural fragmentation engenders rigid \"knowledge islands\" that bottleneck multi-hop causal inference, temporal tracking, and narrative synthesis.",
      "arxiv_id": "2609.28108",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-09-28",
      "found_by": "archive",
      "published": "2026-09-23",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.28108",
      "tasks": [],
      "title": "Dual-Hypergraph Indexing: Bridging Knowledge Islands for Multi-Hop Reasoning in Retrieval-Augmented Generation",
      "verdict": null
    },
    {
      "abstract": "Modern information retrieval (IR) systems rarely represent time, yet many information needs depend on it: in clinical, journalistic, and legal search, when an event occurred can decide whether a document is relevant. Dense retrievers and Retrieval-Augmented Generation (RAG) pipelines match queries to documents well on topic but poorly on time, so they surface content that is on-topic yet temporally wrong.",
      "arxiv_id": "2609.28048",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 4
          }
        ]
      },
      "found_at": "2026-09-28",
      "found_by": "archive",
      "published": "2026-09-23",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.28048",
      "tasks": [],
      "title": "TEMPS: Temporal Sentence Embeddings for Temporal Information Retrieval",
      "verdict": null
    },
    {
      "abstract": "An agent that interacts with users over long periods must recall facts, preferences, events, and changes from a continuously growing interaction history. Existing memory systems often compress interactions into generic summaries or retrieve anonymous text chunks, making it difficult for an agent to identify the correct entity, property, and supporting evidence. We present EnSIMem, an entity-structured long-term memory architecture for an agent.",
      "arxiv_id": "2609.27279",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-09-28",
      "found_by": "archive",
      "published": "2026-09-23",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.27279",
      "tasks": [],
      "title": "EnSIMem: Entity-Structured Indexing for Long-Term Agent Memory",
      "verdict": null
    },
    {
      "abstract": "Open-ended queries in modern Retrieval-Augmented Generation (RAG) are increasingly \"diffuse,\" requiring a large set of documents to be assembled into a finite LLM context window. To ensure retrieval quality, systems use fast dual-encoders and more expensive cross-encoders (CEs) to score candidates. However, the CE budget $B$ is strictly bounded by latency and is often smaller than the context window capacity $k$.",
      "arxiv_id": "2609.27213",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-09-28",
      "found_by": "archive",
      "published": "2026-09-23",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.27213",
      "tasks": [],
      "title": "BoundaryMORPH: Budgeted Reranking via Active Set Selection for Diffuse Retrieval",
      "verdict": null
    },
    {
      "abstract": "Retrieval-augmented generation (RAG) gives large language models (LLMs) access to external knowledge, but its conventional retrieve-concatenate-generate pipeline makes retrieval decisions on behalf of the model. As tool use and agent loops become more reliable, an agent can decide whether to retrieve, what to inspect, and when to stop. This shift exposes a new bottleneck: the agent may not know what a knowledge base contains.",
      "arxiv_id": "2609.25991",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 4
          }
        ]
      },
      "found_at": "2026-09-28",
      "found_by": "archive",
      "published": "2026-09-22",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.25991",
      "tasks": [],
      "title": "Knowledge-as-Skill: A Structural Design for Autonomous Knowledge-Base Use by LLM Agents",
      "verdict": null
    },
    {
      "abstract": "Retrieval augmented generation (RAG) systems have emerged as the dominant architecture for grounding large language model (LLM) outputs in verifiable external knowledge, yet their structural reliance on a dynamic retrieval pipeline introduces a largely unexplored class of adversarial vulnerability. Existing knowledge-base poisoning attacks are fundamentally static.",
      "arxiv_id": "2609.25469",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-09-28",
      "found_by": "archive",
      "published": "2026-09-21",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.25469",
      "tasks": [],
      "title": "RAG-NAROK: Retrieval-Aware Knowledge Corpus Poisoning in RAG with Source-specific Refutation",
      "verdict": null
    },
    {
      "abstract": "Retrieval-augmented generation (RAG) is hard to monitor in production: exhaustive relevance labels do not exist for non-stationary multi-million-passage corpora that re-index in real time. As a result, retrieval quality is generally understudied and often deprioritised in favour of generation-oriented metrics. In this work, we propose auditing retrieval coverage by probing for evidence of missing documents rather than enumerating every relevant one.",
      "arxiv_id": "2609.24122",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 5
          }
        ]
      },
      "found_at": "2026-09-28",
      "found_by": "archive",
      "published": "2026-09-21",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.24122",
      "tasks": [],
      "title": "Re:CAP - Auditing Retrieval Coverage in Production RAG Pipelines",
      "verdict": null
    },
    {
      "abstract": "Retrieval-Augmented Generation (RAG) improves large language models by grounding outputs in external knowledge sources, but this dependency also creates a surface for poisoning attacks. This paper introduces Micro-Collaborative Poisoning, a distributed attack in which a false target claim is divided across multiple locally plausible documents instead of being concentrated in a single malicious passage.",
      "arxiv_id": "2609.21573",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-09-21",
      "found_by": "archive",
      "published": "2026-09-18",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.21573",
      "tasks": [],
      "title": "Micro-Collaborative Poisoning: A Distributed Attack on RAG Systems",
      "verdict": null
    },
    {
      "abstract": "Retrieval-Augmented Generation (RAG) is increasingly used to ground large language model (LLM) outputs in scientific literature. However, in open-ended literature exploration, the evidence context used for generation is often produced through hidden retrieval, reranking, assessment, and filtering steps.",
      "arxiv_id": "2609.19601",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-09-21",
      "found_by": "archive",
      "published": "2026-09-17",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.19601",
      "tasks": [],
      "title": "FootprintRAG: Visual Analytics for Evidence Context Refinement in RAG-based Scientific Literature Exploration",
      "verdict": null
    },
    {
      "abstract": "Real-world agents fundamentally require persistent non-parametric knowledge for dynamic reasoning, i.e., long-term memory and retrieval-augmented generation. While graphs have shown reliable advantages in providing structured evidence, the sparse graph representations naturally restrict machine readability and semantic density required for complex agentic workflows.",
      "arxiv_id": "2609.18182",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 4
          }
        ]
      },
      "found_at": "2026-09-21",
      "found_by": "archive",
      "published": "2026-09-16",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.18182",
      "tasks": [],
      "title": "WFM: Wiki Foundation Model for Complex Agentic Reasoning",
      "verdict": null
    },
    {
      "abstract": "Language models face unique challenges in analyzing interdisciplinary scientific research literature. In biophysics research, faithful answers require grounding observed data in source evidence, interpreting it through a quantitative physics model, and linking it to a biological mechanism. To address this challenge, we introduce BioPhys-Bridge, a novel benchmark dataset for evidence-grounded scientific reasoning over biophysical literature.",
      "arxiv_id": "2609.19180",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-09-21",
      "found_by": "archive",
      "published": "2026-09-15",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.19180",
      "tasks": [],
      "title": "BioPhys-Bridge: A Benchmark for Interdisciplinary Scientific Reasoning in Physics-Grounded Biological Research",
      "verdict": null
    },
    {
      "abstract": "Retrieval-Augmented Generation (RAG) pipelines typically rely on a fixed indexing and retrieval configuration determined at preprocessing time. This one-size-fits-all design is ill-suited to domain-expert settings, where heterogeneous queries require different chunking granularities, metadata constraints, and source-selection strategies. As a result, configurations that are effective for one family of queries often perform poorly for others.",
      "arxiv_id": "2609.17012",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 5
          }
        ]
      },
      "found_at": "2026-09-21",
      "found_by": "archive",
      "published": "2026-09-15",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.17012",
      "tasks": [],
      "title": "ORDER: Task-Conditioned Routing for Retrieval-Augmented Generation",
      "verdict": null
    },
    {
      "abstract": "Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for improving the quality of generated contents of Large Language Models (LLMs) by grounding responses in external knowledge, thus reducing hallucinations and factual errors. However, recent studies have highlighted a critical vulnerability: adversaries can exploit the retrieval process to extract personally identifiable information (PII) from the underlying corpus.",
      "arxiv_id": "2609.16095",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-09-21",
      "found_by": "archive",
      "published": "2026-09-14",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.16095",
      "tasks": [],
      "title": "RAG-CT: Mitigating Privacy Risks on Retrieval-Augmented Generation Systems via Scanning Prompt Distribution",
      "verdict": null
    },
    {
      "abstract": "Dense retrieval systems are core components of modern search engines, recommendation platforms, and retrieval-augmented generation pipelines. They encode documents and queries into dense embeddings, enabling efficient semantic search via vector similarity. However, because document embeddings are computed offline and stored in static indexes, these systems struggle to adapt to user feedback or evolving search intent.",
      "arxiv_id": "2609.05110",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 5
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-09-04",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.05110",
      "tasks": [],
      "title": "Embedding Surgery: Localized Updates for Adaptive Ranking Correction in Dense Retrieval",
      "verdict": null
    },
    {
      "abstract": "Traditional Retrieval-Augmented Generation (RAG) systems score each passage independently against the query, assembling context sets that may be individually relevant yet collectively incoherent. We introduce Coherence-Aware Graph Encoding (CAGE), a reranking framework that models \"between-chunk coherence\" across four dimensions: Intra-Domain Relevance, Noise Resistance, Informational Bonding, and Factual Consistency.",
      "arxiv_id": "2609.04647",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 5
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-09-04",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.04647",
      "tasks": [],
      "title": "CAGE: Coherence-Aware Graph Encoding for Retrieval-Augmented Generation",
      "verdict": null
    },
    {
