{"total":166,"rows":[{"id":"2503.05592","title":"R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning","created":"2025-03-07","cited_by_count":336,"area":"lr"},{"id":"2507.19849","title":"Agentic Reinforced Policy Optimization","created":"2025-07-26","cited_by_count":172,"area":"lr"},{"id":"2503.19470","title":"ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning","created":"2025-03-25","cited_by_count":161,"area":"lr"},{"id":"2406.13629","title":"InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales","created":"2024-06-19","cited_by_count":101,"area":"rag"},{"id":"2411.06037","title":"Sufficient Context: A New Lens on Retrieval Augmented Generation Systems","created":"2024-11-09","cited_by_count":72,"area":"rag"},{"id":"2505.14069","title":"Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning","created":"2025-05-20","cited_by_count":70,"area":"rag"},{"id":"2503.21322","title":"HyperGraphRAG: Retrieval-Augmented Generation via Hypergraph-Structured Knowledge Representation","created":"2025-03-27","cited_by_count":68,"area":"rag"},{"id":"2507.21892","title":"Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning","created":"2025-07-29","cited_by_count":52,"area":"rag"},{"id":"2501.14342","title":"Chain-of-Retrieval Augmented Generation","created":"2025-01-24","cited_by_count":49,"area":"lr"},{"id":"2411.19443","title":"Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language Models","created":"2024-11-29","cited_by_count":43,"area":"rag"},{"id":"2407.13101","title":"Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approach","created":"2024-07-18","cited_by_count":40,"area":"rag"},{"id":"2510.14545","title":"Agentic Entropy-Balanced Policy Optimization","created":"2025-10-16","cited_by_count":40,"area":"agents"},{"id":"2408.01262","title":"RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework","created":"2024-08-02","cited_by_count":39,"area":"rag"},{"id":"2411.02959","title":"HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems","created":"2024-11-05","cited_by_count":37,"area":"rag"},{"id":"2506.04185","title":"R-Search: Empowering LLM Reasoning with Search via Multi-Reward Reinforcement Learning","created":"2025-06-04","cited_by_count":34,"area":"lr"},{"id":"2406.16828","title":"Ragnar\\\"ok: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track","created":"2024-06-24","cited_by_count":32,"area":"rag"},{"id":"2501.07861","title":"ReARTeR: Retrieval-Augmented Reasoning with Trustworthy Process Rewarding","created":"2025-01-14","cited_by_count":32,"area":"lr"},{"id":"2503.18016","title":"Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook","created":"2025-03-23","cited_by_count":32,"area":"rag"},{"id":"2407.01102","title":"BERGEN: A Benchmarking Library for Retrieval-Augmented Generation","created":"2024-07-01","cited_by_count":31,"area":"rag"},{"id":"2505.17005","title":"R1-Searcher++: Incentivizing the Dynamic Knowledge Acquisition of LLMs via Reinforcement Learning","created":"2025-05-22","cited_by_count":30,"area":"rag"},{"id":"2505.18581","title":"Removal of Hallucination on Hallucination: Debate-Augmented RAG","created":"2025-05-24","cited_by_count":29,"area":"rag"},{"id":"2406.18676","title":"Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation","created":"2024-06-26","cited_by_count":27,"area":"rag"},{"id":"2409.19804","title":"Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systems","created":"2024-09-29","cited_by_count":26,"area":"rag"},{"id":"2501.00353","title":"RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions","created":"2024-12-31","cited_by_count":25,"area":"rag"},{"id":"2501.02968","title":"FlippedRAG: Black-Box Opinion Manipulation Adversarial Attacks to Retrieval-Augmented Generation Models","created":"2025-01-06","cited_by_count":25,"area":"rag"},{"id":"2505.02811","title":"Knowing You Don't Know: Learning When to Continue Search in Multi-round RAG through Self-Practicing","created":"2025-05-05","cited_by_count":25,"area":"rag"},{"id":"2503.01478","title":"SePer: Measure Retrieval Utility Through