Graphical user interface (GUI) agents built on vision-language models have emerged as a promising approach to automate human-computer workflows. However, they also face the inefficiency challenge as they process long sequences of high-resolution screenshots and solving long-horizon tasks, making inference slow, costly...
Kung-Hsiang Huang, Haoyi Qiu, Yu-Tong Dai et al.· Trans. Mach. Learn. Res.· 12 citations· ⚡3
Terminal-agent capability depends jointly on model weights and the runtime harness that formats prompts, binds tools, and handles error recovery. Existing harness-model co-evolution approaches improve both components, yet often treat trajectories produced during harness search as an undifferentiated replay buffer. This...
Ji-Xuan Chen, Jia-Xin Zhang, Qinyuan Ye et al.· 1 citation
Reinforcement learning with verifiable rewards (RLVR) turns agent experience into learning signals primarily through scalar outcome rewards after interaction. For group-relative objectives, however, this signal vanishes when all rollouts receive the same reward, even though their trajectories may reveal useful informat...
Haoxiang Zhang, Qing-Lin Chen, Hiroaki Hayashi et al.· 1 citation
Reinforcement learning for long-horizon agents relies on purely retrospective training signals: credit is assigned only after observing environmental consequences, leaving the agent's belief at action time invisible to the gradient. We introduce Prospective Hindsight (PH), a self-calibrating training principle that aug...
Jia-Xin Zhang, Xiang-Yu Peng, Qing-Lin Chen et al.· 0 citations
This work formalizes fork placement as locating the pivots of the chain's value curve, where the expected outcome turns, and proposes belief-shift branching, which read the model's answer belief at candidate boundaries and fork just before the step where consecutive beliefs diverge most.
Bin Lei, Yu Li, Prafulla Kumar Choubey et al.· 0 citations
Memory-augmented speculation yields a 19--39\% relative accuracy improvement on action prediction and up to a $2.5\times$ increase on observation prediction tasks with repetitive action spaces as memory accumulates and generalize across speculator models of varying cost.
Yu Li, Qinyuan Ye, Prafulla Kumar Choubey et al.· arXiv.org· 2 citations· ⚡1
This work introduces a controlled testbed for studying how goal-directed persuaders shift elicited stances in networks of LLM agents grounded in real-world ego-network topologies, and argues for evaluating multi-agent persuasion as a trajectory- and exposure-level process.
Haoyi Qiu, Genglin Liu, P. Venkit et al.· 0 citations
A comprehensive re-evaluation of two memory-based methods for self-improving agents is conducted, broadening the scope of evaluation along two axes and hypothesizing that task and environment underspecification contribute to this fragility.
Qinyuan Ye, Yu Li, Yada Pruksachatkun et al.· 3 citations
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