      "abstract": "Automated short-answer grading research has historically focused on exams consisting solely of questions pertaining to a single topic. Automatic grading of exams containing questions about more than one topic remains less explored. In this work, a Graph-Retrieval Automated Scoring Pipeline (GRASP) is introduced for grading label-free multi-topic science exams.",
      "arxiv_id": "2609.03857",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-09-03",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.03857",
      "tasks": [],
      "title": "GRASP: Graph-Retrieval Automated Scoring Pipeline for Label-Free Multi-Topic Essay Grading",
      "verdict": null
    },
    {
      "abstract": "Neural ranking models have become core components of modern information retrieval systems and important building blocks of AI systems such as retrieval-augmented generation (RAG) pipelines. However, their robustness remains insufficiently understood in the presence of large language models (LLMs), which can generate fluent and deceptive content at scale.",
      "arxiv_id": "2609.01325",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-09-01",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.01325",
      "tasks": [],
      "title": "VerTox: Verifiable Reward-Guided Corpus Poisoning Against Neural Ranking Models",
      "verdict": null
    },
    {
      "abstract": "Retrieval-Augmented Generation (RAG) mitigates large language models (LLMs) hallucinations, yet conventional dense retrieval struggles with the complex reasoning paths of multi-hop question answering (QA). Graph-based RAG captures multi-step relationships but suffers from severe semantic drift and high online latency due to noisy global graph traversals. Thus, we propose ISO-RAG (ISOperimetric Retrieval-Augmented Generation), a geometry-aware RAG framework.",
      "arxiv_id": "2609.00513",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-09-01",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.00513",
      "tasks": [],
      "title": "ISO-RAG: Isoperimetric Noise Control for Retrieval-Augmented Generation",
      "verdict": null
    },
    {
      "abstract": "For emerging scientific research domains, local Small Language Models (SLMs) are becoming more attractive, as they offer stronger privacy control and more stable deployment pipelines than Large Language Models. However, in practice, scientific question-answering on SLMs often operates under inevitable constraints: small literature collections, fragmented evidence, limited context window and reasoning abilities.",
      "arxiv_id": "2609.00479",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-08-31",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.00479",
      "tasks": [],
      "title": "EGT-KG: Evidence-Grounded Typed KG Retrieval for Practical Scientific QA with Small Language Models",
      "verdict": null
    },
    {
      "abstract": "Retrieval-Augmented Generation (RAG) grounds large language models in external corpora, but implicit trust in retrieved documents creates a critical attack surface: PoisonedRAG shows that a handful of crafted passages can dominate dense retrieval and steer generation toward attacker-chosen answers.",
      "arxiv_id": "2609.00470",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 4
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-08-31",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.00470",
      "tasks": [],
      "title": "TRIS: A Tri-Layer Retrieval Integrity Sieve Against Knowledge Poisoning",
      "verdict": null
    },
    {
      "abstract": "Ontology alignment (OA) has evolved through several methodological paradigms, ranging from lexical and structural aligners to knowledge graph embedding (KGE) models and, more recently, Large Language Model (LLM)-based approaches. Although modern OA frameworks provide unified ecosystems for deploying these heterogeneous aligners, mechanisms for systematically reconciling their complementary and sometimes conflicting predictions remain relatively underexplored.",
      "arxiv_id": "2608.31137",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-08-31",
      "repositories": [],
      "source": "https://arxiv.org/abs/2608.31137",
      "tasks": [],
      "title": "OntoAligner-Ensemble: Voting-Based Fusion across Heterogeneous Ontology Alignment Techniques",
      "verdict": null
    },
    {
      "abstract": "Flat retrieval-augmented generation treats a corpus as a bag of chunks, discarding document hierarchy and cross document structure. We introduce SearchWiki, a harness framework that synthesizes a corpus into a hierarchical, typed, navigable wiki and trains an agent, WikiResearcher-9B, to retrieve information through multi-turn tool use.",
      "arxiv_id": "2608.29953",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-08-30",
      "repositories": [],
      "source": "https://arxiv.org/abs/2608.29953",
      "tasks": [],
      "title": "SearchWiki: Learning to Build and Navigate Knowledge Wikis for Active Information Seeking",
      "verdict": null
    },
    {
      "abstract": "How small can a competitive multilingual retrieval model be? We present Bekko Embedding: its smallest model, bekko-embedding-v1-a8m, has just under 8M Active Parameters (AP) -- the non-embedding parameters that dominate inference compute -- yet on official MMTEB Multilingual v2 Retrieval (nDCG@10) it scores 56.2, above the multilingual-e5 family and BGE-M3 (40x the AP) in our comparison.",
      "arxiv_id": "2607.25180",
      "citations": 0,
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "mechanismWeak",
            "count": 5
          }
        ]
      },
      "found_at": "2026-08-12",
      "published": "2026-07-28",
      "repositories": [],
      "source": "https://paperswithcode.co/api/v1/papers/2607.25180",
      "tasks": [],
      "title": "Bekko Embedding: Parameter-Efficient Multilingual Retrieval with Ultra-Compact Encoders",
      "verdict": null
    },
    {
      "abstract": "Graph-based RAG methods like GraphRAG have shown promising global understanding of the knowledge base by constructing hierarchical entity graphs. However, they often suffer from inefficiency and rely on manually pre-defined query modes, limiting practical use. In this paper, we propose E^2GraphRAG, a streamlined graph-based RAG framework that improves both Efficiency and Effectiveness.",
      "arxiv_id": "2505.24226",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG"
      ],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG"
            ]
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-08-13",
      "published": "2025-05-30",
      "repositories": [],
      "source": "https://arxiv.org/abs/2505.24226",
      "tasks": [],
      "title": "E^2GraphRAG: Streamlining Graph-based RAG for High Efficiency and Effectiveness",
      "verdict": null
    },
    {
      "abstract": "Retrieval-Augmented Generation (RAG) improves large language models (LLMs) by retrieving relevant information from external sources and has been widely adopted for text-based tasks. For structured data, such as knowledge graphs, Graph Retrieval-Augmented Generation (GraphRAG) retrieves and aggregates information along graph structures.",
      "arxiv_id": "2502.11371",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG",
        "Graph-RAG survey list"
      ],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG",
              "Graph-RAG survey list"
            ]
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "curated",
      "published": "2025-02-17",
      "repositories": [],
      "source": "https://arxiv.org/abs/2502.11371",
      "tasks": [],
      "title": "RAG vs. GraphRAG: A Systematic Evaluation and Key Insights",
      "verdict": null
    },
    {
      "abstract": "Retrieval-augmented generation (RAG) has emerged as a promising technology for addressing hallucination issues in the responses generated by large language models (LLMs). Existing studies on RAG primarily focus on applying semantic-based approaches to retrieve isolated relevant chunks, which ignore their intrinsic relationships.",
      "arxiv_id": "2502.06864",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG"
      ],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG"
            ]
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-08-13",
      "published": "2025-02-08",
      "repositories": [],
      "source": "https://arxiv.org/abs/2502.06864",
      "tasks": [],
      "title": "Knowledge Graph-Guided Retrieval Augmented Generation",
      "verdict": null
    },
    {
      "abstract": "GraphRAG advances retrieval-augmented generation (RAG) by structuring external knowledge as multi-scale knowledge graphs, enabling language models to integrate both broad context and granular details in their generation. While GraphRAG has demonstrated success across domains, its security implications remain largely unexplored.",
      "arxiv_id": "2501.14050",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG",
        "Graph-RAG survey list"
      ],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG",
              "Graph-RAG survey list"
            ]
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "curated",
      "published": "2025-01-23",
      "repositories": [],
      "source": "https://arxiv.org/abs/2501.14050",
      "tasks": [],
      "title": "GraphRAG under Fire",
      "verdict": null
    },
    {
      "abstract": "Graph Retrieval-Augmented Generation (GRAG or Graph RAG) architectures aim to enhance language understanding and generation by leveraging external knowledge. However, effectively capturing and integrating the rich semantic information present in textual and structured data remains a challenge.",
      "arxiv_id": "2412.18644",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG"
      ],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG"
            ]
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-08-13",
      "published": "2024-12-24",
      "repositories": [],
      "source": "https://arxiv.org/abs/2412.18644",
      "tasks": [],
      "title": "DynaGRAG | Exploring the Topology of Information for Advancing Language Understanding and Generation in Graph Retrieval-Augmented Generation",
      "verdict": null
    },
    {
      "abstract": "Large Language Models (LLMs) demonstrate strong reasoning abilities but face limitations such as hallucinations and outdated knowledge. Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) addresses these issues by grounding LLM outputs in structured external knowledge from KGs.",
      "arxiv_id": "2410.20724",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG",
        "Graph-RAG survey list"
      ],
      "fit": {
        "score": 3,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG",
              "Graph-RAG survey list"
            ]
          },
          {
            "code": "mechanismWeak",
            "count": 3
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "curated",
      "published": "2024-10-28",
      "repositories": [],
      "source": "https://arxiv.org/abs/2410.20724",
      "tasks": [],
      "title": "Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation",
      "verdict": null
    },
    {
      "abstract": "Optimizing product listings for generative search requires language agents not only to rewrite effectively but also to continually self-improve by retaining successful strategies across diverse categories. However, existing approaches often treat structured listings as free-form text, leaving effective strategies implicit in prompts, search trajectories, or rewritten outputs rather than accumulating them as a reusable agentic memory.",
      "arxiv_id": "2610.23709",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-19",
      "found_by": "archive",
      "published": "2026-10-16",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.23709",
      "tasks": [],
      "title": "GLOBE: Evolving Structured Agentic Memory for E-commerce Product Optimization in Generative Search",
      "verdict": null
    },
    {
