ANNEAL: Adapting LLM Agents via Governed Symbolic Patch Learning
The paper introduces ANNEAL, a neuro-symbolic agent designed to address recurring failures in LLM-based agents. It employs a mechanism called Failure-Driven Knowledge Acquisition (FDKA) to repair symbolic structures without altering model weights. The results indicate that ANNEAL significantly reduces failure rates compared to existing systems.
- ▪ANNEAL converts recurring failures into governed symbolic edits of a process knowledge graph.
- ▪The core mechanism, FDKA, localizes the responsible operator and synthesizes a typed patch.
- ▪ANNEAL achieves a 0% failure rate in tested recurring-failure settings, outperforming strong baselines.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.16309 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
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Computer Science > Artificial Intelligence arXiv:2605.16309 (cs) [Submitted on 4 May 2026] Title:ANNEAL: Adapting LLM Agents via Governed Symbolic Patch Learning Authors:Safayat Bin Hakim, Keyan Guo, Wenkai Tan, Alvaro Velasquez, Shouhuai Xu, Houbing Herbert Song View a PDF of the paper titled ANNEAL: Adapting LLM Agents via Governed Symbolic Patch Learning, by Safayat Bin Hakim and 5 other authors View PDF HTML (experimental) Abstract:LLM-based agents can recover from individual execution errors, yet they repeatedly fail on the same fault when the underlying process knowledge--operator schemas, preconditions, and constraints--remains unrepaired.
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