{"total":59,"rows":[{"id":"2406.11176","title":"Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement","created":"2024-06-17","cited_by_count":88,"area":"agents"},{"id":"2508.04652","title":"LLM Collaboration With Multi-Agent Reinforcement Learning","created":"2025-08-06","cited_by_count":49,"area":"agents"},{"id":"2505.21496","title":"UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents","created":"2025-05-27","cited_by_count":34,"area":"agents"},{"id":"2607.14777","title":"SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning","created":"2026-07-16","cited_by_count":32,"area":"agents"},{"id":"2502.18407","title":"AgentRM: Enhancing Agent Generalization with Reward Modeling","created":"2025-02-25","cited_by_count":27,"area":"agents"},{"id":"2412.21154","title":"Aviary: training language agents on challenging scientific tasks","created":"2024-12-30","cited_by_count":26,"area":"agents"},{"id":"2402.01469","title":"AMOR: A Recipe for Building Adaptable Modular Knowledge Agents Through Process Feedback","created":"2024-02-02","cited_by_count":25,"area":"agents"},{"id":"2410.07745","title":"StepTool: Enhancing Multi-Step Tool Usage in LLMs via Step-Grained Reinforcement Learning","created":"2024-10-10","cited_by_count":25,"area":"agents"},{"id":"2402.15809","title":"Empowering Large Language Model Agents through Action Learning","created":"2024-02-24","cited_by_count":22,"area":"agents"},{"id":"2411.03817","title":"From Novice to Expert: LLM Agent Policy Optimization via Step-wise Reinforcement Learning","created":"2024-11-06","cited_by_count":22,"area":"agents"},{"id":"2410.09713","title":"Agentic Information Retrieval","created":"2024-10-13","cited_by_count":21,"area":"agents"},{"id":"2602.05073","title":"Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities","created":"2026-02-04","cited_by_count":20,"area":"agents"},{"id":"2509.10401","title":"Abduct, Act, Predict: Scaffolding Causal Inference for Automated Failure Attribution in Multi-Agent Systems","created":"2025-09-12","cited_by_count":14,"area":"agents"},{"id":"2412.08445","title":"TapeAgents: a Holistic Framework for Agent Development and Optimization","created":"2024-12-11","cited_by_count":13,"area":"agents"},{"id":"2604.06240","title":"The Art of Building Verifiers for Computer Use Agents","created":"2026-04-05","cited_by_count":13,"area":"agents"},{"id":"2604.23581","title":"AgentEval: DAG-Structured Step-Level Evaluation for Agentic Workflows with Error Propagation Tracking","created":"2026-04-26","cited_by_count":9,"area":"agents"},{"id":"2608.05179","title":"Autonomous Research Agents: A Survey of AI Scientists and the Verification Gap","created":"2026-06-29","cited_by_count":9,"area":"agents"},{"id":"2506.05305","title":"ProRefine: Inference-Time Prompt Refinement with Textual Feedback","created":"2025-06-05","cited_by_count":8,"area":"agents"},{"id":"2601.21972","title":"Learning Decentralized LLM Collaboration with Multi-Agent Actor Critic","created":"2026-01-29","cited_by_count":8,"area":"agents"},{"id":"2604.08178","title":"Aligning Agents via Planning: A Benchmark for Trajectory-Level Reward Modeling","created":"2026-04-09","cited_by_count":8,"area":"agents"},{"id":"2508.02921","title":"PentestJudge: Judging Agent Behavior Against Operational Requirements","created":"2025-08-04","cited_by_count":7,"area":"agents"},{"id":"2605.16217","title":"Argus: Evidence Assembly for Scalable Deep Research Agents","created":"2026-05-15","cited_by_count":6,"area":"agents"},{"id":"2603.03800","title":"A Rubric-Supervised Critic from Sparse Real-World Outcomes","created":"2026-03-04","cited_by_count":5,"area":"agents"},{"id":"2606.27154","title":"OpenRCA 2.0: From Outcome Labels to Causal Process Supervision","created":"2026-06-25","cited_by_count":5,"area":"agents"},{"id":"2510.14703","title":"ToolPRM: Fine-Grained Inference Scaling of Structured Outputs for Function Calling","created":"2025-10-16","cited_by_count":4,"area":"agents"},{"id":"2603.12011","title":"Can RL Improve Generalization of LLM Agents? An Empirical Study","created":"2026-03-12","cited_by_count":4,"area":"agents"},{"id":"2605.27955","title":"Skill-as-Pseudocode: Refactoring Skill Libraries to Pseudocode for LLM Agents","created":"2026-05-27","cited_by_count":4,"area":"agents"},{"id":"2606.07367","title":"Self-evolving LLM agents with in-distribution Optimization","created":"2026-06-05","cited_by_count":4,"area":"agents"},{"id":"2503.10105","title":"StepMathAgent: A Step-Wise Agent for Evaluating Mathematical Processes through Tree-of-Error","created":"2025-03-13","cited_by_count":3,"area":"agents"},{"id":"2605.20061","title":"Rewarding Beliefs, Not Actions: Consistency-Guided Credit Assignment for Long-Horizon Agents","created":"2026-05-19","cited_by_count":3,"area":"agents"},{"id":"2608.28281","title":"LoopArena: Benchmarking Models as Runtime Controllers for Loop Engineering","created":"2026-08-28","cited_by_count":3,"area":"agents"},{"id":"2604.14877","title":"Does RL Expand the Capability Boundary of LLM Agents? A PASS@(k,T) Analysis","created":"2026-04-16","cited_by_count":2,"area":"agents"},{"id":"2606.12916","title":"MDForge: Agentic Molecular Dynamics Pipeline Design under Sparse Simulator Feedback","created":"2026-06-11","cited_by_count":2,"area":"agents"},{"id":"2606.21399","title":"Calibration Is Not Control: Why LLM-Agent Oversight Needs Intervention","created":"2026-06-19","cited_by_count":2,"area":"agents"},{"id":"2609.00038","title":"trajectory-judge: What Outcome-Only LLM Judges Miss on Agent Trajectories","created":"2026-08-29","cited_by_count":2,"area":"agents"},{"id":"2609.12424","title":"Granularity-Adaptive Credit Assignment for Long-Horizon LLM Agent Reinforcement Learning","created":"2026-09-11","cited_by_count":2,"area":"agents"},{"id":"2609.20474","title":"How Do Agent Harnesses Create Value? Planning Information and Release Control in Stateful LLM Agents","created":"2026-09-17","cited_by_count":2,"area":"agents"},{"id":"2608.01805","title":"CockpitHAT: Dependency-Graph-Driven Hierarchical Attribution for Embodied Multi-Agent Cockpits","created":"2026-08-03","cited_by_count":1,"area":"agents"},{"id":"2608.29605","title":"Hindsight Memory-PRM: Supervising Memory Management with Auditable Hindsight Credit","created":"2026-08-30","cited_by_count":1,"area":"agents"},{"id":"2609.04629","title":"SiLR: Structure-Preserving Admission and Process Reward for LLM Tool Agents","created":"2026-09-04","cited_by_count":1,"area":"agents"},{"id":"2601.22491","title":"SSL: Sweet Spot Learning for Differentiated Guidance in Agentic Optimization","created":"2026-01-30","cited_by_count":0,"area":"agents"},{"id":"2602.05353","title":"AgentXRay: White-Boxing Agentic Systems via Workflow Reconstruction","created":"2026-02-05","cited_by_count":0,"area":"agents"},{"id":"2602.17910","title":"Alignment in Time: Peak-Aware Orchestration for Long-Horizon Agentic Systems","created":"2026-02-20","cited_by_count":0,"area":"agents"},{"id":"2606.01830","title":"CAPF: Guiding Search-Agent Rollouts with Credit-Attenuated Privileged Feedback","created":"2026-06-01","cited_by_count":0,"area":"agents"},{"id":"2607.14108","title":"Eta Given Delta: Defining LLM Tool Efficiency With Marginal Tool Utility","created":"2026-05-07","cited_by_count":0,"area":"agents"},{"id":"2608.28128","title":"VICT: Verifier-Instrumented Credit Tracing for Long-Horizon LLM Agent Reinforcement Learning","created":"2026-08-28","cited_by_count":0,"area":"agents"},{"id":"2609.06783","title":"AURA-Eval: Evaluation Framework for Acting Under Risk Awareness in LLM Agent Trajectories","created":"2026-09-06","cited_by_count":0,"area":"agents"},{"id":"2609.07395","title":"Uncertainty Quantification for LLM Agents: A Taxonomy, an Evaluation Protocol, and an Empirical Study","created":"2026-09-07","cited_by_count":0,"area":"agents"},{"id":"2609.14422","title":"Safety Signals to Verify NetOps Agents with Action-Level Granularity","created":"2026-09-13","cited_by_count":0,"area":"agents"},{"id":"2609.24983","title":"onPanda: Efficient Annotation of On-Policy Alignment Data for LLMs and Agents via Token-Level Correction","created":"2026-09-21","cited_by_count":0,"area":"agents"},{"id":"2609.29578","title":"PartHackBench: Certified Equal-Progress Stress Tests for Partial-Credit Tool-Agent Evaluation","created":"2026-08-27","cited_by_count":0,"area":"agents"},{"id":"2609.33822","title":"Vestrum: Improving Agent Harnesses by Adapting Their Verification, Structure and Memory","created":"2026-09-27","cited_by_count":0,"area":"agents"},{"id":"2610.01207","title":"Dependency-Aware Reward Shaping for Agentic Reinforcement Learning","created":"2026-10-01","cited_by_count":0,"area":"agents"},{"id":"2610.01509","title":"Sharpening Tax in Post-Training","created":"2026-10-01","cited_by_count":0,"area":"agents"},{"id":"2610.08993","title":"Verify Less, Evolve More: Training Idea-Level Critics for Verification-Efficient ML Evolving Agents","created":"2026-10-06","cited_by_count":0,"area":"agents"},{"id":"2610.09858","title":"Training Advisors for LLM Agents from Task Outcomes","created":"2026-10-07","cited_by_count":0,"area":"agents"},{"id":"2610.13755","title":"Matched-Context Group Policy Optimization for LLM Agent Training","created":"2026-10-09","cited_by_count":0,"area":"agents"},{"id":"2610.17724","title":"Evidence-First Agentic Verification","created":"2026-10-13","cited_by_count":0,"area":"agents"},{"id":"2610.19614","title":"Divergent Paths: Learning an Outcome-Predictive Representation of LLM Agent Trajectories","created":"2026-10-14","cited_by_count":0,"area":"agents"}],"next_cursor":null}