      "abstract": "Evaluating language models and agents requires combining benchmark accuracy, judge-based assessment, runtime performance, tool-use behavior, and retrieval quality. Differences in interfaces, metric semantics, and trace formats complicate these comparisons and can hide failures in agentic workflows.",
      "arxiv_id": "2610.22511",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-19",
      "found_by": "archive",
      "published": "2026-10-15",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.22511",
      "tasks": [],
      "title": "AuditKIT: A Unified Evaluation Spine for Benchmarking, Judging, and Comparing Large Language Models and Agentic Systems",
      "verdict": null
    },
    {
      "abstract": "Retrieval-augmented generation over text-rich knowledge graphs requires identifying evidence that contributes to answering a question, rather than merely matching its textual content. Existing agentic retrievers adaptively balance global search and multi-hop exploration but primarily rely on textual similarity to guide retrieval.",
      "arxiv_id": "2610.22017",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-19",
      "found_by": "archive",
      "published": "2026-10-15",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.22017",
      "tasks": [],
      "title": "CARA: Causally Guided Retrieval Agent for Question Answering",
      "verdict": null
    },
    {
      "abstract": "An agent's memory must remain useful as information changes across conversations. A new fact may replace an earlier value without making that value irrelevant to a later question. We present Temporal State Tracking Memory (TEM), a training-free framework for tracking these changes. TEM stores facts as versioned records linked to their sources.",
      "arxiv_id": "2610.21900",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-19",
      "found_by": "archive",
      "published": "2026-10-15",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.21900",
      "tasks": [],
      "title": "TEM: Temporal State Tracking Memory for Large Language Model Agents",
      "verdict": null
    },
    {
      "abstract": "Agent memory architectures define how LLM agents store and reuse experience across tasks, but they are typically fixed after deployment, limiting their adaptability across task types. Recent self-evolving approaches instead adapt these memory architectures using execution feedback, typically represented as evaluation scores or compressed summaries.",
      "arxiv_id": "2610.21170",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-19",
      "found_by": "archive",
      "published": "2026-10-15",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.21170",
      "tasks": [],
      "title": "MemScope: Evolving Agent Memory Architectures through Localized Code Repair",
      "verdict": null
    },
    {
      "abstract": "Agent memory enables large language model (LLM) agents to reuse past experiences without updating their parameters. However, semantic similarity alone does not guarantee memory usefulness: downstream performance depends on how memories are selected and presented to the agent. We propose MemComposer, a memory composer that jointly examines the current query and retrieved candidates to decide which experiences to include, how many to select, and how to order them.",
      "arxiv_id": "2610.21091",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-19",
      "found_by": "archive",
      "published": "2026-10-15",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.21091",
      "tasks": [],
      "title": "MemComposer: Learning to Assemble Memories for LLM Agents via Reinforcement Learning",
      "verdict": null
    },
    {
      "abstract": "Medication recommendation (MR) is critical for clinical decision support. While existing MR methods are mostly designed for single-center learning, where only one independent hospital is involved, multi-center MR with hospitals of different scales has become increasingly demanding in practice. In multi center settings, abundant data from large medical centers can improve recommendation performance for low-resource centers.",
      "arxiv_id": "2610.20191",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-19",
      "found_by": "archive",
      "published": "2026-10-14",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.20191",
      "tasks": [],
      "title": "HEM-Med: A Hierarchical Agentic Memory Framework for Safe Multi-Center Medication Recommendation",
      "verdict": null
    },
    {
      "abstract": "Iterative retrieval-augmented generation (RAG) accumulates knowledge from retrieved evidence to answer complex questions, but existing methods struggle to reconstruct entity correspondences and inferential dependencies across retrieved passages as new information emerges. We propose RCER, an iterative RAG method that reconstructs these cross-evidence relations directly from accumulated source passages.",
      "arxiv_id": "2610.19398",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-19",
      "found_by": "archive",
      "published": "2026-10-14",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.19398",
      "tasks": [],
      "title": "RCER: Reconstructing Cross-Evidence Relations for Iterative Retrieval-Augmented Generation",
      "verdict": null
    },
    {
      "abstract": "Legal question answering requires answers that are not only plausible, but also grounded in legally valid and citable evidence. Standard retrieval-augmented generation often relies on one-shot semantic matching, which is insufficient when legal evidence must be selected according to authority, applicability, and diversity across heterogeneous sources. We introduce Legal-R1, an agentic retrieval framework for evidence-based legal reasoning.",
      "arxiv_id": "2610.18611",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-19",
      "found_by": "archive",
      "published": "2026-10-13",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.18611",
      "tasks": [],
      "title": "Legal-R1: Agentic Retrieval for Evidence-Based Legal Reasoning",
      "verdict": null
    },
    {
      "abstract": "Medication recommendation requires jointly considering diagnoses, comorbidities, longitudinal patient history, and treatment interactions. Although recent retrieval-augmented generation approaches using language models have shown promise for clinical recommendation, most existing systems rely on fixed retrieval pipelines that cannot adapt to diverse clinical retrieval scenarios and may introduce noisy or irrelevant evidence.",
      "arxiv_id": "2610.16440",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-19",
      "found_by": "archive",
      "published": "2026-10-12",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.16440",
      "tasks": [],
      "title": "ARMED: Adaptive Retrieval-Augmented Medication Recommendation",
      "verdict": null
    },
    {
      "abstract": "With the rapid proliferation of social media, harmful memes have become a growing threat to online ecosystems. Their harmfulness is often implicitly conveyed through complex interactions among visual, textual, and contextual cues, making accurate detection challenging. Recent studies have introduced Retrieval-Augmented Generation (RAG) and external knowledge into harmful meme detection.",
      "arxiv_id": "2610.15463",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-19",
      "found_by": "archive",
      "published": "2026-10-11",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.15463",
      "tasks": [],
      "title": "MemeLogos: Knowledge-Driven Retrieval-Augmented Reasoning for Harmful Meme Detection",
      "verdict": null
    },
    {
      "abstract": "Large language models (LLMs) offer a new approach to wearable-sensor-based human activity recognition. Retrieval-augmented generation (RAG) further introduces similar motion examples and discriminative sensor knowledge, enabling decisions grounded in observations relevant to the current query. More evidence, however, does not necessarily mean better-organized evidence.",
      "arxiv_id": "2610.15298",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-19",
      "found_by": "archive",
      "published": "2026-10-11",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.15298",
      "tasks": [],
      "title": "CHEAR: Confusion-Hierarchy Evidence-Aware Reasoning for Cost-Efficient Human Activity Recognition",
      "verdict": null
    },
    {
      "abstract": "LLMs increasingly support sensor-based human activity recognition (HAR) by linking motion signals with activity semantics and physical knowledge. Retrieval-augmented generation (RAG) grounds decisions in sensor examples and domain features, enabling training-free target-user inference with less dependence on raw numerical arrays. However, existing LLM/RAG-based HAR typically searches single-level activity spaces using static population knowledge.",
      "arxiv_id": "2610.15292",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-19",
      "found_by": "archive",
      "published": "2026-10-11",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.15292",
      "tasks": [],
      "title": "OntoAR-HAR: Ontology-Guided Agentic RAG with Personalized Memory for Human Activity Recognition",
      "verdict": null
    },
    {
      "abstract": "Retrieval-augmented generation (RAG) reuses the same documents across many requests, making non-prefix KV-cache reuse attractive. Yet a cached document state is not reusable text: it is a context-conditioned causal state whose keys and values depend on the prefix, position, and preceding documents under which it was computed.",
      "arxiv_id": "2610.15186",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-19",
      "found_by": "archive",
      "published": "2026-10-10",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.15186",
      "tasks": [],
      "title": "SAFE-KV: Causal Suffix Repair for Behavior-Preserving Non-Prefix KV Cache Reuse in RAG",
      "verdict": null
    },
    {
      "abstract": "Biomedical Named Entity Linking (BioNEL) is the task of mapping mentions extracted from biomedical texts to canonical entities or concepts in the standardized knowledge base. Although existing methods have achieved remarkable performance in mapping accuracy, most rely on training with domain-specific datasets, which fundamentally limits their cross-domain adaptability.",
      "arxiv_id": "2610.14789",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-19",
      "found_by": "archive",
      "published": "2026-10-10",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.14789",
      "tasks": [],
      "title": "Training-Free BioNEL-RAG: Advancing Biomedical Entity Linking through Retrieval-Augmented Generation and Large Language Model",
      "verdict": null
    },
    {
      "abstract": "Chinese historical documents are invaluable cultural treasures, but their complexity poses significant comprehension challenges for non-specialists. Despite the success of Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) in modern document tasks, their capabilities in Long (full-book) Historical Document understanding remain largely unexplored.",
      "arxiv_id": "2610.14531",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-19",
      "found_by": "archive",
      "published": "2026-10-10",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.14531",
      "tasks": [],
      "title": "LongHisDoc: A Comprehensive Benchmark for Chinese Long Historical Document Understanding",
      "verdict": null
    },
    {
      "abstract": "Medication recommendations seek to deliver personalized drug combinations based on patients' clinical information, where ensuring transparent decision-making and personalized safety are of paramount importance. However, existing approaches predominantly operate as ``black boxes'', offering limited interpretability and hindering clinician trust.",
      "arxiv_id": "2610.14474",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-19",
      "found_by": "archive",
