CausalGate: Causal Importance Distillation for Transformer Module Pruning

• URL: http://arxiv.org/abs/2607.22720v2
• Date: Tue, 28 Jul 2026 20:01:05 GMT
• Title: CausalGate: Causal Importance Distillation for Transformer Module Pruning
• Abstract summary: CausalGate is an intervention-guided framework for compute-efficient runtime inference.<n>It measures semantic damage via the Kullback-Leibler divergence of the final logit distribution.<n>It consistently outperforms prominent dynamic routing and layer-skipping baselines.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing adaptive inference methods for Large Language Models rely on observational heuristics, such as hidden-state similarity or activation magnitudes, to drop redundant modules. However, these correlation-based metrics often fail to capture subtle, non-linear structural computations vital for semantic accuracy. We introduce CausalGate, an intervention-guided framework for compute-efficient transformer inference. During a calibration phase, CausalGate isolates individual Attention and MLP sub-layers, zeros out their respective outputs, and measures the exact semantic damage via the Kullback-Leibler divergence of the final logit distribution. To eliminate runtime routing overhead, this structural importance hierarchy is distilled into a global set of static, lightweight scalar gates using an Exponential Moving Average smoothing objective paired with a differentiable pairwise ranking loss. Evaluated on TinyLlama-1.1B, Qwen2.5-3B, and Llama-3.1-8B across language modeling and commonsense reasoning benchmarks, CausalGate consistently outperforms prominent dynamic routing and layer-skipping baselines, translating theoretical compute savings into concrete hardware latency reductions with zero operational overhead.

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