@inproceedings{zeng-etal-2026-depth,
title = "Depth-Attention: Cross-Layer Value Mixing for Language Models",
author = "Zeng, Boyi and
Hao, Yiqin and
Wang, Zitong and
Song, Shixiang and
Li, He and
Song, Feichen and
Liu, Yifan and
He, Ziwei and
Wang, Xinbing and
Lin, Zhouhan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2085/",
pages = "47493--47514",
ISBN = "979-8-89176-459-0",
abstract = "Self-attention selects information freely across the sequence, but across depth Transformers merely add each layer{'}s output to the residual stream, so later layers cannot selectively reuse earlier-layer representations. Recent cross-layer methods improve this flow but operate on hidden states outside attention, adding state beyond the key-value cache at inference{---}a cost that becomes increasingly salient as modern LLMs compress the cache with grouped-query and multi-head latent attention. We introduce Depth-Attention, which performs this selection inside the attention module itself: before a layer attends over the sequence, its query attends along depth over the keys of earlier layers at the same position and mixes their values into the value that self-attention then reads. Because Depth-Attention reuses the standard attention queries, keys, and value-cache slots, storing depth-mixed values in place of the original values, it adds no parameters and introduces no persistent inference state beyond the standard key-value cache{---}the same cache size as a vanilla decoder and less than hidden-state-based cross-layer methods. On Qwen3-style decoders at 1.5B and 3B parameters, Depth-Attention attains the lowest perplexity and the highest average downstream accuracy, improving over the vanilla Transformer by up to 2.3 accuracy points and surpassing strong cross-layer baselines (mHC, Attention Residuals, DenseFormer) in perplexity and average accuracy, while adding under 0.01{\%} extra arithmetic FLOPs and no additional persistent inference state. The gains hold from 360M to 3B parameters and extend to looped Transformers."
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<abstract>Self-attention selects information freely across the sequence, but across depth Transformers merely add each layer’s output to the residual stream, so later layers cannot selectively reuse earlier-layer representations. Recent cross-layer methods improve this flow but operate on hidden states outside attention, adding state beyond the key-value cache at inference—a cost that becomes increasingly salient as modern LLMs compress the cache with grouped-query and multi-head latent attention. We introduce Depth-Attention, which performs this selection inside the attention module itself: before a layer attends over the sequence, its query attends along depth over the keys of earlier layers at the same position and mixes their values into the value that self-attention then reads. Because Depth-Attention reuses the standard attention queries, keys, and value-cache slots, storing depth-mixed values in place of the original values, it adds no parameters and introduces no persistent inference state beyond the standard key-value cache—the same cache size as a vanilla decoder and less than hidden-state-based cross-layer methods. On Qwen3-style decoders at 1.5B and 3B parameters, Depth-Attention attains the lowest perplexity and the highest average downstream accuracy, improving over the vanilla Transformer by up to 2.3 accuracy points and surpassing strong cross-layer baselines (mHC, Attention Residuals, DenseFormer) in perplexity and average accuracy, while adding under 0.01% extra arithmetic FLOPs and no additional persistent inference state. The gains hold from 360M to 3B parameters and extend to looped Transformers.</abstract>
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%0 Conference Proceedings
%T Depth-Attention: Cross-Layer Value Mixing for Language Models
%A Zeng, Boyi
%A Hao, Yiqin
%A Wang, Zitong
%A Song, Shixiang
%A Li, He
%A Song, Feichen
%A Liu, Yifan
%A He, Ziwei
%A Wang, Xinbing
%A Lin, Zhouhan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zeng-etal-2026-depth
%X Self-attention selects information freely across the sequence, but across depth Transformers merely add each layer’s output to the residual stream, so later layers cannot selectively reuse earlier-layer representations. Recent cross-layer methods improve this flow but operate on hidden states outside attention, adding state beyond the key-value cache at inference—a cost that becomes increasingly salient as modern LLMs compress the cache with grouped-query and multi-head latent attention. We introduce Depth-Attention, which performs this selection inside the attention module itself: before a layer attends over the sequence, its query attends along depth over the keys of earlier layers at the same position and mixes their values into the value that self-attention then reads. Because Depth-Attention reuses the standard attention queries, keys, and value-cache slots, storing depth-mixed values in place of the original values, it adds no parameters and introduces no persistent inference state beyond the standard key-value cache—the same cache size as a vanilla decoder and less than hidden-state-based cross-layer methods. On Qwen3-style decoders at 1.5B and 3B parameters, Depth-Attention attains the lowest perplexity and the highest average downstream accuracy, improving over the vanilla Transformer by up to 2.3 accuracy points and surpassing strong cross-layer baselines (mHC, Attention Residuals, DenseFormer) in perplexity and average accuracy, while adding under 0.01% extra arithmetic FLOPs and no additional persistent inference state. The gains hold from 360M to 3B parameters and extend to looped Transformers.
%U https://aclanthology.org/2026.emnlp-main.2085/
%P 47493-47514
Markdown (Informal)
[Depth-Attention: Cross-Layer Value Mixing for Language Models](https://aclanthology.org/2026.emnlp-main.2085/) (Zeng et al., EMNLP 2026)
ACL
- Boyi Zeng, Yiqin Hao, Zitong Wang, Shixiang Song, He Li, Feichen Song, Yifan Liu, Ziwei He, Xinbing Wang, and Zhouhan Lin. 2026. Depth-Attention: Cross-Layer Value Mixing for Language Models. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 47493–47514, Budapest, Hungary. Association for Computational Linguistics.