{"total":57,"rows":[{"id":"2602.10090","title":"Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning","created":"2026-02-10","cited_by_count":66,"area":"wm"},{"id":"2601.05808","title":"EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis","created":"2026-01-09","cited_by_count":63,"area":"agents"},{"id":"2604.18292","title":"Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence","created":"2026-04-20","cited_by_count":54,"area":"agents"},{"id":"2512.18832","title":"From Word to World: Can Large Language Models be Implicit Text-based World Models?","created":"2025-12-21","cited_by_count":42,"area":"wm"},{"id":"2602.06820","title":"ScaleEnv: Scaling Environment Synthesis from Scratch for Generalist Interactive Tool-Use Agent Training","created":"2026-02-06","cited_by_count":32,"area":null},{"id":"2602.07274","title":"TermiGen: High-Fidelity Environment and Robust Trajectory Synthesis for Terminal Agents","created":"2026-02-06","cited_by_count":29,"area":"agents"},{"id":"2606.24597","title":"Qwen-AgentWorld: Language World Models for General Agents","created":"2026-06-23","cited_by_count":28,"area":"wm"},{"id":"2602.14721","title":"WebWorld: A Large-Scale World Model for Web Agent Training","created":"2026-02-16","cited_by_count":26,"area":"wm"},{"id":"2601.22607","title":"From Self-Evolving Synthetic Data to Verifiable-Reward RL: Post-Training Multi-turn Interactive Tool-Using Agents","created":"2026-01-30","cited_by_count":21,"area":null},{"id":"2511.19304","title":"AutoEnv: Automated Environments for Measuring Cross-Environment Agent Learning","created":"2025-11-24","cited_by_count":20,"area":"agents"},{"id":"2607.09996","title":"Who&When Pro: Can LLMs Really Attribute Failures in AI Agents?","created":"2026-07-10","cited_by_count":18,"area":"agents"},{"id":"2602.12268","title":"CM2: Reinforcement Learning with Checklist Rewards for Multi-Turn and Multi-Step Agentic Tool Use","created":"2026-02-12","cited_by_count":14,"area":"agents"},{"id":"2604.22748","title":"Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond","created":"2026-04-24","cited_by_count":14,"area":"wm"},{"id":"2601.10355","title":"Unlocking Implicit Experience: Synthesizing Tool-Use Trajectories from Text","created":"2026-01-15","cited_by_count":13,"area":null},{"id":"2603.11076","title":"DIVE: Scaling Diversity in Agentic Task Synthesis for Generalizable Tool Use","created":"2026-03-10","cited_by_count":13,"area":"agents"},{"id":"2603.00656","title":"InfoPO: Information-Driven Policy Optimization for User-Centric Agents","created":"2026-02-28","cited_by_count":12,"area":"agents"},{"id":"2608.19197","title":"SPADE: Self-Play in Adaptive Synthetic Executable Environments","created":"2026-08-19","cited_by_count":12,"area":"synth"},{"id":"2605.19769","title":"OpenComputer: Verifiable Software Worlds for Computer-Use Agents","created":"2026-05-19","cited_by_count":10,"area":"agents"},{"id":"2605.29801","title":"AgentDoG 1.5: A Lightweight and Scalable Alignment Framework for AI Agent Safety and Security","created":"2026-05-28","cited_by_count":8,"area":"agents"},{"id":"2606.12191","title":"Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application","created":"2026-06-10","cited_by_count":8,"area":"agents"},{"id":"2604.10866","title":"OccuBench: Evaluating AI Agents on Real-World Professional Tasks via Language Environment Simulation","created":"2026-04-13","cited_by_count":7,"area":"agents"},{"id":"2603.05553","title":"EigenData: A Self-Evolving Multi-Agent Platform for Function-Calling Data Synthesis, Auditing, and Repair","created":"2026-03-05","cited_by_count":6,"area":"agents"},{"id":"2511.09572","title":"SynthTools: A Framework for Scaling Synthetic Tools for Agent Development","created":"2025-11-11","cited_by_count":5,"area":"agents"},{"id":"2601.12822","title":"MirrorGuard: Toward Secure Computer-Use Agents via Simulation-to-Real Reasoning Correction","created":"2026-01-19","cited_by_count":5,"area":"agents"},{"id":"2602.03219","title":"Beyond Quantity: Trajectory Diversity Scaling for Code Agents","created":"2026-02-03","cited_by_count":5,"area":"agents"},{"id":"2606.09032","title":"Bridging the Agent-World Gap: Text World Models for LLM-based Agents","created":"2026-06-08","cited_by_count":5,"area":"wm"},{"id":"2603.01714","title":"TopoCurate:Modeling Interaction Topology for Tool-Use Agent Training","created":"2026-03-02","cited_by_count":4,"area":null},{"id":"2603.01940","title":"CoVe: Training Interactive Tool-Use Agents via Constraint-Guided Verification","created":"2026-03-02","cited_by_count":4,"area":null},{"id":"2601.07309","title":"ARM: Role-Conditioned Neuron Transplantation for Training-Free Generalist