6 papers
Last Step Matters: Early Uncertainty Cannot Predict Failure in Long-Horizon Agents
Zongyue Li, Chengyue Yu, Lei Zang +3
Early failure prediction is important for long-horizon agents, as it enables timely intervention and can reduce inference and tool-use costs. Uncertainty quantification, such as ve…
Teach the Magnitude, Not the Direction: Verifier-Anchored Credit Assignment for Multi-Turn Multi-Step LLM Agents
Zechuan Wang, Siyuan Lu, Hongxuan Zhang +3
Reinforcement learning with verifiable rewards (RLVR) directly optimizes verifier-defined outcomes for multi-turn tool-use agents, yet its trajectory-level credit assignment confla…
V2P: Visual Attention Calibration for GUI Grounding via Background Suppression and Center Peaking
Jikai Chen, Long Chen, Dong Wang +6
Precise localization of GUI elements is crucial for the development of GUI agents. Traditional methods rely on bounding box or center-point regression, neglecting spatial interacti…
Don't Just Fine-tune the Agent, Tune the Environment
Siyuan Lu, Zechuan Wang, Hongxuan Zhang +5
Large Language Model (LLM) agents show great promise for complex, multi-turn tool-use tasks, but their development is often hampered by the extreme scarcity of high-quality trainin…
V2P: Visual Attention Calibration for GUI Grounding via Background Suppression and Center Peaking
Jikai Chen, Long Chen, Dong Wang +6
Precise localization of GUI elements is crucial for the development of GUI agents. Traditional methods rely on bounding box or center-point regression, neglecting spatial interacti…
SQLCritic: Correcting Text-to-SQL Generation via Clause-wise Critic
Jikai Chen, Leilei Gan, Ziyu Zhao +3
Existing refinement methods in LLM-based Text-to-SQL systems exhibit limited effectiveness. They often introduce new errors during the self-correction process and fail to detect an…