arXiv AI
Verify--Backtrack Loop: Structured Precondition Recovery for Long-Horizon Language Agents
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Travel Back To Move Forward: Retrospective Exploration and Evolving for Long Horizon Agents
The paper introduces ReTRAIL, a framework that allows long‑horizon agents to rewind the environment to earlier states while preserving forward progress in knowledge. By training a shared policy with State‑Anchored GRPO and a branch‑advancement reward, ReTRAIL enables agents to recover from unproductive states and reuse insights from abandoned exploration. Experiments on MineSweeper, Sokoban, ALFWorld, and ScienceWorld show that this combined rewinding and experience‑reuse strategy outperforms simple rewind primitives and improves long‑horizon task solving.
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arXiv AI
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StructAgent: Harness Long-horizon Digital Agents with Unified Causal Structure
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arXiv AI
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From State to Action: OODA-Tool for Reliable Multi-Turn Tool Use
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arXiv Machine Learning
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Success Leaves Detours: Learning Executable Walkthroughs for Long-Horizon Agents
The paper introduces Trace, a framework that transforms sparse-reward trajectories into executable walkthroughs by identifying progress anchors, propagating credit, and estimating action prerequisites. Trace compiles noisy trajectories into state‑conditioned, verifiable procedures that remove loops and detours, enabling reuse, intermediate‑state resumption, and programmatic verification. Experiments on J‑TTL, WebShop, and ScienceWorld with three open‑source LLMs show that Trace outperforms eight baselines, improving average AUC and Final‑$3$ by 30.0% and 40.5% while using fewer inference tokens.