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Orchestrating multiple agents on a complex task requires reconciling local subtask execution with global task planning. Dynamic dispatch approaches provide execution flexibility but do not explicitly model subtask hierarchies or dependencies, while predefined workflows impose this structure but fix the decomposition be...
Coding agents solve repository-level software engineering tasks through long trajectories of code inspection, search, editing, and testing. As a task progresses, earlier exploration becomes stale, so managing context is more than avoiding overflow: an agent must decide when to compact, what working state to preserve, a...
It is shown that under GRPO-style optimization, a global normalization baseline may deviate from diverse agents'reward distributions, which ultimately leads to gradient-norm instability, which theoretically pinpoints a key reason for training instability when extending group-based RL to multi-agent LLM systems.
JET is introduced, which evolves an executable judge on labeled source trajectories, then freezes and transfers it to guide target-side program evolution, andalyses identify useful reward-prediction logic in the evolved code and show that better final selection alone cannot explain the gains.
AgentBrew is proposed, an offline training framework that learns effective tool-use policies from a single batch of raw interaction trajectories, without task verifiers or iterative on-policy rollouts, and demonstrates that fine-grained offline learning can recover useful supervision from raw trajectories that filterin...
AppDeltaWorld is proposed, a transition-grounded delta code world model that predicts the next GUI as a reachable code update rather than as an unconstrained image or text description, and achieves the highest fidelity on CMGUIBench-500 under Code2World evaluation.
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