RIMRULE: Neuro-Symbolic Rule Injection Improves LLM Tool Use

RIMRULE presents a promising approach to enhance LLM tool usage by dynamically injecting rules derived from failure traces. The use of MDL for rule consolidation and the portability of learned rules across different LLMs are particularly noteworthy. Further research should focus on scalability and robustness in more complex, real-world scenarios.

• •RIMRULE uses neuro-symbolic approach for LLM adaptation.
• •Rules are distilled from failure traces and injected into prompts.
• •Learned rules are portable across different LLM architectures.