arXiv:2512.10903v2 Announce Type: replace Abstract: Circuit discovery aims to identify minimal subnetworks that are responsible for specific behaviors in large language models (LLMs). Existing approaches primarily rely on iterative edge pruning, which is computationally expensive and limited to coarse-grained units such as attention heads or MLP blocks, overlooking finer structures like individual neurons. We propose a node-level pruning framework for circuit discovery that addresses both scalability and granularity limitations. Our method introduces learnable masks across multiple levels of g
The increasing scale and complexity of LLMs necessitate more efficient and granular methods for understanding and improving their internal workings.
This development could significantly accelerate the discovery and optimization of critical circuits within LLMs, making their development more interpretable and controllable.
Circuit discovery can now move beyond coarse-grained units to address individual neurons, offering a more precise understanding of LLM behavior and potential for targeted intervention.
- · AI researchers
- · LLM developers
- · AI safety researchers
- · Developers reliant on black-box LLM optimization
More efficient and granular identification of functional components within large language models.
Improved ability to debug, control, and ensure the safety of increasingly powerful AI systems.
Accelerated development of AI agents and specialized LLMs with highly optimized and transparent functionalities.
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Read at arXiv cs.AI