arXiv:2601.00086v3 Announce Type: replace Abstract: Large language models (LLMs) often struggle to use tools reliably in domain-specific settings, where APIs may be idiosyncratic, under-documented, or tailored to private workflows. This highlights the need for effective adaptation to task-specific tools. We propose RIMRULE, a neuro-symbolic approach for LLM adaptation based on dynamic rule injection. Compact, interpretable rules are distilled from failure traces and injected into the prompt during inference to improve task performance. These rules are proposed by the LLM itself and consolidate
The development of RIMRULE comes at a time when enterprise adoption of LLMs is accelerating, but their reliability in complex, domain-specific tasks remains a critical bottleneck for wider deployment.
This development offers a practical method to enhance the reliability and adaptability of large language models in specialized applications, significantly improving their practical utility for businesses and automating complex workflows.
LLMs can now be more effectively 'taught' to perform specific tasks, even with imperfect APIs, by dynamically learning and injecting rules, leading to more robust and less error-prone agentic systems.
- · AI software developers
- · Enterprises adopting AI agents
- · LLM developers
- · SaaS providers
- · Manual workflow operators
- · Companies with inefficient specialized APIs
Improved reliability of autonomous AI agents in domain-specific tasks.
Accelerated automation of complex white-collar workflows within organizations currently constrained by LLM reliability.
Enhanced competition among AI agent platforms, driving innovation in dynamic adaptation and learning capabilities.
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Read at arXiv cs.CL