{"total":54,"rows":[{"id":"2503.21460","title":"Large Language Model Agent: A Survey on Methodology, Applications and Challenges","created":"2025-03-27","cited_by_count":205,"area":"agents"},{"id":"2501.00309","title":"Retrieval-Augmented Generation with Graphs (GraphRAG)","created":"2024-12-31","cited_by_count":172,"area":"rag"},{"id":"2404.11584","title":"The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey","created":"2024-04-17","cited_by_count":168,"area":"agents"},{"id":"2406.04244","title":"Benchmark Data Contamination of Large Language Models: A Survey","created":"2024-06-06","cited_by_count":147,"area":null},{"id":"2404.18824","title":"Benchmarking Benchmark Leakage in Large Language Models","created":"2024-04-29","cited_by_count":106,"area":null},{"id":"2406.12708","title":"AgentReview: Exploring Peer Review Dynamics with LLM Agents","created":"2024-06-18","cited_by_count":93,"area":"agents"},{"id":"2407.04069","title":"A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations","created":"2024-07-04","cited_by_count":85,"area":null},{"id":"2407.08733","title":"Is Your Model Really A Good Math Reasoner? Evaluating Mathematical Reasoning with Checklist","created":"2024-07-11","cited_by_count":48,"area":"lr"},{"id":"2502.17521","title":"Recent Advances in Large Langauge Model Benchmarks against Data Contamination: From Static to Dynamic Evaluation","created":"2025-02-23","cited_by_count":38,"area":null},{"id":"2406.17271","title":"DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning Graph","created":"2024-06-25","cited_by_count":26,"area":"lr"},{"id":"2406.18966","title":"DataGen: Unified Synthetic Dataset Generation via Large Language Models","created":"2024-06-27","cited_by_count":23,"area":"synth"},{"id":"2503.04149","title":"Dynamic Benchmarking of Reasoning Capabilities in Code Large Language Models Under Data Contamination","created":"2025-03-06","cited_by_count":23,"area":"lr"},{"id":"2410.08695","title":"Dynamic Multimodal Evaluation with Flexible Complexity by Vision-Language Bootstrapping","created":"2024-10-11","cited_by_count":22,"area":"mllm"},{"id":"2410.21259","title":"AutoBench-V: Can Large Vision-Language Models Benchmark Themselves?","created":"2024-10-28","cited_by_count":21,"area":"mllm"},{"id":"2502.16268","title":"ThinkBench: Dynamic Out-of-Distribution Evaluation for Robust LLM Reasoning","created":"2025-02-22","cited_by_count":17,"area":"lr"},{"id":"2504.15280","title":"Seeing from Another Perspective: Evaluating Multi-View Understanding in MLLMs","created":"2025-04-21","cited_by_count":16,"area":"mllm"},{"id":"2503.16402","title":"The Emperor's New Clothes in Benchmarking? A Rigorous Examination of Mitigation Strategies for LLM Benchmark Data Contamination","created":"2025-03-20","cited_by_count":15,"area":null},{"id":"2502.01609","title":"Adaptive Distraction: Probing LLM Contextual Robustness with Automated Tree Search","created":"2025-02-03","cited_by_count":10,"area":null},{"id":"2602.12966","title":"ProbeLLM: Automating Principled Diagnosis of LLM Failures","created":"2026-02-13","cited_by_count":9,"area":null},{"id":"2409.09318","title":"ODE: Open-Set Evaluation of Hallucinations in Multimodal Large Language Models","created":"2024-09-14","cited_by_count":8,"area":"mllm"},{"id":"2505.18917","title":"Behavior Injection: Preparing Language Models for Reinforcement Learning","created":"2025-05-25","cited_by_count":8,"area":"lr"},{"id":"2509.24210","title":"BeyondBench: Contamination-Resistant Evaluation of Reasoning in Language Models","created":"2025-09-29","cited_by_count":8,"area":"lr"},{"id":"2502.01683","title":"LLM-Powered Benchmark Factory: Reliable, Generic, and Efficient","created":"2025-02-02","cited_by_count":7,"area":null},{"id":"2503.10452","title":"DynaCode: A Dynamic Complexity-Aware Code Benchmark for Evaluating Large Language Models in Code Generation","created":"2025-03-13","cited_by_count":7,"area":null},{"id":"2505.18102","title":"CapBencher: Give Your LLM Benchmark a Built-in Alarm for Test-Set Overfitting","created":"2025-05-23","cited_by_count":7,"area":null},{"id":"2507.02870","title":"Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Models","created":"2025-06-06","cited_by_count":7,"area":null},{"id":"2409.14302","title":"Reliable and diverse evaluation of LLM medical knowledge mastery","created":"2024-09-22","cited_by_count":6,"area":null},{"id":"2506.02648","title":"Truly Assessing Fluid Intelligence