LLM Agent Merging","created":"2026-01-12","cited_by_count":3,"area":"agents"},{"id":"2607.15660","title":"ToolVerse: Unlocking Massive Environments and Long-Horizon Tasks for Agentic Reinforcement Learning","created":"2026-07-17","cited_by_count":3,"area":"lr"},{"id":"2608.27260","title":"What Makes Good Agentic Data? An ACE Lens on Data Generation for LLM Agents","created":"2026-08-27","cited_by_count":3,"area":"agents"},{"id":"2602.19000","title":"MagicAgent: Towards Generalized Agent Planning","created":"2026-02-22","cited_by_count":2,"area":"agents"},{"id":"2605.18181","title":"Scalable Environments Drive Generalizable Agents","created":"2026-05-18","cited_by_count":2,"area":null},{"id":"2606.02908","title":"WRIT: Write-Read Intensive Trajectory Synthesis for Multi-Turn User-Facing Agents","created":"2026-06-01","cited_by_count":2,"area":"agents"},{"id":"2606.19047","title":"RODS: Reward-Driven Online Data Synthesis for Multi-Turn Tool-Use Agents","created":"2026-06-17","cited_by_count":2,"area":"synth"},{"id":"2607.07820","title":"DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment","created":"2026-07-08","cited_by_count":2,"area":"lr"},{"id":"2607.16900","title":"Simulate to Generalize: Scaling Stateful Supervision for API-calling Agents using LLM World Models","created":"2026-07-18","cited_by_count":2,"area":"wm"},{"id":"2608.06197","title":"EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning","created":"2026-08-06","cited_by_count":2,"area":"wm"},{"id":"2609.05837","title":"AgentBrew: Offline Tool-Use Agent Learning from Raw Real-World Trajectories","created":"2026-09-05","cited_by_count":2,"area":"agents"},{"id":"2604.17739","title":"Democratizing Tool Learning with Environments Fully Simulated by a Free 8B Language Model","created":"2026-04-20","cited_by_count":1,"area":"agents"},{"id":"2605.07247","title":"EnvSimBench: A Benchmark for Evaluating and Improving LLM-Based Environment Simulation","created":"2026-05-08","cited_by_count":1,"area":null},{"id":"2606.16733","title":"A First-Principles Derivation of LLM Policy Optimization: From Expected Reward to GRPO and Its Structural Extensions","created":"2026-06-15","cited_by_count":1,"area":"lr"},{"id":"2608.17499","title":"Towards Better Agents for Multi-Turn User Interaction: The Next User Turn Is More Than Context","created":"2026-08-18","cited_by_count":1,"area":null},{"id":"2608.20314","title":"MidTool: Mid-training Data Synthesis for Agentic Tool Use","created":"2026-08-20","cited_by_count":1,"area":"agents"},{"id":"2609.05576","title":"EnvCraft: Synthesizing Executable Environments in Agentic RL for Claw-like Agent","created":"2026-09-04","cited_by_count":1,"area":"agents"},{"id":"2609.31995","title":"Before the Rollout Ends: Early Terminal Reward Prediction for Long-horizon Coding Agents","created":"2026-09-25","cited_by_count":1,"area":"agents"},{"id":"2602.20424","title":"Implicit Intelligence -- Evaluating Agents on What Users Don't Say","created":"2026-02-23","cited_by_count":0,"area":"agents"},{"id":"2608.26133","title":"Agent Seer: Synthesizing Scenarios from Specification Understanding","created":"2026-06-24","cited_by_count":0,"area":"agents"},{"id":"2608.27508","title":"WM-R1: Training GUI Agents to Reason and leverage World Models with Reinforcement Learning","created":"2026-08-27","cited_by_count":0,"area":"wm"},{"id":"2609.28416","title":"Agent-Editing World Model: Rethinking World Modeling for LLM Agents","created":"2026-09-23","cited_by_count":0,"area":"wm"},{"id":"2609.30698","title":"MM-VeriAgent: Learning to Use Extensive Tools to Verify Multimodal Misinformation with Reinforcement Learning","created":"2026-09-25","cited_by_count":0,"area":"lr"},{"id":"2609.33618","title":"ParaAgent: Reinforcing Parallel Acting in Open-World Tool Environments","created":"2026-09-27","cited_by_count":0,"area":"agents"},{"id":"2610.07332","title":"Structuring MoE Expert Selection for Agentic Reinforcement Learning","created":"2026-10-05","cited_by_count":0,"area":"agents"},{"id":"2610.13438","title":"Rollout-Time Intervention: Unlocking Agentic Reinforcement Learning Beyond Policy Limits","created":"2026-10-09","cited_by_count":0,"area":"lr"},{"id":"2610.14774","title":"ProgAgent: Learning Tool Orchestration through Programmatic Training","created":"2026-10-10","cited_by_count":0,"area":"agents"},{"id":"2610.16027","title":"Of Errors and Echoes: Responsibility-Aware Credit Assignment for Tool-Use Agents","created":"2026-10-12","cited_by_count":0,"area":null},{"id":"2610.17953","title":"Agent-Reflex: Teaching Language Agents to Act Reflectively","created":"2026-10-13","cited_by_count":0,"area":"agents"}],"next_cursor":null}