      "published": "2026-10-10",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.14474",
      "tasks": [],
      "title": "RES-MR: Risk-Aware Reasoning for Explainable and Safe Medication Recommendation",
      "verdict": null
    },
    {
      "abstract": "Long-term memory enables LLM agents to learn from past interactions but exposes memory formation to adversarial influence. Existing studies primarily organize memory poisoning by attack channels, corruption patterns, or lifecycle stages, leaving formation interfaces insufficiently characterized.",
      "arxiv_id": "2610.14431",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-19",
      "found_by": "archive",
      "published": "2026-10-10",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.14431",
      "tasks": [],
      "title": "MemForm: Benchmarking Memory Formation Poisoning in LLM Agents",
      "verdict": null
    },
    {
      "abstract": "We present SmartSalesBench, the first multi-turn conversational benchmark explicitly designed to evaluate large language model (LLM) sales agents against psychologically grounded, resistant buyer personas. Existing task-oriented dialogue benchmarks assume cooperative users and measure task completion; SmartSalesBench inverts this assumption, placing agents against personas programmed to resist and challenge across conversations of up to twenty turns.",
      "arxiv_id": "2610.12975",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-10-09",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.12975",
      "tasks": [],
      "title": "SmartSalesBench: A Resistance-Aware Multi-Turn Benchmark for Evaluating LLM Sales Agents",
      "verdict": null
    },
    {
      "abstract": "Following retrieved evidence does not guarantee factual correctness: misleading evidence can induce a model to replace an answer it previously gave correctly. Standard accuracy measures obscure this behavior by combining answer replacement with preexisting errors. We introduce RAG-Stress, a controlled diagnostic protocol for examining the limits of evidence reliance in retrieval-augmented generation.",
      "arxiv_id": "2610.11183",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-10-08",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.11183",
      "tasks": [],
      "title": "RAG-Stress: Probing the Limits of Evidence Reliance in Retrieval-Augmented Generation",
      "verdict": null
    },
    {
      "abstract": "Analog/RF circuits remain the critical interface between digital computation and the physical world, and emerging standards from Wi-Fi 7 to 6G place stringent demands on them, yet analog/RF design remains one of the most labor-intensive steps in chip development.",
      "arxiv_id": "2610.10858",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-10-07",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.10858",
      "tasks": [],
      "title": "RFChipAgent: Multi-Agentic AI Flow for Analog/RF Chip Design",
      "verdict": null
    },
    {
      "abstract": "Long-term agents face growing storage demands as they accumulate experience. World models capture reusable regularities that can reduce the information stored for each experience. We formulate the problem of memory allocation conditioned on a world model and introduce MemoWM, a framework that uses shared predictions to compress retained information and reconstruct omitted content.",
      "arxiv_id": "2610.10778",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-10-07",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.10778",
      "tasks": [],
      "title": "MemoWM: How World Models Change What Agents Need to Remember",
      "verdict": null
    },
    {
      "abstract": "Large language models are increasingly applied to tasks grounded in long, heterogeneous information sources. Conventional Retrieval-Augmented Generation (RAG) relies on fixed similarity-based retrieval, while agentic variants adapt queries and tool use but remain largely retrieval-centric. However, in many tasks, the evidence required for a solution is not explicitly present in any single source item.",
      "arxiv_id": "2610.10507",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-10-07",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.10507",
      "tasks": [],
      "title": "RECAST: Learning to Compute the Right Context through Adaptive Evidence Routing",
      "verdict": null
    },
    {
      "abstract": "Retrieval-Augmented Generation (RAG) systems are vulnerable to prompt-injection attacks embedded in retrieved content. We introduce RAG-PIBench, a benchmark for RAG-style prompt-injection detection containing 4,876 contextual examples across frozen train, validation, and protected-test splits. Using a leakage-aware construction pipeline and strict evaluation protocol, we compare keyword-based, semantic-reference, TF-IDF, and transformer-based detectors.",
      "arxiv_id": "2610.08571",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-10-06",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.08571",
      "tasks": [],
      "title": "RAG-PIBench: A Leakage-Aware Benchmark for Prompt-Injection Detection in Trustworthy RAG Systems",
      "verdict": null
    },
    {
      "abstract": "LLM agents often lack the operational knowledge to act reliably in new environments, as they must discover specific tool behaviors or environment conventions on their own. Without memory of past attempts, they repeat the same mistakes across tasks, leading to more task failures and longer trajectories.",
      "arxiv_id": "2610.08048",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-10-06",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.08048",
      "tasks": [],
      "title": "DAEDALUS: Bootstrapping Agent Memory from Self-Generated Tasks",
      "verdict": null
    },
    {
      "abstract": "Dietary nutrition planning plays an important role in chronic disease management and maintaining a healthy body. In applications, it must simultaneously satisfy personalized constraints and reasonable multidimensional nutritional goals. These two aspects often conflict, and user constraints evolve with feedback, resulting in a substantial gap between generic guidelines and executable plans.",
      "arxiv_id": "2610.07886",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-10-06",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.07886",
      "tasks": [],
      "title": "ShanLiangRen: A Nutrition Agent for Personalized Daily Meal Planning",
      "verdict": null
    },
    {
      "abstract": "Memory has become integral to the LLM agent ecosystem, supporting information retention and reuse across interactions. However, most existing agent memory systems construct memory in a query-agnostic manner, which can incur unnecessary preprocessing cost and discard details that later prove essential.",
      "arxiv_id": "2610.06830",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-10-05",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.06830",
      "tasks": [],
      "title": "MemPilot: Orchestrating On-Demand Multimodal Memory Curation for LLM Agents",
      "verdict": null
    },
    {
      "abstract": "Agentic AI is emerging as a promising paradigm for automating complex cybersecurity decisions, yet its use in enforcing zero trust introduces significant challenges in safety, reliability, and policy compliance. This paper presents Agentic AI based zero trust architecture (Agentic-ZTA) that operationalizes the NIST SP 800-207 ZTA architecture control loop through coordinated multi- agent decision pipeline.",
      "arxiv_id": "2610.05782",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-10-05",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.05782",
      "tasks": [],
      "title": "Agentic-ZTA: A Multi-Agent Architecture for Autonomous Zero Trust Enforcement",
      "verdict": null
    },
    {
      "abstract": "Large language model (LLM)-based multi-agent systems increasingly rely on memory to transform execution trajectories into reusable procedural knowledge. Yet repeated retrieval also makes memory errors persistent: memory pollution arises when outdated, weakly supported, or spuriously successful procedures become recurring components of future reasoning. Multi-agent execution introduces an additional structural risk.",
      "arxiv_id": "2610.05176",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-10-04",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.05176",
      "tasks": [],
      "title": "AECG: Asymmetric Experience Consolidation and Governance In Multi-Agent Systems",
      "verdict": null
    },
    {
      "abstract": "Retrieval-Augmented Generation (RAG) is typically evaluated under the implicit assumption that the underlying knowledge base (KB) is clean, leaving the behaviour of RAG systems under realistic KB degradation poorly characterised. We introduce RAGStress, a controlled evaluation benchmark for stress-testing RAG systems under systematic KB corruption.",
      "arxiv_id": "2610.04691",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-10-03",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.04691",
      "tasks": [],
      "title": "RAGStress: A controlled benchmark for evaluating retrieval-augmented generation under knowledge-base degradation",
      "verdict": null
    },
    {
      "abstract": "Detecting responses that retrieval-augmented generation does not ground in its context trades speed against accuracy: surface checks miss paraphrased fabrication, sampling-based methods cost extra generations. Hidden-state probes sit between the two, but every existing one reads the generating model's own activations, so a change of generator invalidates the detector and a closed-weight generator is out of reach. This paper removes that coupling.",
      "arxiv_id": "2610.04642",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-10-03",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.04642",
      "tasks": [],
      "title": "Grounding Probes: Generator-Independent Hallucination Detection from Observer Model Hidden States",
      "verdict": null
    },
    {
      "abstract": "Is agentic memory robust against stealthy steganographic attacks? We carry out a large-scale red-teaming exercise to test whether agents can encode attacker-controlled strings in one session and recover them in another without triggering safety oversight. Following SHADE-Arena-style tasks, we embed malicious side tasks to encode secret strings using steganography within otherwise benign tasks and evaluate them using independent task-completion and safety oversight.",
      "arxiv_id": "2610.04589",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-10-03",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.04589",
      "tasks": [],
      "title": "StegoMemory: Agentic Memory Acts as Covert Steganographic Channel",
      "verdict": null
    },
    {
      "abstract": "Long-term agents must remember not only what is true about a user, but also how a particular agent should work with that user as their shared history evolves. Existing benchmarks primarily supervise user facts and preferences or experience reusable across users, leaving this relationship-specific agent memory implicit.",
      "arxiv_id": "2610.03020",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-10-02",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.03020",
      "tasks": [],
      "title": "DyadMem: A Long-Term Memory Benchmark of How Agents Work with Users",
      "verdict": null
    },
    {
      "abstract": "LLM-based personal agents are emerging as persistent carriers of user semantics and intermediaries between users and recommendation platforms, maintaining richer user knowledge locally. As agents interact with one another, the conventional \\textit{User--Platform} relation evolves into a \\textit{User--Agent Web--Platform} information pathway, enabling distributed user-side information to complement item-side information.",
      "arxiv_id": "2610.01705",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-10-01",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.01705",