of Large Language Models through Dynamic Reasoning Evaluation","created":"2025-06-03","cited_by_count":6,"area":"lr"},{"id":"2509.23061","title":"Local Success Does Not Compose: Benchmarking Large Language Models for Compositional Formal Verification","created":"2025-09-27","cited_by_count":6,"area":null},{"id":"2505.22467","title":"Topological Structure Learning Should Be A Research Priority for LLM-Based Multi-Agent Systems","created":"2025-05-28","cited_by_count":5,"area":"agents"},{"id":"2506.09656","title":"Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives","created":"2025-06-11","cited_by_count":5,"area":"agents"},{"id":"2604.10988","title":"WebForge: Breaking the Realism-Reproducibility-Scalability Trilemma in Browser Agent Benchmark","created":"2026-04-13","cited_by_count":5,"area":null},{"id":"2411.14062","title":"MMGenBench: Fully Automatically Evaluating LMMs from the Text-to-Image Generation Perspective","created":"2024-11-21","cited_by_count":4,"area":"mllm"},{"id":"2502.06655","title":"Unbiased Evaluation of Large Language Models from a Causal Perspective","created":"2025-02-10","cited_by_count":3,"area":null},{"id":"2502.17800","title":"Your Language Model May Think Too Rigidly: Achieving Reasoning Consistency with Symmetry-Enhanced Training","created":"2025-02-25","cited_by_count":3,"area":"lr"},{"id":"2508.06142","title":"SDEval: Safety Dynamic Evaluation for Multimodal Large Language Models","created":"2025-08-08","cited_by_count":3,"area":"mllm"},{"id":"2509.16543","title":"ChemOrch: Empowering LLMs with Chemical Intelligence via Synthetic Instructions","created":"2025-09-20","cited_by_count":3,"area":"synth"},{"id":"2604.08571","title":"Robust Reasoning Benchmark","created":"2026-03-26","cited_by_count":3,"area":"lr"},{"id":"2502.18308","title":"RefuteBench 2.0 -- Agentic Benchmark for Dynamic Evaluation of LLM Responses to Refutation Instruction","created":"2025-02-25","cited_by_count":2,"area":"agents"},{"id":"2504.08300","title":"Large Language Models Could Be Rote Learners","created":"2025-04-11","cited_by_count":2,"area":null},{"id":"2506.00741","title":"Data Swarms: Optimizable Generation of Synthetic Evaluation Data","created":"2025-05-31","cited_by_count":2,"area":null},{"id":"2508.19570","title":"Generative Models for Synthetic Data: Transforming Data Mining in the GenAI Era","created":"2025-08-27","cited_by_count":2,"area":"diffusion"},{"id":"2511.09139","title":"MACEval: A Multi-Agent Continual Evaluation Network for Large Models","created":"2025-11-12","cited_by_count":2,"area":null},{"id":"2602.23729","title":"From Static Benchmarks to Dynamic Protocol: Agent-Centric Text Anomaly Detection for Evaluating LLM Reasoning","created":"2026-02-27","cited_by_count":2,"area":"lr"},{"id":"2604.20548","title":"Enhancing Research Idea Generation through Combinatorial Innovation and Multi-Agent Iterative Search Strategies","created":"2026-04-22","cited_by_count":2,"area":"agents"},{"id":"2410.22368","title":"Project MPG: towards a generalized performance benchmark for LLM capabilities","created":"2024-10-28","cited_by_count":1,"area":null},{"id":"2412.01526","title":"Addressing Data Leakage in HumanEval Using Combinatorial Test Design","created":"2024-12-02","cited_by_count":1,"area":null},{"id":"2501.13983","title":"AdEval: Alignment-based Dynamic Evaluation to Mitigate Data Contamination in Large Language Models","created":"2025-01-23","cited_by_count":1,"area":null},{"id":"2508.18646","title":"Beyond Benchmarks: LLM Evaluation with an Anthropomorphic and Lifecycle-oriented Roadmap","created":"2025-08-26","cited_by_count":1,"area":null},{"id":"2603.04656","title":"iAgentBench: Benchmarking Sensemaking Capabilities of Information-Seeking Agents on High-Traffic Topics","created":"2026-03-04","cited_by_count":1,"area":"agents"},{"id":"2510.15746","title":"LLMs Judge Themselves: A Game-Theoretic Framework for Human-Aligned Evaluation","created":"2025-10-17","cited_by_count":0,"area":"alignment"},{"id":"2512.08965","title":"Financial Instruction Following Evaluation (FIFE)","created":"2025-12-01","cited_by_count":0,"area":null},{"id":"2602.12424","title":"RankLLM: Weighted Ranking of LLMs by Quantifying Question Difficulty","created":"2026-02-12","cited_by_count":0,"area":null},{"id":"2603.05290","title":"X-RAY: Mapping LLM Reasoning Capability via Formalized and Calibrated Probes","created":"2026-03-05","cited_by_count":0,"area":"lr"}],"next_cursor":null}