ANNEAL: Neuro-Symbolic Agent Patches Process Knowledge Without Model Weight Changes
A team of researchers has unveiled ANNEAL, a neuro-symbolic agent designed to rectify persistent issues in LLM-based agents by modifying a process knowledge graph, all while keeping the foundation model weights intact. Central to its operation is Failure-Driven Knowledge Acquisition (FDKA), which identifies malfunctioning operators, generates typed patches through constrained LLM generation, and assesses suggestions using multi-dimensional scoring, symbolic guardrails, and canary testing prior to implementation. Each approved modification includes governance assurances for secure deployment. This innovative method fills the void left by current self-evolving techniques that only adjust prompts, memory, or weights, neglecting the repair of symbolic task execution frameworks.
Key facts
- ANNEAL converts recurring failures into governed symbolic edits of a process knowledge graph.
- Does not modify foundation model weights.
- FDKA mechanism localizes responsible operator, synthesizes typed patch, and validates via scoring, guardrails, and canary testing.
- Existing self-evolving approaches update prompts, memory, or weights but not symbolic structures.
- Provides governance guarantees for safe deployment.
- Introduced in arXiv paper 2605.16309.
- Neuro-symbolic approach combines LLM generation with symbolic guardrails.
- Patches are validated before commit.
Entities
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