Bayesian-Agent: Posterior-Guided Skill Evolution Across LLM Agent Harnesses
LLM agents increasingly rely on prompts, tools, memory, SOPs, skills, and harness feedback, yet current self-evolution pipelines often update these assets through heuristic reflection or raw success counts. Such updates are brittle when trajectories are sparse, expensive, and context-dependent. We introduce Bayesian-Agent, a native and cross-harness framework that treats reusable agent skills as Bayesian evidence objects. Bayesian-Agent records verified trajectories, maintains posterior beliefs over skill reliability and failure modes, and turns those beliefs into auditable skill actions and model-facing guardrails. This posterior view provides a finite-sample alternative to raw empirical...
| Authors | Xiaojun Wu, Cehao Yang, Honghao Liu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 6 June 2026 |
| Last updated at source | 11 August 2026 |
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