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Self-Meta-Evolve is proposed, a hierarchical framework that maintains a dedicated prompt for each user and continuously refines it through a dual-loop process: an inner loop that edits structured prompts based on persona-conditioned feedback, and an outer loop that evolves the meta-prompt itself by distilling successfu...
Experiments show that BehR-based training improves long-term alignment in several settings, with the clearest gains in WebShop and less movement in near-ceiling regimes, while preserving or improving single-step prediction quality in three of four settings.
This work introduces WebXSkill, a framework that bridges a grounding gap with executable skills, each pairing a parameterized action program with step-level natural-language guidance, and finds that better skill deployment mode depends on a model's plan and execution capability.
CoHarden is proposed, a co-generation framework that uses the Lax signal as an in-loop convergence criterion that generates a test before any fix, then iteratively hardens the test and fix against surviving mutation patches until the generated test no longer admits Lax regressions.
The proposed AutoSaddler, an automatic harness optimization framework that formulates harness improvement as an offline learning problem and iteratively updates the harness using failure signals from mini-batches, suggests that automatic harness optimization is a promising path toward more performant and reliable agent...
This work introduces ToolAtlas, a graph-based framework that builds a persistent provider-side tool memory of tool capabilities, failure boundaries, and cross-tool compositions through execution-verified probing and establishes provider-side tool memory as an effective and reusable paradigm for tool servers.
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