      "tasks": [],
      "title": "AgentWebRec: Compact Evidence Fusion over the Agent Web for Personalized Recommendation",
      "verdict": null
    },
    {
      "abstract": "Quantized large language models are increasingly deployed on edge devices for their low latency and energy efficiency. However, model quantization weakens alignment safeguards, leaving qLLMs (quantized large language models) highly vulnerable to jailbreak attacks. To address this challenge, we present MOMAT (Mixture of Multiple Atlases), a hardware-enhanced safety framework that combines structured knowledge retrieval with low-power defense acceleration.",
      "arxiv_id": "2610.01058",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-10-01",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.01058",
      "tasks": [],
      "title": "MOMAT: Mixture of Multiple Atlases for Low-Power Jailbreak Defense of Quantized LLMs",
      "verdict": null
    },
    {
      "abstract": "Recent advances in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) have significantly democratized access to legal information. Nevertheless, most existing legal assistants remain confined to multi-turn conversational QA, failing to support complex legal tasks that require systematic evidence retrieval, multi-step reasoning, and report-level synthesis.",
      "arxiv_id": "2610.01027",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-10-01",
      "repositories": [],
      "source": "https://arxiv.org/abs/2610.01027",
      "tasks": [],
      "title": "LawCompass: Navigating from Legal QA to Multi-Agent Deep Research with Grounded Evidence",
      "verdict": null
    },
    {
      "abstract": "Agent memory faces heterogeneous access needs: a single-hop question may require one piece of evidence, whereas a multi-hop question must combine evidence from multiple sources. Predefined memory workflows cannot adapt to these varying needs. Recent adaptive methods search or learn over memory components and their compositions, but the design space itself remains predefined.",
      "arxiv_id": "2609.39765",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-09-30",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.39765",
      "tasks": [],
      "title": "MemCodex: Self-Programming Hierarchical Memory for Language Agents",
      "verdict": null
    },
    {
      "abstract": "Retrieval-augmented generation (RAG) systems need inexpensive ways to route generated answers: accept low-risk outputs, review uncertain ones, and reserve strong verifiers for the expensive tail. We present RAGScope, a leakage-controlled protocol for evaluating local evidence gates that use only the task input, retrieved context, and answer text.",
      "arxiv_id": "2609.39075",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-09-30",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.39075",
      "tasks": [],
      "title": "RAGScope: A Leakage-Controlled, Cost-Aware Evidence-Gating Protocol for RAG Hallucination Triage",
      "verdict": null
    },
    {
      "abstract": "Knowledge-intensive language-model systems typically represent external knowledge as text chunks or static graphs, with limited support for concept evolution, point-in-time reasoning, and distinctions between validated and inferred knowledge.",
      "arxiv_id": "2609.38684",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-10-12",
      "found_by": "archive",
      "published": "2026-09-30",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.38684",
      "tasks": [],
      "title": "Concept-Grounded Attention: A Controlled Evaluation of Graph-Injected Attention, Temporal Versioning, and Epistemic Status",
      "verdict": null
    },
    {
      "abstract": "Automated and highly usable Quality-of-Service (QoS) enforcement requires translating high-level service intents into deployable traffic-management policies. Although intent-based networking (IBN) has simplified policy specification, bridging the gap between business-level intents and executable network configurations remains complex, error-prone, and difficult to automate.",
      "arxiv_id": "2609.31397",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-28",
      "found_by": "archive",
      "published": "2026-09-25",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.31397",
      "tasks": [],
      "title": "Intent2Tc: Automated Intent-to-Traffic Control Translation with Language Models",
      "verdict": null
    },
    {
      "abstract": "Retrieval-augmented generation (RAG) is often used to address outdated knowledge by providing external evidence. But retrieval helps only when that evidence is still valid. We identify a temporal alignment failure, stale-document poisoning, in which outdated evidence makes a model wrong despite answering correctly without retrieval.",
      "arxiv_id": "2609.31342",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-28",
      "found_by": "archive",
      "published": "2026-09-25",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.31342",
      "tasks": [],
      "title": "Stale-Document Poisoning: When Outdated Retrieval Overrides Correct Model Answers",
      "verdict": null
    },
    {
      "abstract": "General-purpose agent memory summarizes conversations: it extracts salient snippets, embeds them, and retrieves the top-k into the prompt. A health agent cannot run on summaries: a dose becomes a sentence, \"since last week\" is resolved at the model's discretion, and a three-month glucose trend cannot be answered by text similarity. We present PIA, a personal intelligence agent deployed alongside a consumer health agent.",
      "arxiv_id": "2609.31255",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-28",
      "found_by": "archive",
      "published": "2026-09-25",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.31255",
      "tasks": [],
      "title": "PIA: A Personal Intelligence Agent Turning Health Conversations into Records and Records into Understanding",
      "verdict": null
    },
    {
      "abstract": "Long-form technical text generation underpins knowledge-intensive workflows, yet remains challenging for large language models (LLMs) due to the need for globally consistent logical structuring and faithful technical reasoning beyond local coherence. Patent drafting is a canonical instance of this challenge, demanding holistic generation of a legally compliant and technically exhaustive document through sustained multi-expert collaboration.",
      "arxiv_id": "2609.30943",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-28",
      "found_by": "archive",
      "published": "2026-09-25",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.30943",
      "tasks": [],
      "title": "LogicTree-RAG: Logic Tree-guided Retrieval-Augmented Generation for Long-form Patent Drafting",
      "verdict": null
    },
    {
      "abstract": "Agentic memory systems reuse past experience to improve future performance, yet most existing designs curate memory at write time: once a task is completed, its trajectory is distilled into a fixed artifact, such as a reflection, workflow, skill, or reasoning strategy, that is later retrieved by similarity.",
      "arxiv_id": "2609.27334",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-28",
      "found_by": "archive",
      "published": "2026-09-23",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.27334",
      "tasks": [],
      "title": "Just-in-Time Memory: Learning to Curate Task-Adaptive Memory for LLM Agents",
      "verdict": null
    },
    {
      "abstract": "Scientific investigations into microbial natural products (NPs) present significant challenges for novices, largely due to the complexity of microbial systems, biochemical diversity, technical skill requirements, and the demands of bioinformatics and data analysis processes. To address these issues, we introduce ChatT2, a large language model (LLM)-based agent that is specifically tailored to the unique characteristics of bacterial type II polyketides.",
      "arxiv_id": "2609.25620",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-28",
      "found_by": "archive",
      "published": "2026-09-22",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.25620",
      "tasks": [],
      "title": "ChatT2: An Adaptive Framework for Developing a Large Language Model-Based Agent for Natural Product Domain Research",
      "verdict": null
    },
    {
      "abstract": "Agent memory enables enterprise agents to retain knowledge acquired during work and reuse it across tasks and agents, turning execution experience into persistent organizational knowledge. Realizing this potential requires both source--memory integration, through which enterprise sources and accumulated memory can be utilized together, and memory governance, through which shared memory remains subject to organizational policies throughout its lifecycle.",
      "arxiv_id": "2609.25563",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-28",
      "found_by": "archive",
      "published": "2026-09-22",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.25563",
      "tasks": [],
      "title": "AkasicMEM: Governed Enterprise Memory for Agents",
      "verdict": null
    },
    {
      "abstract": "Agents today often take real-world actions that depend on long-term memory and context recall over time. However, most current memory benchmarks are built for a conversational question-answer format, where the question itself signals that some fact must be retrieved, and often which one. Moreover, benchmarks rarely require anything beyond accuracy from submissions, allowing memory systems to make unreasonable cost/time tradeoffs to achieve higher scores.",
      "arxiv_id": "2609.24971",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-28",
      "found_by": "archive",
      "published": "2026-09-21",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.24971",
      "tasks": [],
      "title": "DolphinBench: Mapping the Pareto Frontier of Agent Memory",
      "verdict": null
    },
    {
      "abstract": "Graph Retrieval-Augmented Generation (GraphRAG) has remarkably enhanced large language models on complex reasoning by leveraging structured entity topologies. However, existing frameworks heavily rely on standard autoregressive language models where the nature of inherent sequential generation severely hinders overall inference efficiency.",
      "arxiv_id": "2609.24346",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-28",
      "found_by": "archive",
      "published": "2026-09-21",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.24346",
      "tasks": [],
      "title": "LADDER: Graph-Guided Diffusion Language Models for Efficient Multi-Hop Reasoning",
      "verdict": null
    },
    {
      "abstract": "The effectiveness of agent memory ultimately depends on whether the underlying LLM gives each memory in context an appropriate degree of influence over its response. Yet this capability has remained largely overlooked. To assess this capability, we introduce MemCalib, a benchmark grounded in realistic memory-system scenarios for evaluating memory use and advancing optimization algorithms.",
      "arxiv_id": "2609.24259",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-28",
      "found_by": "archive",
      "published": "2026-09-21",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.24259",
      "tasks": [],
      "title": "MemCalib: Benchmarking and Optimizing Memory Use in LLM Agents",
      "verdict": null
    },
    {
      "abstract": "Large language models (LLMs) have shown remarkable progress in natural language understanding, yet their effectiveness in specialized fields like astronomy and astrodynamics remains limited due to challenges in multi-step reasoning, symbolic manipulation, and domain-specific terminology.",
      "arxiv_id": "2609.24246",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-28",
      "found_by": "archive",
      "published": "2026-09-21",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.24246",
