NLL-Guided Full-Attention Layer Selection for Training-Free Sliding-Window Adaptation
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Computer Science > Computation and Language
arXiv:2606.27791 (cs)
[Submitted on 26 Jun 2026]
Title:NLL-Guided Full-Attention Layer Selection for Training-Free Sliding-Window Adaptation
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Abstract:Hybrid attention models that mix full and sliding-window attention across layers offer a promising approach to efficient long-context inference, but the critical question of \emph{which layers} should retain full attention remains unsolved. Existing methods use either fixed periodic patterns or attention-based heuristics that may not capture what matters for downstream accuracy. We propose NLL-guided layer selection, a training-free method that directly measures each layer's importance by computing the negative log-likelihood degradation on answer tokens when that layer uses sliding-window instead of full attention. On LongMemEval with Qwen3-4B, our method achieves 64.6\% accuracy using only 1/4 full-attention layers, matching the 1/2-FA periodic baseline (65.0\%) while halving the computational budget. NLL-guided selection outperforms the SWAA-reported periodic 1/4-FA baseline by 10.4 percentage points and a matched LightTransfer-style baseline by 26.4 percentage points. De-confounding analysis shows the signal is consistent with long-range attention needs rather than generic layer sensitivity. The method requires only $\sim$15 minutes of one-time calibration, advancing the efficiency-accuracy Pareto frontier for long-context LLM deployment.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.27791 [cs.CL] |
| (or arXiv:2606.27791v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.27791
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled NLL-Guided Full-Attention Layer Selection for Training-Free Sliding-Window Adaptation, by Qiong Tang and 4 other authors
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