arXiv Artificial Intelligence

Multi-Granular Node Pruning for Causal Circuit Discovery

2 hours ago 11.08.2026 07:00 👁 2 views
Multi-Granular Node Pruning for Causal Circuit Discovery

Quick summary

arXiv:2512.10903v3 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

Key takeaways

  • arXiv:2512.10903v3 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.

Why it matters

The importance of “Multi-Granular Node Pruning for Causal Circuit Discovery” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