      "tasks": [],
      "title": "Taramandal-GPT: Enhancing Astrodynamics Problem-Solving with Knowledge Retrieval and Structured Thinking",
      "verdict": null
    },
    {
      "abstract": "Agentic memory is becoming essential for long-horizon AI agents, yet many existing systems rely on autoregressive LLMs to control how memories are organized, retrieved, and used, placing expensive generation on the critical path of memory operations. We introduce \\textbf{\\method}, a new agentic memory architecture inspired by System-One/System-Two cognition. System One captures fast, lightweight decision-making, whereas System Two performs slower, deliberative reasoning.",
      "arxiv_id": "2609.23986",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-28",
      "found_by": "archive",
      "published": "2026-09-21",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.23986",
      "tasks": [],
      "title": "Jev-Mem: System-One-Controlled Agentic Memory for Efficient AI Agents",
      "verdict": null
    },
    {
      "abstract": "Temporal Information Retrieval (TIR) has been increasingly critical given the rise of Retrieval-Augmented Generation (RAG). Since temporally mismatched evidence can be highly misleading, TIR aims to retrieve documents that are both semantically and temporally relevant to a query. Two TIR paradigms have emerged - temporal retrievers and temporal re-rankers - differing in how temporal relevance is modeled.",
      "arxiv_id": "2609.23880",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-28",
      "found_by": "archive",
      "published": "2026-09-20",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.23880",
      "tasks": [],
      "title": "Q-TIE: A Lightweight and Generalizable Re-ranking Framework for Temporal Information Retrieval",
      "verdict": null
    },
    {
      "abstract": "Similarity-based retrieval is the dominant rule for conditioning large language models (LLMs) in in-context learning, retrieval-augmented generation, and prompt-based time series forecasting. The rule concentrates on near-duplicate candidates, an issue that has motivated diversity-aware retrieval but remains unexamined in other retrieval-conditioned pipelines.",
      "arxiv_id": "2609.22977",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-28",
      "found_by": "archive",
      "published": "2026-09-19",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.22977",
      "tasks": [],
      "title": "Beyond Similarity: Coverage-Aware Prompt Selection for Time Series Forecasting with LLMs",
      "verdict": null
    },
    {
      "abstract": "Enterprise AI assistants must produce responses that are verifiable and traceable to source evidence. However, retrieval augmented generation (RAG) over heterogeneous enterprise data can suffer from citation drift, unsupported content, and weak source traceability. We present EvidenT (T = Trust + Transparency + Traceability), a lightweight pipeline that verifies extracted evidence against retrieved documents before answer generation, without model retraining.",
      "arxiv_id": "2609.22537",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-28",
      "found_by": "archive",
      "published": "2026-09-18",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.22537",
      "tasks": [],
      "title": "EvidenT: Building Trustworthy Enterprise Assistants through Evidence Groundedness and Traceability",
      "verdict": null
    },
    {
      "abstract": "Empirical evaluation is central to recommender-systems (RecSys) research, but turning experimental designs into executable code remains a manual and error-prone task. We present AutoRecLab, a Python-based autonomous RecSys lab that automates RecSys experiments from natural-language prompts. Given a research idea, AutoRecLab derives explicit experiment requirements, builds and validates a prototype, and iteratively expands it into the requested full experiment.",
      "arxiv_id": "2609.21863",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-21",
      "found_by": "archive",
      "published": "2026-09-18",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.21863",
      "tasks": [],
      "title": "AutoRecLab: Describe the Experiment, Get the Code!",
      "verdict": null
    },
    {
      "abstract": "Scientific research increasingly relies on large, heterogeneous data sources, motivating interest in retrieval-augmented generation (RAG) systems that provide natural language access to scientific knowledge and research workflows. Researchers are exploring the viability of these systems as natural language interfaces for document search and for generating analysis code and pipeline components.",
      "arxiv_id": "2609.16519",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-21",
      "found_by": "archive",
      "published": "2026-09-15",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.16519",
      "tasks": [],
      "title": "AquiLLM: Evaluating Faithfulness in Open-Weight RAG-LLM Systems for Scientific Research",
      "verdict": null
    },
    {
      "abstract": "Medical knowledge evolves continuously, whereas the parametric knowledge encoded in large language models (LLMs) is fixed at training time. External retrieval, including retrieval-augmented generation (RAG), can provide access to newly available evidence, but retrieved information may be irrelevant, incomplete, or conflicting. As a result, external retrieval can in turn degrade the factual accuracy and evidence grounding of LLM outputs.",
      "arxiv_id": "2609.16301",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-21",
      "found_by": "archive",
      "published": "2026-09-14",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.16301",
      "tasks": [],
      "title": "CLEAR: Cross-Source Evidence Adjudication for Large Language Models in Medicine",
      "verdict": null
    },
    {
      "abstract": "Retrieval-augmented generation (RAG) can improve access to complex information; however, retrieving evidence alone does not ensure that answers are grounded, citation-valid, or appropriately refused. This paper introduces CiteGuard-RAG, a validation-centered AI system for evidence-grounded question answering. The system integrates hybrid semantic-lexical retrieval, citation-constrained generation, sentence-level grounding validation, and single-pass regeneration.",
      "arxiv_id": "2609.15830",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-21",
      "found_by": "archive",
      "published": "2026-09-14",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.15830",
      "tasks": [],
      "title": "CiteGuard-RAG: A Validation-Centered AI System for Evidence-Grounded Question Answering",
      "verdict": null
    },
    {
      "abstract": "Retrieval-augmented generation (RAG) grounds a language model's answers on retrieved external knowledge and returns each answer with citations that identify its sources. Those citations are the user's audit trail: they let a reader verify a claim without trusting the model. Prior security work on RAG asks whether an attacker can corrupt the answer, leaving the citation channel unexplored. We show that this channel is a new and practical attack surface.",
      "arxiv_id": "2609.15660",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-21",
      "found_by": "archive",
      "published": "2026-09-14",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.15660",
      "tasks": [],
      "title": "CiteShade: Citation Laundering in Multi-Source Retrieval-Augmented Generation and Its Counterfactual Defense",
      "verdict": null
    },
    {
      "abstract": "Recently, Large Language Models (LLMs) have gained significant attention due to their strong language understanding and generation capabilities, demonstrating impressive reasoning abilities as well as effective utilization of external knowledge. Many studies have proposed methods that specialize in improving performance for individual tasks. However, ironically, only a limited number of attempts have explored general-purpose, task-agnostic methods.",
      "arxiv_id": "2609.15234",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-21",
      "found_by": "archive",
      "published": "2026-09-14",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.15234",
      "tasks": [],
      "title": "CWM: Controllable White-Box Meta-Prompting for Adaptive Retrieval-Augmented Generation and Reasoning Ability",
      "verdict": null
    },
    {
      "abstract": "Cloud-hosted large language models (LLMs) are increasingly used for root cause analysis (RCA) in AIOps pipelines, but they introduce data privacy risk, network latency, and per-query cost that scale poorly with production log volumes.",
      "arxiv_id": "2609.14762",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-21",
      "found_by": "archive",
      "published": "2026-09-13",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.14762",
      "tasks": [],
      "title": "TriCalRAG: A Three-Strategy, Retrieval-Augmented Benchmark for On-Premise LLM-Based Root Cause Analysis in AIOps",
      "verdict": null
    },
    {
      "abstract": "Teaching abstract theoretical computer science (TCS) concepts such as algorithm analysis and complexity theory is challenging because students must handle formal proofs and asymptotic reasoning that conventional resources rarely explain in an adaptive, on-demand way. We present AlgoRAG, a specialized Retrieval-Augmented Generation (RAG) system that couples a large language model (LLM) with a curated, domain-specific knowledge base to address these challenges.",
      "arxiv_id": "2609.14572",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-21",
      "found_by": "archive",
      "published": "2026-09-13",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.14572",
      "tasks": [],
      "title": "AlgoRAG: Retrieval-Augmented Generation for Theoretical Computer Science Education -- A Comprehensive Evaluation Framework for Algorithm Analysis and Complexity Theory",
      "verdict": null
    },
    {
      "abstract": "Long-form literary narratives pose a distinctive information-processing challenge for retrieval-augmented generation: relevant evidence is distributed across chapters, relations evolve over narrative time, and correct answers may depend jointly on temporal, spatial, and relational constraints.",
      "arxiv_id": "2609.05139",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-09-04",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.05139",
      "tasks": [],
      "title": "NS-ST-GraphRAG: Neuro-Symbolic Spatio-Temporal GraphRAG for Literary Knowledge Processing",
      "verdict": null
    },
    {
      "abstract": "We introduce PetQA, a Korean long-form question-answering (QA) benchmark for evaluating veterinary knowledge and clinical reasoning in large language models (LLMs) and large vision-language models (LVLMs). PetQA contains 10,076 text-only and 8,751 multimodal QA pairs derived from real-world questions about dogs and cats, paired with answers from expert veterinarians. Its test split, PetQA-Bench, further includes annotations for question types and clinical conditions.",
      "arxiv_id": "2609.04598",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-09-04",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.04598",
      "tasks": [],
      "title": "PetQA: Benchmarking Veterinary Knowledge and Clinical Reasoning",
      "verdict": null
    },
    {
      "abstract": "Latent entity extraction (LEE) tackles the challenge of identifying implicit, contextually inferred entities within free text-an area where traditional entity extraction methods fall short. In this paper, we introduce LentEx, a novel framework for latent entity extraction that leverages synthetic data generation and instruction fine-tuning to optimize smaller, efficient large language models (LLMs).",
      "arxiv_id": "2609.04511",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-09-03",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.04511",
