{"total":24,"rows":[{"id":"2401.13178","title":"AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents","created":"2024-01-24","cited_by_count":339,"area":"agents"},{"id":"2405.17935","title":"Tool Learning with Large Language Models: A Survey","created":"2024-05-28","cited_by_count":223,"area":"agents"},{"id":"2409.00920","title":"ToolACE: Winning the Points of LLM Function Calling","created":"2024-09-02","cited_by_count":216,"area":"agents"},{"id":"2504.01990","title":"Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems","created":"2025-03-31","cited_by_count":126,"area":"agents"},{"id":"2412.17259","title":"LegalAgentBench: Evaluating LLM Agents in Legal Domain","created":"2024-12-23","cited_by_count":59,"area":"agents"},{"id":"2602.00933","title":"MCP-Atlas: A Large-Scale Benchmark for Tool-Use Competency with Real MCP Servers","created":"2026-01-31","cited_by_count":53,"area":"agents"},{"id":"2410.07869","title":"Benchmarking Agentic Workflow Generation","created":"2024-10-10","cited_by_count":37,"area":"agents"},{"id":"2508.08791","title":"Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments","created":"2025-08-12","cited_by_count":19,"area":"agents"},{"id":"2411.13547","title":"ToolScan: A Benchmark for Characterizing Errors in Tool-Use LLMs","created":"2024-11-20","cited_by_count":18,"area":"agents"},{"id":"2601.04688","title":"ToolGate: Contract-Grounded and Verified Tool Execution for LLMs","created":"2026-01-08","cited_by_count":15,"area":"agents"},{"id":"2512.04535","title":"GTM: Simulating the World of Tools for AI Agents","created":"2025-12-04","cited_by_count":14,"area":"agents"},{"id":"2506.11102","title":"Evolutionary Perspectives on the Evaluation of LLM-Based AI Agents: A Comprehensive Survey","created":"2025-06-06","cited_by_count":13,"area":"agents"},{"id":"2412.15495","title":"TL-Training: A Task-Feature-Based Framework for Training Large Language Models in Tool Use","created":"2024-12-20","cited_by_count":11,"area":"agents"},{"id":"2511.01527","title":"TPS-Bench: Evaluating AI Agents' Tool Planning \\& Scheduling Abilities in Compounding Tasks","created":"2025-11-03","cited_by_count":9,"area":"agents"},{"id":"2507.21035","title":"GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis","created":"2025-07-28","cited_by_count":8,"area":"agents"},{"id":"2502.06589","title":"Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training","created":"2025-02-10","cited_by_count":6,"area":"agents"},{"id":"2605.27995","title":"AsyncTool: Evaluating the Asynchronous Function Calling Capability under Multi-Task Scenarios","created":"2026-05-27","cited_by_count":5,"area":"agents"},{"id":"2410.11805","title":"NesTools: A Dataset for Evaluating Nested Tool Learning Abilities of Large Language Models","created":"2024-10-15","cited_by_count":4,"area":"agents"},{"id":"2607.01084","title":"Can Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool Use","created":"2026-07-01","cited_by_count":4,"area":"agents"},{"id":"2605.15077","title":"Concurrency without Model Changes: Future-based Asynchronous Function Calling for LLMs","created":"2026-05-14","cited_by_count":2,"area":"agents"},{"id":"2504.18373","title":"Auto-SLURP: A Benchmark Dataset for Evaluating Multi-Agent Frameworks in Smart Personal Assistant","created":"2025-04-25","cited_by_count":1,"area":"agents"},{"id":"2506.14248","title":"Re-Initialization Token Learning for Tool-Augmented Large Language Models","created":"2025-06-17","cited_by_count":1,"area":"agents"},{"id":"2601.12680","title":"MetaToolAgent: Towards Generalizable Tool Usage in LLMs through Meta-Learning","created":"2026-01-19","cited_by_count":1,"area":"agents"},{"id":"2606.06566","title":"NTILC: Neural Tool Invocation via Learned Compression","created":"2026-06-04","cited_by_count":1,"area":"agents"}],"next_cursor":null}