{"total":66,"rows":[{"id":"2507.09477","title":"Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs","created":"2025-07-13","cited_by_count":42,"area":"lr"},{"id":"2504.18041","title":"RAG LLMs are Not Safer: A Safety Analysis of Retrieval-Augmented Generation for Large Language Models","created":"2025-04-25","cited_by_count":24,"area":"rag"},{"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":"2501.03151","title":"Large language models for artificial general intelligence (AGI): A survey of foundational principles and approaches","created":"2025-01-06","cited_by_count":18,"area":"mllm"},{"id":"2504.10499","title":"Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey","created":"2025-04-08","cited_by_count":18,"area":"rag"},{"id":"2510.26493","title":"Context Engineering 2.0: The Context of Context Engineering","created":"2025-10-30","cited_by_count":18,"area":null},{"id":"2502.07555","title":"O1 Embedder: Let Retrievers Think Before Action","created":"2025-02-11","cited_by_count":15,"area":"lr"},{"id":"2506.20963","title":"EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora","created":"2025-06-26","cited_by_count":15,"area":"rag"},{"id":"2504.16787","title":"Credible Plan-Driven RAG Method for Multi-Hop Question Answering","created":"2025-04-23","cited_by_count":12,"area":"rag"},{"id":"2506.02672","title":"EvaLearn: Quantifying the Learning Capability and Efficiency of LLMs via Sequential Problem Solving","created":"2025-06-03","cited_by_count":10,"area":null},{"id":"2602.23368","title":"Keyword search is all you need: Achieving RAG-Level Performance without vector databases using agentic tool use","created":"2025-12-19","cited_by_count":10,"area":"rag"},{"id":"2603.01152","title":"DeepResearch-9K: A Challenging Benchmark Dataset of Deep-Research Agent","created":"2026-03-01","cited_by_count":10,"area":"agents"},{"id":"2504.03561","title":"SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement","created":"2025-04-04","cited_by_count":9,"area":"agents"},{"id":"2510.15253","title":"Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding","created":"2025-10-17","cited_by_count":9,"area":"rag"},{"id":"2504.01509","title":"PROPHET: An Inferable Future Forecasting Benchmark with Causal Intervened Likelihood Estimation","created":"2025-04-02","cited_by_count":8,"area":null},{"id":"2509.10697","title":"A Survey on Retrieval And Structuring Augmented Generation with Large Language Models","created":"2025-09-12","cited_by_count":8,"area":null},{"id":"2510.08383","title":"QAgent: A modular Search Agent with Interactive Query Understanding","created":"2025-10-09","cited_by_count":8,"area":"rag"},{"id":"2411.19463","title":"Understanding the Fundamental Design Decisions of Retrieval-Augmented Generation Systems","created":"2024-11-29","cited_by_count":7,"area":"rag"},{"id":"2505.02171","title":"A New HOPE: Domain-agnostic Automatic Evaluation of Text Chunking","created":"2025-05-04","cited_by_count":7,"area":"rag"},{"id":"2505.15753","title":"Scalable Defense against In-the-wild Jailbreaking Attacks with Safety Context Retrieval","created":"2025-05-21","cited_by_count":7,"area":"safety"},{"id":"2601.14952","title":"CorpusQA: A 10 Million Token Benchmark for Corpus-Level Analysis and Reasoning","created":"2026-01-21","cited_by_count":7,"area":"lr"},{"id":"2502.14100","title":"Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach","created":"2025-02-19","cited_by_count":6,"area":"rag"},{"id":"2508.04289","title":"Method-Based Reasoning for Large Language Models: Extraction, Reuse, and Continuous