arXiv AI

MOSAIC: Query-Aware Exploration Policy Adaptation for GraphRAG

MOSAIC is a training‑free framework that adapts Graph Retrieval‑Augmented Generation (GraphRAG) to each query by converting query‑specific evidence needs into a bounded policy over seed selection, traversal, stopping, and evidence selection. It keeps the corpus graph, indexes, scoring, grounding, and answer generation shared, while an LLM analyzer tailors the exploration strategy per query. On GraphRAG‑Bench, MOSAIC improves answer correctness by over 5 points on Medical and 4 points on Novel, achieves high evidence recall and context relevancy, and reduces path and evidence evaluations compared to fixed policies.

arXiv Computation and Language

Aug 25

DynaKRAG: A Unified Framework for Learnable Evidence Control in Multi-Hop Retrieval-Augmented Generation

DynaKRAG is a unified framework that learns a state‑conditioned policy to control evidence acquisition in multi‑hop retrieval‑augmented generation. It uses a deterministic validity layer to build an action set, a learned continuation gate to decide between generating an answer or gathering more evidence, and an advantage scorer to rank evidence operations by predicted gain. Across HotpotQA, 2Wiki, and MuSiQue with various backbone models, DynaKRAG achieves top EM and F1 scores, improves token and retrieval efficiency, and enables terminal evidence compression that reduces context size while boosting answer quality.

By Chenyu Zhou, Yaqi Wu, Xiaolei Guo, Jiaqi Huang, Xianfa Zhang, Junxu Zhang, Zhuo Yu, Zhubo Shi, Jianghao Lin, Dongdong Ge

llms rag

arXiv AI

Jul 24

GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG

arXiv:2607. 21324v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems increasingly employ multiple LLM agents.

By Paolo Pedinotti, Enrico Santus

llms rag agents benchmarks

arXiv Computation and Language

Aug 31

Entity-Memory Graph Retrieval Improves Evidence Coverage in Long-Conversation Question Answering

Entity-Memory graph retrieval preserves dialogue turns as verbatim memory nodes, links repeated mentions via shared entities, and connects adjacent memories with chronological edges. During retrieval, the system gates through entities, fuses semantics, and performs one‑hop chronological recovery before dense backfill, allowing it to keep neighboring memories that dense cosine ranking might miss. On 1,986 questions from ten LoCoMo conversations, this graph retrieval method increases official evidence recall at top‑k 25 from 79.7468 % to 84.4842 %, with the advantage extending from top‑k 5 to 50, though it does not improve overall final‑answer F1.

By Shumao Sun

llms rag nlp

Hugging Face Trending Papers

Jul 23

GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG

Retrieval-Augmented Generation (RAG) systems increasingly employ multiple LLM agents. Yet, most prior work optimizes components in isolation rather than coordinating improvements across the pipeline.

llms rag agents benchmarks

arXiv Computation and Language

Sep 1

Hi-Q: Hierarchical Evidence-guided Query Refinement for Multi-Hop Question Answering

Hi-Q is a new framework for multi‑hop question answering that refines queries hierarchically based on evidence retrieved from a corpus. At each node it tests whether the current query unit is supported by evidence; if not, the node is expanded using a dependency‑preserving binary operator and verified for semantic coverage. The resulting query tree grows according to corpus support signals, and Hi‑Q achieves state‑of‑the‑art performance on three multi‑hop QA benchmarks, outperforming both iterative retrieval and graph‑based baselines without constructing a corpus‑wide graph.

By Jueun Kim, Sungho Park, Wook-Shin Han

rag nlp benchmarks

arXiv AI

Sep 7

Iris: Climbing to the Search Frontier

The paper introduces Iris-mini and Iris-pro, two search agents trained at 35B and 397B parameter scales. They use a novel data pipeline that constructs reverse‑engineered multi‑hop queries from web hyperlinks, filters trajectories, and alternates supervised fine‑tuning with reinforcement learning in a process called SFT‑RL climbing. Evaluations on several benchmarks show that, with inference‑time context management, the agents achieve the best open‑source results in their parameter ranges.

By Ziyuan Liu, Hengqi Liu, Zichuan Wang, Yang Qin, Jiachen Liang, Xu Chu, Shaowei Chen, Yuantao Gu, Mu Chuan

agents reinforcement-learning fine-tuning benchmarks data-engineering

arXiv AI

Oct 15

Benchmarking and Training Reward Models for Cross-Source Evidence Grounding in Search Agents

arXiv:2610.17827v1 Announce Type: new Abstract: Existing approaches to training and evaluating search agents focus heavily on final-answer quality, largely neglecting how generated answers map back t...

By Haochen Zhao, Minsheng Hao, Zheng Li, Yifan Li, Zikang Liu, Bowen Qin, Yuhao Cheng, Chuang Fan, Haosheng Zou, Jeff Chen, Sujian Li

agents benchmarks

arXiv Computation and Language

2d ago

Towards a Learnable Retriever for Textual Graph Question Answering

arXiv:2610.33511v1 Announce Type: new Abstract: Textual graph question answering (GraphQA) and graph-based retrieval-augmented generation (GraphRAG) have gained increasing attention as a way to groun...

By Kaifeng Jin, Yuchen Yan, Zhichen Zeng, Xiao Lin, Lihui Liu, Yinglong Xia, Hanghang Tong

llms rag nlp fine-tuning

arXiv Machine Learning

Sep 30

Relevance Is Not Sufficient Evidence: Detecting Evidence Gaps Before Generation in RAG

arXiv:2609.37469v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) grounds large language models in external sources, but retrieved passages often name the right entities without...

By Suting Chen, Peichun Hua, Yunming Xiao

llms rag benchmarks

arXiv AI

Sep 1

PAGE-RAG: Provenance-Aware Graph Evidence Promotion for Fixed-Budget Multi-hop Retrieval-Augmented Generation

arXiv:2608.29753v1 Announce Type: new Abstract: Multi-hop question answering in retrieval-augmented gener?ation (RAG) often benefits from retrieving beyond the few candidates that will finally be rea...

By Haokun Deng, Xunkai Li, Hongchao Qin, Rong-Hua Li

rag nlp benchmarks

arXiv AI

1d ago

When Connectivity Misleads: Guiding Agentic Search with Contrastive Evidence Paths

arXiv:2610.36134v1 Announce Type: cross Abstract: Search agents can use cross-document graphs to discover sources through multi-hop connections and choose subsequent actions based on candidate rankin...

By Junchen Li, Liang Xu, Rongzheng Wang, Qizhi Chen, Junhao Wen, Shuang Liang

agents diffusion benchmarks safety

arXiv AI

Aug 5

ACE-GraphRAG: Agentic Context Engineering for Hierarchical GraphRAG

arXiv:2608. 01269v2 Announce Type: replace-cross Abstract: Hierarchical Graph Retrieval-Augmented Generation (GraphRAG) organizes corpus knowledge at multiple levels of granularity, yet fixed context construction may fail to translate these multi-resolution representations into a context suited to the current query.

By Yongfeng Huang, Yuren Lai, Ruiying Chen, Haoyu Huang, Mingming Zhao, James Cheng

rag agents nlp benchmarks

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