OpenAI新研究提出Circuit Condensation，能将模型行为压缩316倍，让AI内部机制更透明可解释。
研究人员提出Circuit Condensation方法，通过后训练将模型行为压缩到更小的因果图中。该方法在4种行为和8个模型上测试，平均压缩比例为8.1倍，最高达316倍。研究发现权重更新而非搜索本身是电路规模减少的关键因素。该方法能识别出24个heads，其中17个有已记录角色，相比原始电路的61个heads和36个未记录角色显著减少。
Circuit Condensation: Post-Training that Concentrates a Behavior's Causal Circuit
One approach to mechanistic interpretability explains behavior through circuits: the components and connections that carry it. Frozen discovery often returns hundreds of edges, making them hard to inspect, compare, or verify exhaustively. We introduce Circuit Condensation, which post-trains models to concentrate behaviors into smaller causal graphs. Each round prunes low-attribution edges and trains a low-rank adapter to match the original through what remains, retaining the cut only if task performance and general capability survive. Across four behaviors and eight models, condensed circuits are smaller than the strongest frozen baseline in 30 of 32 settings, by $8.1\times$ on average and up to $316\times$. Repeating the search without weight updates produces larger circuits in 29 of 32 settings, showing that weight updates, rather than search alone, drive the reduction. Testing every subset of 19 circuits finds 11 that cannot be reduced and reveals removable edges in the rest. Pair ablations expose dependencies between edges, showing that their effects cannot be understood independently. On indirect object identification, condensation isolates 24 heads, 17 of them with documented roles, against 61 heads and 36 undocumented ones for the matched frozen circuit: a sufficient sub-circuit of the published mechanism rather than a reconstruction of it. The resulting circuit tracks the original model's next-token distribution and predicts its errors.