      "tasks": [],
      "title": "LentEx: Generalizable Latent Entity Extraction via Synthetic Data and Instruction-Tuned LLMs",
      "verdict": null
    },
    {
      "abstract": "Large language models deployed in high-stakes settings frequently generate plausible but ungrounded claims. Standard retrieval-augmented generation (RAG) pipelines offer limited remedy, since they retrieve isolated passages without tracking cross-document evidence relationships or quantifying uncertainty.",
      "arxiv_id": "2609.04442",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-09-03",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.04442",
      "tasks": [],
      "title": "GRACE: Graph-Grounded Reflective Agent Copilot Engine for Expert-in-the-Loop Knowledge Expansion",
      "verdict": null
    },
    {
      "abstract": "Early-onset colorectal cancer is increasing among younger adults, yet red-flag symptoms in this age group have no evidence-based guidelines for follow-up testing, and structured encounter data do not capture the detail needed to support early detection and inform follow-up, including symptom duration, context, and fam- ily history, an established colorectal-cancer risk factor.",
      "arxiv_id": "2609.04366",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-09-03",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.04366",
      "tasks": [],
      "title": "VERGE: Verification-Enhanced Refinement for Grounded Extraction of Early-Onset Colorectal Cancer Symptoms in Clinical Notes",
      "verdict": null
    },
    {
      "abstract": "Retrieval-augmented generation (RAG) complements parametric models with retrieved external evidence. The same idea is attractive for continuous-output regression, but directly reusing retrieved target values is often not robust when samples differ in output level, numerical scale, or local dynamics.",
      "arxiv_id": "2609.03937",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-09-03",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.03937",
      "tasks": [],
      "title": "RATL: Learning from Retrieved Residuals for Robust Multivariate Time-Series Forecasting",
      "verdict": null
    },
    {
      "abstract": "Third-party retrieval-augmented generation (RAG) marketplaces create a new auditing problem: data providers may license corpora to a RAG operator, yet later have no visibility into whether their documents are being reused without compensation. Auditing this misuse is difficult because the operator is non-cooperative, answers are paraphrased by the generator, and one response may combine evidence from many providers.",
      "arxiv_id": "2609.03749",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-09-03",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.03749",
      "tasks": [],
      "title": "Rent-a-RAG: Embedding-Space Watermarks for Auditing Third-Party RAG",
      "verdict": null
    },
    {
      "abstract": "Identifying the issues disputed between litigating parties is a crucial component of real-world litigation. However, legal issues remain comparatively underexplored in legal AI research. In this work, we study the computational modelling of legal issue identification in litigation.",
      "arxiv_id": "2609.02954",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-09-02",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.02954",
      "tasks": [],
      "title": "LexIssue: Benchmarking Legal Issue Identification in Chinese Civil Litigation",
      "verdict": null
    },
    {
      "abstract": "Document Visual Question Answering (DocVQA) often leverages Retrieval-Augmented Generation (RAG), where late-interaction encoders are commonly used to identify document pages relevant to a user query, before answer generation by a Large Vision-Language Model (LVLM). Existing approaches typically retrieve a fixed top-$k$ number of pages regardless of query complexity, which increases LVLM latency and may degrade answer accuracy.",
      "arxiv_id": "2609.02486",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-09-02",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.02486",
      "tasks": [],
      "title": "ViSAR: Training-Free Adaptive-$k$ Retrieval for Visual Document Question Answering",
      "verdict": null
    },
    {
      "abstract": "Named Entity Recognition (NER) has achieved substantial progress since the advent of large language models (LLMs). Nevertheless, the recognition of long-tail and domain-specific entities remains challenging due to the deficiency in parametric knowledge. Retrieval-augmented generation (RAG) offers a promising remedy by injecting external knowledge, but it also introduces noise and unnecessary cost when dealing with familiar cases.",
      "arxiv_id": "2609.02366",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-09-02",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.02366",
      "tasks": [],
      "title": "NE-R1: Enhancing Named Entity Recognition Model via Reinforcement Learning",
      "verdict": null
    },
    {
      "abstract": "The browser has become a first-class database host: applications increasingly want to store, query, and reason over structured data entirely on the client - for privacy, offline operation, local-first collaboration, and, most recently, as durable memory for in-browser AI agents. One way to get SQL in the browser, compiling PostgreSQL to WebAssembly (PGlite), inherits PostgreSQL's process model: a single backend connection that executes one statement at a time and blocks.",
      "arxiv_id": "2609.01818",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-09-01",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.01818",
      "tasks": [],
      "title": "Zeta-Lite: A Concurrent, Branchable In-Browser SQL Database for Agentic Memory",
      "verdict": null
    },
    {
      "abstract": "MutMem V1 introduced retention-preserving, cryptographically authorized mutation for persistent agent memory but did not provide a complete portable verification contract or clean-install reproduction path. MutMem V2 closes that publication gap without introducing a second memory engine.",
      "arxiv_id": "2609.01235",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-09-01",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.01235",
      "tasks": [],
      "title": "MutMem-V2: Cryptographically Authorized Mutation in Persistent Agent Memory Portable Verification and Reproducible Evidence",
      "verdict": null
    },
    {
      "abstract": "Clinical LLM assistants must reason over multi-visit patient trajectories, yet whether the compact history representations used to scale them---retrieval, structured timelines, LLM summaries, agentic memory---preserve the longitudinal signal clinical reasoning needs has not been measured.",
      "arxiv_id": "2609.01111",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-09-01",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.01111",
      "tasks": [],
      "title": "ClinTraceBench: Source-Verifiable Longitudinal Clinical Reasoning over EHR-Derived Dialogues",
      "verdict": null
    },
    {
      "abstract": "Retrieval-Augmented Generation (RAG) is highly sensitive to retrieval noise: when retrieved documents mix informative and irrelevant context, LLMs are easily distracted, leading to hallucinations. To overcome this, we propose SCoNE (Selective Context-aware Neuron Editing), a training-free model editing approach that improves retrieval noise robustness by selectively strengthening context-aware FFN neurons that are identified by both high attribution and high cross-input varia…",
      "arxiv_id": "2609.00689",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-09-01",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.00689",
      "tasks": [],
      "title": "SCoNE: Selective Context-aware Neuron Editing for Robust Retrieval-Augmented Generation",
      "verdict": null
    },
    {
      "abstract": "Large Vision-Language Models (LVLMs) achieve strong performance on image-grounded text generation and visual question answering. However, it remains difficult for them to comprehensively and accurately describe the factual relations among the entities and concepts associated with the objects depicted in an image.",
      "arxiv_id": "2609.00629",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-09-01",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.00629",
      "tasks": [],
      "title": "ExpArt-KG: Artwork Image Description Generation through Iterative Exploration of Knowledge Graphs",
      "verdict": null
    },
    {
      "abstract": "The growing complexity of cyber-physical attack surfaces in advanced manufacturing has made cyber threat intelligence analysis increasingly difficult. Although large language models and retrieval-augmented generation have improved CTI workflows, text-based approaches remain vulnerable to hallucinations and provide limited support for structured reasoning over interconnected threats.",
      "arxiv_id": "2609.00604",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-09-01",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.00604",
      "tasks": [],
      "title": "NeuroGraph: An AI Graph-Driven Neuro-Symbolic Framework for Explainable Threat Reasoning in Advanced Manufacturing",
      "verdict": null
    },
    {
      "abstract": "Retrieval-Augmented Generation enhances Large Language Models by grounding responses in external knowledge, but multi-hop reasoning remains vulnerable to error propagation, where early retrieval failures confound subsequent steps. Standard outcome-based optimization only rewards the final answer, leaving intermediate retrieval and reasoning errors undetected.",
      "arxiv_id": "2609.01658",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-08-31",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.01658",
      "tasks": [],
      "title": "PRO-Step: Step-level Process Reward Optimization for Retrieval-Augmented Generation",
      "verdict": null
    },
    {
      "abstract": "The exponential growth of wireless devices is driving unprecedented spectrum demand, pushing spectrum management toward more fine-grained decisions across space, time, and device constraints. As a result, spectrum policymakers and engineers must process large volumes of data that come from diverse sources and take many different forms, such as text and tables.",
      "arxiv_id": "2609.00427",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-08-31",
      "repositories": [],
      "source": "https://arxiv.org/abs/2609.00427",
      "tasks": [],
      "title": "SpecMind: Enabling Spectrum Intelligence via Multi-Agent Hybrid Retrieval-Augmented Generation",
      "verdict": null
    },
    {
      "abstract": "Missing institutional context during meetings can impede effective participation. Retrieving relevant information, often scattered across heterogeneous internal and external sources, requires costly task-switching that disrupts both individual focus and collective conversational flow, particularly detrimental during cognitively demanding tasks such as decision-making.",
      "arxiv_id": "2608.31115",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-08-31",
      "repositories": [],
      "source": "https://arxiv.org/abs/2608.31115",
      "tasks": [],
      "title": "InsightToast: Proactive Information Retrieval & Glanceable Visualization in the Side Channel of Data-Rich Meetings",
      "verdict": null
    },
    {
      "abstract": "Multimodal Large Language Models (MLLMs) have shown remarkable success in STEM domains, where progress is often driven by vertical, step-by-step deduction under relatively stable symbol systems. Their horizontal, interdisciplinary cultural reasoning, however, remains underexplored.We propose CM2, a multi-agent framework grounded in the cognitive pathway of human cultural interpretation.",
      "arxiv_id": "2608.30498",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-08-31",
      "repositories": [],
      "source": "https://arxiv.org/abs/2608.30498",