The Lens Of Semantic Perplexity Reduction","created":"2025-03-03","cited_by_count":24,"area":"rag"},{"id":"2512.16676","title":"DataFlow: An LLM-Driven Framework for Unified Data Preparation and Workflow Automation in the Era of Data-Centric AI","created":"2025-12-18","cited_by_count":23,"area":null},{"id":"2412.11919","title":"RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation","created":"2024-12-16","cited_by_count":20,"area":null},{"id":"2504.18243","title":"DualRAG: A Dual-Process Approach to Integrate Reasoning and Retrieval for Multi-Hop Question Answering","created":"2025-04-25","cited_by_count":20,"area":null},{"id":"2505.09316","title":"Scent of Knowledge: Optimizing Search-Enhanced Reasoning with Information Foraging","created":"2025-05-14","cited_by_count":20,"area":"lr"},{"id":"2505.23794","title":"R3-RAG: Learning Step-by-Step Reasoning and Retrieval for LLMs via Reinforcement Learning","created":"2025-05-26","cited_by_count":20,"area":"lr"},{"id":"2603.01050","title":"MM-DeepResearch: A Simple and Effective Multimodal Agentic Search Baseline","created":"2026-03-01","cited_by_count":20,"area":"agents"},{"id":"2502.20640","title":"LexRAG: Benchmarking Retrieval-Augmented Generation in Multi-Turn Legal Consultation Conversation","created":"2025-02-28","cited_by_count":19,"area":"rag"},{"id":"2505.18543","title":"Benchmarking Poisoning Attacks against Retrieval-Augmented Generation","created":"2025-05-24","cited_by_count":18,"area":"rag"},{"id":"2503.06580","title":"Agent models: Internalizing Chain-of-Action Generation into Reasoning models","created":"2025-03-09","cited_by_count":17,"area":"lr"},{"id":"2602.01601","title":"Adaptive Rollout Allocation for Online Reinforcement Learning with Verifiable Rewards","created":"2026-02-02","cited_by_count":17,"area":null},{"id":"2410.03537","title":"Ward: Provable RAG Dataset Inference via LLM Watermarks","created":"2024-10-04","cited_by_count":16,"area":"safety"},{"id":"2505.10413","title":"Hierarchical Document Refinement for Long-context Retrieval-augmented Generation","created":"2025-05-15","cited_by_count":16,"area":"rag"},{"id":"2606.28733","title":"Agentic Abstention: Do Agents Know When to Stop Instead of Act?","created":"2026-06-27","cited_by_count":15,"area":"agents"},{"id":"2502.20377","title":"PhantomWiki: On-Demand Datasets for Reasoning and Retrieval Evaluation","created":"2025-02-27","cited_by_count":14,"area":"lr"},{"id":"2601.04888","title":"SmartSearch: Process Reward-Guided Query Refinement for Search Agents","created":"2026-01-08","cited_by_count":14,"area":"agents"},{"id":"2412.08519","title":"Bridging Relevance and Reasoning: Rationale Distillation in Retrieval-Augmented Generation","created":"2024-12-11","cited_by_count":13,"area":"lr"},{"id":"2502.02464","title":"Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation","created":"2025-02-04","cited_by_count":13,"area":"rag"},{"id":"2502.18397","title":"KiRAG: Knowledge-Driven Iterative Retriever for Enhancing Retrieval-Augmented Generation","created":"2025-02-25","cited_by_count":13,"area":"rag"},{"id":"2509.13772","title":"Who Taught the Lie? Responsibility Attribution for Poisoned Knowledge in Retrieval-Augmented Generation","created":"2025-09-17","cited_by_count":12,"area":"rag"},{"id":"2502.18139","title":"LevelRAG: Enhancing Retrieval-Augmented Generation with Multi-hop Logic Planning over Rewriting Augmented Searchers","created":"2025-02-25","cited_by_count":11,"area":"rag"},{"id":"2504.04062","title":"QE-RAG: A Robust Retrieval-Augmented Generation Benchmark for Query Entry Errors","created":"2025-04-05","cited_by_count":11,"area":"rag"},{"id":"2506.00232","title":"ComposeRAG: A Modular and Composable RAG for Corpus-Grounded Multi-Hop Question Answering","created":"2025-05-30","cited_by_count":11,"area":"rag"},{"id":"2601.05503","title":"Over-Searching in Search-Augmented Large Language Models","created":"2026-01-09","cited_by_count":11,"area":null},{"id":"2601.11888","title":"Agentic-R: Learning to Retrieve for Agentic