CIRCUS：通过稳定性集合在不确定性下实现电路共识
CIRCUS: Circuit Consensus under Uncertainty via Stability Ensembles
AI总结：
CIRCUS通过不确定性框架重构电路发现，通过稳定性集合提取鲁棒结构，使电路在保持解释力的同时显著减小规模，优于基线方法。
摘要：
CIRCUS通过在不确定性框架下重构电路发现，通过稳定性集合提取鲁棒结构，使电路在保持解释力的同时显著减小规模，优于基线方法。
英文摘要：
Every mechanistic circuit carries an invisible asterisk: it reflects not just the model's computation, but the analyst's choice of pruning threshold. Change that choice and the circuit changes, yet current practice treats a single pruned subgraph as ground truth with no way to distinguish robust structure from threshold artifacts. We introduce CIRCUS, which reframes circuit discovery as a problem of uncertainty over explanations. CIRCUS prunes one attribution graph under B configurations, assigns each edge an empirical inclusion frequency s(e) in [0,1] measuring how robustly it survives across the configuration family, and extracts a consensus circuit of edges present in every view. This yields a principled core/contingent/noise decomposition (analogous to posterior model-inclusion indicators in Bayesian variable selection) that separates robust structure from threshold-sensitive artifacts, with negligible overhead. On Gemma-2-2B and Llama-3.2-1B, consensus circuits are 40x smaller than the union of all configurations while retaining comparable influence-flow explanatory power, consistently outperform influence-ranked and random baselines, and are confirmed causally relevant by activation patching.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
