arXiv:2603.20510v2 Announce Type: replace Abstract: Language models often lack grounded reasoning capabilities in specialized domains where training data is scarce but bespoke systems excel. We introduce a general framework for distilling expert system reasoning into natural language chain-of-thought explanations, enabling compact models to acquire domain expertise and the ability to generate faithful, grounded explanations. Rather than distilling only final outputs, we capture the full reasoning process, transforming opaque expert computations into transparent, step-by-step explanations. We d
The paper presents a framework for improving language models' grounded reasoning in specialized domains, addressing current limitations in complex logical tasks and a scarcity of training data for such domains.
This development could significantly enhance the capabilities of AI agents and specialized AI systems by enabling them to acquire and explain expert knowledge, moving beyond pattern matching to genuine understanding.
Language models can now transparently explain their reasoning in complex, expert domains, providing auditable and reliable AI decision-making instead of opaque 'black box' operations.
- · AI agents developers
- · Specialized AI system providers
- · Industries requiring grounded AI reasoning (e.g., legal, medical, engineering)
- · Small-to-medium sized AI firms
- · Companies relying on opaque AI systems
- · Competitors without similar distillation techniques
Compact language models gain the ability to perform expert-level reasoning and generate transparent explanations for their decisions.
This capability accelerates the deployment of AI in highly regulated and specialized domains that demand verifiable and grounded reasoning, such as finance or healthcare.
The democratization of expert-level AI reasoning via distillation could lead to the proliferation of highly specialized AI agents in niche sectors, transforming white-collar work previously considered immune to automation.
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Read at arXiv cs.AI