Search","created":"2026-01-17","cited_by_count":11,"area":"agents"},{"id":"2505.08643","title":"WixQA: A Multi-Dataset Benchmark for Enterprise Retrieval-Augmented Generation","created":"2025-05-13","cited_by_count":10,"area":"rag"},{"id":"2510.00568","title":"ReSeek: A Self-Correcting Framework for Search Agents with Instructive Rewards","created":"2025-10-01","cited_by_count":10,"area":"agents"},{"id":"2605.29307","title":"GrepSeek: Training Search Agents for Direct Corpus Interaction","created":"2026-05-28","cited_by_count":10,"area":"agents"},{"id":"2501.10053","title":"AirRAG: Autonomous Strategic Planning and Reasoning Steer Retrieval Augmented Generation","created":"2025-01-17","cited_by_count":9,"area":"lr"},{"id":"2502.20969","title":"TeleRAG: Efficient Retrieval-Augmented Generation Inference with Lookahead Retrieval","created":"2025-02-28","cited_by_count":9,"area":"rag"},{"id":"2503.02003","title":"HoT: Highlighted Chain of Thought for Referencing Supporting Facts from Inputs","created":"2025-03-03","cited_by_count":9,"area":"lr"},{"id":"2505.07903","title":"SEM: Reinforcement Learning for Search-Efficient Large Language Models","created":"2025-05-12","cited_by_count":9,"area":null},{"id":"2510.16844","title":"FinSight: Towards Real-World Financial Deep Research","created":"2025-10-19","cited_by_count":9,"area":null},{"id":"2603.01425","title":"LaSER: Internalizing Explicit Reasoning into Latent Space for Dense Retrieval","created":"2026-03-02","cited_by_count":9,"area":"lr"},{"id":"2604.23783","title":"S2G-RAG: Structured Sufficiency and Gap Judging for Iterative Retrieval-Augmented QA","created":"2026-04-26","cited_by_count":9,"area":"rag"},{"id":"2408.11381","title":"RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation","created":"2024-08-21","cited_by_count":8,"area":"rag"},{"id":"2507.02652","title":"HiRA: A Hierarchical Reasoning Framework for Decoupled Planning and Execution in Deep Search","created":"2025-07-03","cited_by_count":8,"area":"rag"},{"id":"2602.07035","title":"DLLM-Searcher: Adapting Diffusion Large Language Model for Search Agents","created":"2026-02-03","cited_by_count":8,"area":"agents"},{"id":"2508.08088","title":"HierSearch: A Hierarchical Enterprise Deep Search Framework Integrating Local and Web Searches","created":"2025-08-11","cited_by_count":7,"area":"lr"},{"id":"2509.21359","title":"Influence Guided Context Selection for Effective Retrieval-Augmented Generation","created":"2025-09-21","cited_by_count":7,"area":"rag"},{"id":"2511.05385","title":"TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework","created":"2025-11-07","cited_by_count":7,"area":"lr"},{"id":"2408.08444","title":"W-RAG: Weakly Supervised Dense Retrieval in RAG for Open-domain Question Answering","created":"2024-08-15","cited_by_count":6,"area":null},{"id":"2504.08761","title":"UltraRAG: A Modular and Automated Toolkit for Adaptive Retrieval-Augmented Generation","created":"2025-03-31","cited_by_count":6,"area":"rag"},{"id":"2504.10529","title":"HeteRAG: A Heterogeneous Retrieval-augmented Generation Framework with Decoupled Knowledge Representations","created":"2025-04-12","cited_by_count":6,"area":"rag"},{"id":"2510.13272","title":"Beyond Correctness: Rewarding Faithful Reasoning in Retrieval-Augmented Generation","created":"2025-10-15","cited_by_count":6,"area":"lr"},{"id":"2511.01323","title":"DEEPAMBIGQA: Ambiguous Multi-hop Questions for Benchmarking LLM Answer Completeness","created":"2025-11-03","cited_by_count":6,"area":null},{"id":"2512.16883","title":"AdaSearch: Balancing Parametric Knowledge and Search in Large Language Models via Reinforcement Learning","created":"2025-12-18","cited_by_count":6,"area":null},{"id":"2602.00296","title":"RAGRouter-Bench: A Dataset and Benchmark for Adaptive RAG Routing","created":"2026-01-30","cited_by_count":6,"area":"rag"},{"id":"2408.02545","title":"RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation","created":"2024-08-05","cited_by_count":5,"area":"rag"},{"id":"2504.14858","title":"Retrieval is Not Enough: Enhancing RAG Reasoning