      "tasks": [],
      "title": "CM2: Multimodal Cultural Reasoning via an Integrated Multi-Agent Framework",
      "verdict": null
    },
    {
      "abstract": "Real-world knowledge resides in multimodal documents, necessitating retrieval-augmented generation (RAG) for accurate question answering. However, existing multimodal RAG models are primarily designed for single-image or closed-document settings and exhibit limited accuracy in realistic multi-image scenarios. Moreover, processing numerous retrieved images incurs substantial computational overhead from irrelevant visual tokens.",
      "arxiv_id": "2608.30163",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-08-31",
      "repositories": [],
      "source": "https://arxiv.org/abs/2608.30163",
      "tasks": [],
      "title": "Doc-REFRAG: Rethinking Multimodal Document Retrieval-Augmented Generation",
      "verdict": null
    },
    {
      "abstract": "Multi-hop question answering in retrieval-augmented gener?ation (RAG) often benefits from retrieving beyond the few candidates that will finally be read: narrow retrieval can miss an indispensable hop, while expanded retrieval introduces topical distractors. This challenge is not tied to a particu?lar knowledge-base format. Candidate pools may come from standalone retrievers, standard RAG backends, or graph-based retrieval pipelines.",
      "arxiv_id": "2608.29753",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-08-30",
      "repositories": [],
      "source": "https://arxiv.org/abs/2608.29753",
      "tasks": [],
      "title": "PAGE-RAG: Provenance-Aware Graph Evidence Promotion for Fixed-Budget Multi-hop Retrieval-Augmented Generation",
      "verdict": null
    },
    {
      "abstract": "Retrieval-Augmented Generation (RAG) improves the factuality of large language models (LLMs), yet existing RAG systems often struggle with complex, multi-step reasoning that requires adaptive retrieval and continuous revision of intermediate contexts.",
      "arxiv_id": "2608.29622",
      "citations": null,
      "curated_by": [],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "archive",
      "published": "2026-08-30",
      "repositories": [],
      "source": "https://arxiv.org/abs/2608.29622",
      "tasks": [],
      "title": "AgenticRag-R1: Agentic Reinforcement Learning with Stack Memory for Multi-Step Reasoning, Retrieval and Memorizing",
      "verdict": null
    },
    {
      "abstract": "Adapting Large Language Models (LLMs) to specialized domains often incurs an alignment tax, as fine-tuning on domain-specific tasks can cause catastrophic forgetting and substantially degrade performance on general tasks. We propose MemSFT, which mitigates the alignment tax by decoupling domain specialization from backbone parameter updates through a plug-and-play parametric memory.",
      "arxiv_id": "2607.25614",
      "citations": 0,
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "named"
          }
        ]
      },
      "found_at": "2026-08-12",
      "published": "2026-07-28",
      "repositories": [],
      "source": "https://paperswithcode.co/api/v1/papers/2607.25614",
      "tasks": [],
      "title": "MemSFT: Mitigating Alignment Tax with an External Parametric Memory",
      "verdict": null
    },
    {
      "abstract": "In recent years, large language models (LLMs) have revolutionized the field of natural language processing. However, they often suffer from knowledge gaps and hallucinations. Graph retrieval-augmented generation (GraphRAG) enhances LLM reasoning by integrating structured knowledge from external graphs.",
      "arxiv_id": "2503.13804",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG"
      ],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG"
            ]
          }
        ]
      },
      "found_at": "2026-08-13",
      "published": "2025-03-18",
      "repositories": [],
      "source": "https://arxiv.org/abs/2503.13804",
      "tasks": [],
      "title": "Empowering GraphRAG with Knowledge Filtering and Integration",
      "verdict": null
    },
    {
      "abstract": "Graph-based Retrieval-Augmented Generation (RAG) methods have significantly enhanced the performance of large language models (LLMs) in domain-specific tasks. However, existing RAG methods do not adequately utilize the naturally inherent hierarchical knowledge in human cognition, which limits the capabilities of RAG systems.",
      "arxiv_id": "2503.10150",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG"
      ],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG"
            ]
          }
        ]
      },
      "found_at": "2026-08-13",
      "published": "2025-03-13",
      "repositories": [],
      "source": "https://arxiv.org/abs/2503.10150",
      "tasks": [],
      "title": "Retrieval-Augmented Generation with Hierarchical Knowledge",
      "verdict": null
    },
    {
      "abstract": "Despite significant advancements in Large Language Models (LLMs), developing advanced reasoning capabilities in LLMs remains a key challenge. Process Reward Models (PRMs) have demonstrated exceptional promise in enhancing reasoning by providing step-wise feedback, particularly in the context of mathematical reasoning.",
      "arxiv_id": "2503.00845",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG",
        "Graph-RAG survey list"
      ],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG",
              "Graph-RAG survey list"
            ]
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "curated",
      "published": "2025-03-02",
      "repositories": [],
      "source": "https://arxiv.org/abs/2503.00845",
      "tasks": [],
      "title": "Rewarding Graph Reasoning Process makes LLMs more Generalized Reasoners",
      "verdict": null
    },
    {
      "abstract": "Large language models (LLMs) have demonstrated remarkable capabilities in a wide range of tasks, yet their application to specialized domains remains challenging due to the need for deep expertise. Retrieval-Augmented generation (RAG) has emerged as a promising solution to customize LLMs for professional fields by seamlessly integrating external knowledge bases, enabling real-time access to domain-specific expertise during inference.",
      "arxiv_id": "2501.13958",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG",
        "Graph-RAG survey list"
      ],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG",
              "Graph-RAG survey list"
            ]
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "curated",
      "published": "2025-01-21",
      "repositories": [],
      "source": "https://arxiv.org/abs/2501.13958",
      "tasks": [],
      "title": "A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models",
      "verdict": null
    },
    {
      "abstract": "Recommender systems have become increasingly vital in our daily lives, helping to alleviate the problem of information overload across various user-oriented online services. The emergence of Large Language Models (LLMs) has yielded remarkable achievements, demonstrating their potential for the development of next-generation recommender systems.",
      "arxiv_id": "2501.02226",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG",
        "Graph-RAG survey list"
      ],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG",
              "Graph-RAG survey list"
            ]
          }
        ]
      },
      "found_at": "2026-09-07",
      "found_by": "curated",
      "published": "2025-01-04",
      "repositories": [],
      "source": "https://arxiv.org/abs/2501.02226",
      "tasks": [],
      "title": "Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation",
      "verdict": null
    },
    {
      "abstract": "Large Language Models (LLMs) have shown potential in reasoning over structured environments, e.g., knowledge graph and table. Such tasks typically require multi-hop reasoning, i.e., match natural language utterance with instances in the environment. Previous methods leverage LLMs to incrementally build a reasoning path, where the LLMs either invoke tools or pick up schemas by step-by-step interacting with the environment.",
      "arxiv_id": "2403.08593",
      "citations": null,
      "curated_by": [
        "Awesome-GraphRAG"
      ],
      "fit": {
        "score": 2,
        "signals": [
          {
            "code": "curatedList",
            "lists": [
              "Awesome-GraphRAG"
            ]
          }
        ]
      },
      "found_at": "2026-08-13",
      "published": "2024-03-13",
      "repositories": [],
      "source": "https://arxiv.org/abs/2403.08593",
      "tasks": [],
      "title": "Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments",
      "verdict": null
    },
    {
      "abstract": "Making a conference poster is visual coding in miniature: an entire paper must be compressed onto a single page, which exercises summarization, visual design, and code implementation at once, using nothing beyond the paper itself.",
      "arxiv_id": "2610.23230",
      "citations": 0,
      "curated_by": [],
      "fit": {
        "score": 1,
        "signals": [
          {
            "code": "nearTask",
            "tasks": [
              "summarization"
            ]
          },
          {
            "code": "named"
          },
          {
            "code": "offTask",
            "tasks": [
              "reinforcement-learning"
            ]
          }
        ]
      },
      "found_at": "2026-10-19",
      "found_by": "catalogue",
      "published": "2026-10-16",
      "repositories": [],
      "source": "https://paperswithcode.co/api/v1/papers/2610.23230",
      "tasks": [
        "reinforcement-learning",
        "summarization"
      ],
      "title": "RAP: A Dataset for Paper-to-Poster Generation via Declarative Markup from Human-Crafted Exemplars",
      "verdict": null
    },
    {
      "abstract": "Multimodal large language models (MLLMs) have advanced visual understanding and reasoning, yet their static parametric knowledge limits their ability to address knowledge-intensive and dynamically evolving open-world problems. To move beyond this limitation, multimodal deep search has emerged as a key direction for open-world information access, evolving from single-turn factual retrieval toward long-horizon, multi-turn search guided by visual evidence.",
      "arxiv_id": "2608.01827",
      "citations": 0,
      "fit": {
        "score": 0,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "offTask",
            "tasks": [
              "image-understanding",
              "reinforcement-learning"
            ]
          }
        ]
      },
      "found_at": "2026-08-12",
      "published": "2026-08-03",
      "repositories": [],
      "source": "https://paperswithcode.co/api/v1/papers/2608.01827",
      "tasks": [
        "image-understanding",
        "reinforcement-learning"
      ],
      "title": "DeepVoyager-VL: Incentivizing Vision-in-the-Loop Search for Long-Horizon Multimodal Agents",
      "verdict": null
    },
    {
      "abstract": "Reinforcement learning (RL) search agents commonly model retrieval as free-form natural-language query generation and optimize multi-turn interactions using final-answer rewards. Current studies mainly improve training with denser or more structured credit signals, but rarely examine whether retrieval is properly formulated at the policy-environment interface.",
      "arxiv_id": "2607.27652",
      "citations": 0,
      "fit": {
        "score": 0,
        "signals": [
          {
            "code": "named"
          },
          {
            "code": "offTask",
            "tasks": [
              "reinforcement-learning"
            ]
          }
        ]
      },
      "found_at": "2026-08-12",
      "published": "2026-07-30",
      "repositories": [],
      "source": "https://paperswithcode.co/api/v1/papers/2607.27652",
      "tasks": [
        "reinforcement-learning"
      ],
      "title": "Harness-G: A Graph-Structured Harness for Search Agents",
      "verdict": null
    }
  ]
}