Improvement","created":"2025-08-06","cited_by_count":6,"area":"lr"},{"id":"2509.03536","title":"PG-Agent: An Agent Powered by Page Graph","created":"2025-08-27","cited_by_count":6,"area":"mllm"},{"id":"2505.00254","title":"AVA: Towards Agentic Video Analytics with Vision Language Models","created":"2025-05-01","cited_by_count":5,"area":"mllm"},{"id":"2505.10989","title":"DRAGON: Domain-specific Robust Automatic Data Generation for RAG Optimization","created":"2025-05-16","cited_by_count":5,"area":"rag"},{"id":"2506.20274","title":"Enterprise Large Language Model Evaluation Benchmark","created":"2025-06-25","cited_by_count":5,"area":null},{"id":"2507.21526","title":"Accelerating Prefilling via Decoding-time Contribution Sparsity","created":"2025-07-29","cited_by_count":5,"area":null},{"id":"2508.00271","title":"MetaAgent: Toward Self-Evolving Agent via Tool Meta-Learning","created":"2025-08-01","cited_by_count":5,"area":"agents"},{"id":"2511.15169","title":"SafeRBench: Dissecting the Reasoning Safety of Large Language Models","created":"2025-11-19","cited_by_count":4,"area":"lr"},{"id":"2504.00187","title":"Insight-RAG: Enhancing LLMs with Insight-Driven Augmentation","created":"2025-03-31","cited_by_count":2,"area":"rag"},{"id":"2505.03214","title":"DocSpiral: A Platform for Integrated Assistive Document Annotation through Human-in-the-Spiral","created":"2025-05-06","cited_by_count":2,"area":null},{"id":"2506.12494","title":"FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation","created":"2025-06-14","cited_by_count":2,"area":"rag"},{"id":"2509.03540","title":"Improving Factuality in LLMs via Inference-Time Knowledge Graph Construction","created":"2025-08-31","cited_by_count":2,"area":null},{"id":"2510.09266","title":"CFVBench: A Comprehensive Video Benchmark for Fine-grained Multimodal Retrieval-Augmented Generation","created":"2025-10-10","cited_by_count":2,"area":"mllm"},{"id":"2603.14006","title":"Beyond Explicit Edges: Robust Reasoning over Noisy and Sparse Knowledge Graphs","created":"2026-03-14","cited_by_count":2,"area":"lr"},{"id":"2605.29640","title":"VikingMem: A Memory Base Management System for Stateful LLM-based Applications","created":"2026-05-28","cited_by_count":2,"area":null},{"id":"2412.16615","title":"Large Language Model Can Be a Foundation for Hidden Rationale-Based Retrieval","created":"2024-12-21","cited_by_count":1,"area":"rag"},{"id":"2501.08262","title":"Addressing the sustainable AI trilemma: a case study on LLM agents and RAG","created":"2025-01-14","cited_by_count":1,"area":"agents"},{"id":"2502.09156","title":"Improving TCM Question Answering through Tree-Organized Self-Reflective Retrieval with LLMs","created":"2025-02-13","cited_by_count":1,"area":null},{"id":"2502.18471","title":"FinBloom: Knowledge Grounding Large Language Model with Real-time Financial Data","created":"2025-02-04","cited_by_count":1,"area":null},{"id":"2504.05607","title":"FactGuard: Leveraging Multi-Agent Systems to Generate Answerable and Unanswerable Questions for Enhanced Long-Context LLM Extraction","created":"2025-04-08","cited_by_count":1,"area":"agents"},{"id":"2505.02271","title":"Real-time Spatial Retrieval Augmented Generation for Urban Environments","created":"2025-05-04","cited_by_count":1,"area":"rag"},{"id":"2505.02418","title":"SymbioticRAG: Enhancing Document Intelligence Through Human-LLM Symbiotic Collaboration","created":"2025-05-05","cited_by_count":1,"area":"rag"},{"id":"2506.06509","title":"Private GPTs for LLM-driven testing in software development and machine learning","created":"2025-06-06","cited_by_count":1,"area":null},{"id":"2506.10030","title":"Safeguarding