arXiv:2606.15884v1 Announce Type: new Abstract: We presented a neuron-level analysis of legal-domain reasoning in LLMs, comparing it with other applied domain tasks across seven open-weight models. Using neuron attribution scores to rank and suppress influential neurons, we confirmed that suppressing the identified neurons collapses accuracy on the target task, whereas suppressing the same number of random neurons does not. We further found a small subset of neurons influential across all seven tasks; once these are removed, suppressing the remaining neurons degrades only the task they were id
This research provides a neuron-level analysis of LLMs in legal reasoning, indicating a growing understanding of how these complex models function in specialized domains.
Understanding the granular mechanisms of LLM reasoning, especially in critical fields like law, is vital for developing more reliable, controllable, and robust AI systems.
The ability to identify and manipulate specific neurons responsible for task performance offers a new avenue for AI development, debugging, and potentially, fine-grained control over LLM behavior.
- · AI researchers
- · Legal tech companies
- · LLM developers
- · Regulatory bodies
- · Companies relying on black-box LLM applications
- · Developers unable to perform deep model analysis
Improved accuracy and reliability of domain-specific LLMs through targeted neuron suppression or enhancement.
The development of 'explainable AI' tools that can pinpoint and interpret the specific internal components driving an LLM's decisions.
The creation of 'AI rights' or 'AI verification' frameworks based on the ability to audit and understand an AI's internal reasoning processes.
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