through Test-Time Critique and Optimization","created":"2025-04-21","cited_by_count":5,"area":"lr"},{"id":"2505.12065","title":"Demystifying and Enhancing the Efficiency of Large Language Model Based Search Agents","created":"2025-05-17","cited_by_count":5,"area":"agents"},{"id":"2506.10802","title":"Constructing and Evaluating Declarative RAG Pipelines in PyTerrier","created":"2025-06-12","cited_by_count":5,"area":"rag"},{"id":"2508.08742","title":"SciRerankBench: Benchmarking Rerankers Towards Scientific Retrieval-Augmented Generated LLMs","created":"2025-08-12","cited_by_count":5,"area":"rag"},{"id":"2510.20548","title":"GlobalRAG: Enhancing Global Reasoning in Multi-hop Question Answering via Reinforcement Learning","created":"2025-10-23","cited_by_count":5,"area":"rag"},{"id":"2505.11932","title":"Neuro-Symbolic Query Compiler","created":"2025-05-17","cited_by_count":4,"area":"rag"},{"id":"2505.13538","title":"RAGXplain: From Explainable Evaluation to Actionable Guidance of RAG Pipelines","created":"2025-05-18","cited_by_count":4,"area":"rag"},{"id":"2506.05766","title":"BioMol-MQA: A Multi-Modal Question Answering Dataset For LLM Reasoning Over Bio-Molecular Interactions","created":"2025-06-06","cited_by_count":4,"area":"lr"},{"id":"2506.07528","title":"Coordinating Search-Informed Reasoning and Reasoning-Guided Search in Claim Verification","created":"2025-06-09","cited_by_count":4,"area":null},{"id":"2506.16444","title":"REIS: A High-Performance and Energy-Efficient Retrieval System with In-Storage Processing","created":"2025-06-19","cited_by_count":4,"area":null},{"id":"2507.07634","title":"FrugalRAG: Less is More in RL Finetuning for Multi-Hop Question Answering","created":"2025-07-10","cited_by_count":4,"area":"lr"},{"id":"2510.04695","title":"Beyond Outcome Reward: Decoupling Search and Answering Improves LLM Agents","created":"2025-10-06","cited_by_count":4,"area":"agents"},{"id":"2601.14289","title":"RPC-Bench: A Fine-grained Benchmark for Research Paper Comprehension","created":"2026-01-14","cited_by_count":4,"area":null},{"id":"2602.13933","title":"HyMem: Hybrid Memory Architecture with Dynamic Retrieval Scheduling","created":"2026-02-15","cited_by_count":4,"area":null},{"id":"2606.12384","title":"APPO: Agentic Procedural Policy Optimization","created":"2026-06-10","cited_by_count":4,"area":"agents"},{"id":"2409.09052","title":"OrthoDoc: Multimodal Large Language Model for Assisting Diagnosis in Computed Tomography","created":"2024-08-30","cited_by_count":3,"area":"mllm"},{"id":"2412.15529","title":"XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation","created":"2024-12-20","cited_by_count":3,"area":"rag"},{"id":"2507.09138","title":"HedraRAG: Coordinating LLM Generation and Database Retrieval in Heterogeneous RAG Serving","created":"2025-07-12","cited_by_count":3,"area":"rag"},{"id":"2509.00798","title":"Progressive Multimodal Search and Reasoning for Knowledge-Intensive Visual Question Answering","created":"2025-08-31","cited_by_count":3,"area":null},{"id":"2509.17486","title":"AttnComp: Attention-Guided Adaptive Context Compression for Retrieval-Augmented Generation","created":"2025-09-22","cited_by_count":3,"area":"rag"},{"id":"2509.22009","title":"GraphSearch: An Agentic Deep Searching Workflow for Graph Retrieval-Augmented Generation","created":"2025-09-26","cited_by_count":3,"area":"lr"},{"id":"2510.10448","title":"RECON: Reasoning with Condensation for Efficient Retrieval-Augmented Generation","created":"2025-10-12","cited_by_count":3,"area":"lr"},{"id":"2510.11457","title":"From <Answer> to <Think>: Multidimensional Supervision of Reasoning Process for LLM Optimization","created":"2025-10-13","cited_by_count":3,"area":"lr"},{"id":"2512.12716","title":"CoDA: A Context-Decoupled Hierarchical Agent with Reinforcement Learning","created":"2025-12-14","cited_by_count":3,"area":"lr"},{"id":"2601.11037","title":"BAPO: Boundary-Aware Policy Optimization for Reliable Agentic Search","created":"2026-01-16","cited_by_count":3,"area":"agents"}],"next_cursor":"100"}