Multimodal Knowledge Copyright in the RAG-as-a-Service Environment","created":"2025-06-10","cited_by_count":1,"area":"rag"},{"id":"2508.19357","title":"Context-Adaptive Synthesis and Compression for Enhanced Retrieval-Augmented Generation in Complex Domains","created":"2025-08-26","cited_by_count":1,"area":"rag"},{"id":"2510.00552","title":"Data Quality Challenges in Retrieval-Augmented Generation","created":"2025-10-01","cited_by_count":1,"area":"rag"},{"id":"2602.05512","title":"A Human-in-the-Loop, LLM-Centered Architecture for Knowledge-Graph Question Answering","created":"2026-02-05","cited_by_count":1,"area":null},{"id":"2607.05511","title":"Light-Omni: Reflex over Reasoning in Agentic Video Understanding with Long-Term Memory","created":"2026-07-06","cited_by_count":1,"area":"lr"},{"id":"2502.20854","title":"A Pilot Empirical Study on When and How to Use Knowledge Graphs as Retrieval Augmented Generation","created":"2025-02-28","cited_by_count":0,"area":"rag"},{"id":"2504.11704","title":"A Library of LLM Intrinsics for Retrieval-Augmented Generation","created":"2025-04-16","cited_by_count":0,"area":"rag"},{"id":"2505.17206","title":"FB-RAG: Improving RAG with Forward and Backward Lookup","created":"2025-05-22","cited_by_count":0,"area":"rag"},{"id":"2507.04706","title":"UrbanMind: Towards Urban General Intelligence via Tool-Enhanced Retrieval-Augmented Generation and Multilevel Optimization","created":"2025-07-07","cited_by_count":0,"area":"rag"},{"id":"2507.09389","title":"Knowledge Conceptualization Impacts RAG Efficacy","created":"2025-07-12","cited_by_count":0,"area":null},{"id":"2507.22419","title":"Systematic Evaluation of Knowledge Graph Repair with Large Language Models","created":"2025-07-30","cited_by_count":0,"area":null},{"id":"2511.04700","title":"Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation","created":"2025-11-01","cited_by_count":0,"area":"rag"},{"id":"2602.04466","title":"Is Micro Domain-Adaptive Pre-Training Effective for Real-World Operations? Multi-Step Evaluation Reveals Potential and Bottlenecks","created":"2026-02-04","cited_by_count":0,"area":null},{"id":"2602.22828","title":"TCM-DiffRAG: Personalized Syndrome Differentiation Reasoning Method for Traditional Chinese Medicine based on Knowledge Graph and Chain of Thought","created":"2026-02-26","cited_by_count":0,"area":"lr"},{"id":"2604.23336","title":"Efficient Rationale-based Retrieval: On-policy Distillation from Generative Rerankers based on JEPA","created":"2026-04-25","cited_by_count":0,"area":"distill"},{"id":"2605.14259","title":"Hypergraph Enterprise Agentic Reasoner over Heterogeneous Business Systems","created":"2026-05-14","cited_by_count":0,"area":"lr"},{"id":"2605.17101","title":"SEMA-RAG: A Self-Evolving Multi-Agent Retrieval-Augmented Generation Framework for Medical Reasoning","created":"2026-05-16","cited_by_count":0,"area":"lr"},{"id":"2607.05516","title":"Statistical Adversaries: Natural Backdoor-like Adversarial Features in Clean Vision Datasets","created":"2026-07-06","cited_by_count":0,"area":null},{"id":"2609.07629","title":"Open Tabular Insight Extraction: Where Do We Stand, and Where Should We Go?","created":"2026-09-07","cited_by_count":0,"area":null},{"id":"2609.32226","title":"Toward Agentic Optical Networks: A Vision of LLM Agent-Driven Autonomous Lifecycle Management","created":"2026-09-26","cited_by_count":0,"area":"agents"},{"id":"2610.01936","title":"Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning","created":"2026-10-01","cited_by_count":0,"area":"lr"}],"next_cursor":null}