This paper proposes ToolCUA, an end-to-end agent designed to learn optimal GUI-Tool path selection through a staged training paradigm, and introduces an Interleaved GUI-Tool Trajectory Scaling Pipeline that repurposes abundant static GUI trajectories and synthesizes a grounded tool library, enabling diverse GUI-Tool tr...
Xuhao Hu, Xi Zhang, Hai-Yang Xu et al.· arXiv.org· 9 citations· ⚡4
VibeMemBench exposes the gap between the useful experience that repository history holds and the experience existing memory systems deliver for repository coding tasks.
Li-Yang Fan, Yingcheng Shi, Yong-Bin Li et al.· 0 citations
This work introduces two exact, gradient-equivalent corrections: LogitTree, a segmented K-forward traversal, and a packed 4D attention mask, and proposes SDCC (Self-Distillation for Conditioning Consistency), a single-backward-pass variational relaxation.
J. Zinco, Xun-Jie Zhu, Shen Huang et al.· 0 citations
This work eliminates external gold supervision from the RL training reward: the model's own evaluative capability generates learning signals for its optimization -- a closed-loop setting of bounded recursive self-improvement (RSI) termed Recursive Self-Evaluation (RecurSE).
Kai-Yuan Liu, Ziyuan Zhuang, Rong-Xiang Weng et al.· 0 citations
State2State is proposed, an environment-derived mid-training method that converts explored environment states into training objectives, challenging agents to reach a specified target state by deriving tasks from environment exploration and verifying success through rule-based state matching.
Xuanyu Lei, Yi-Qi Zhu, Chen-Liang Li et al.· 1 citation
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