pith. sign in

arxiv: 2606.05014 · v1 · pith:5N6NBPYHnew · submitted 2026-06-03 · 💻 cs.CL

Depth-Attention: Cross-Layer Value Mixing for Language Models

Boyi Zeng , Yiqin Hao , Zitong Wang , Shixiang Song , He Li , Feichen Song , Yifan Liu , Ziwei He
show 2 more authors
Xinbing Wang Zhouhan Lin
This is my paper

Pith reviewed 2026-06-28 05:49 UTC · model grok-4.3

classification 💻 cs.CL
keywords depth-attentioncross-layer mixingtransformer language modelskey-value cachevalue mixingattention mechanisminference efficiency
0
0 comments X

The pith

Depth-Attention mixes values from earlier layers inside the attention module by reusing the existing key-value cache, improving perplexity and accuracy with no added parameters or state.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Standard Transformers pass information across layers only by adding outputs to the residual stream, offering no selective reuse of earlier representations. Depth-Attention changes this by having each layer's query attend over keys from prior layers at the same token position and mix their values into the value used for self-attention. The mixing reuses the standard attention queries, keys, and cache slots so that depth-mixed values simply replace the originals in place. On Qwen3-style decoders from 360M to 3B parameters, this yields lower perplexity and higher average downstream accuracy than both the vanilla model and other cross-layer approaches, while adding under 0.01 percent extra FLOPs and no extra persistent state. The gains also appear in looped Transformers.

Core claim

Depth-Attention performs cross-layer selection inside attention: before a layer attends over the sequence, its query attends over the keys of earlier layers at the same token position and mixes their values into the value that self-attention then reads, storing the depth-mixed values in the standard key-value cache slots.

What carries the argument

Cross-layer key attention at fixed token positions that mixes earlier-layer values into the current value vector before sequence self-attention.

If this is right

  • Depth-Attention reaches the lowest perplexity among compared methods on the tested decoder scales.
  • Average downstream accuracy rises by up to 2.3 points over the vanilla Transformer.
  • The approach adds under 0.01 percent extra arithmetic FLOPs and zero additional persistent inference state.
  • Improvements hold from 360M to 3B parameters and extend to looped Transformers.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same cache-reuse pattern could be tested with grouped-query or multi-head latent attention to keep memory savings intact.
  • Better selective reuse across depth might allow comparable performance with fewer total layers in some settings.
  • The mixing step could be applied in encoder-only or encoder-decoder architectures to test whether the benefit generalizes beyond decoder-only language models.

Load-bearing premise

That depth-mixed values can replace the original values in the cache without disrupting training dynamics or requiring changes to the residual connections.

What would settle it

Training the same 1.5B and 3B Qwen3-style models with Depth-Attention and measuring no reduction in perplexity or gain in average downstream accuracy compared with the vanilla baseline would falsify the performance claim.

read the original 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 over the keys of earlier layers at the same token 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 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces Depth-Attention, an architectural change that performs cross-layer value mixing inside the attention module: the current layer's query attends exclusively over the keys of prior layers at the identical token position, and the resulting mixed value overwrites the layer's value slot in the KV cache for use by standard self-attention. The method reuses existing Q/K/V machinery with no added parameters or persistent inference state beyond the standard cache. On Qwen3-style decoders (360M–3B parameters) it reports the lowest perplexity and highest average downstream accuracy, with gains of up to 2.3 accuracy points over vanilla Transformers and superiority over other cross-layer baselines, at <0.01% extra arithmetic FLOPs; the gains are also stated to extend to looped Transformers.

Significance. If the reported empirical gains and efficiency properties hold under scrutiny, the work would be significant for its demonstration that selective depth-wise mixing can be realized with zero extra persistent state or parameters—an advantage that grows in importance for models that already compress the KV cache via GQA or MLA. The explicit reuse of the existing attention cache slots and the negligible depth-wise cost (depth ≪ sequence length) are cleanly argued strengths. The consistent scaling behavior from 360M to 3B and the looped-Transformer extension, if reproducible, would strengthen the case for the approach.

major comments (2)
  1. [Abstract, §4] Abstract and §4 (Experiments): the central empirical claim of superiority (lowest perplexity, +2.3 accuracy points, outperformance of strong baselines) is presented without error bars, number of random seeds, or statistical significance tests. This directly affects the load-bearing assertion that Depth-Attention is reliably better than the listed baselines.
  2. [§3, §4.1] §3 (Method) and §4.1 (Training details): the description states that depth-mixed values replace original values “in place” with no additional persistent state, yet the paper does not provide an explicit ablation or measurement confirming that the extra depth-wise attention (over ~24–32 layers) does not alter KV-cache eviction behavior or numerical stability under realistic batching and quantization.
minor comments (2)
  1. [§3] Notation for the depth-attention operation (presumably Eq. (X) in §3) should be introduced with an explicit small example showing the shape of the depth-wise key matrix and how the mixed value is written back into the cache slot.
  2. [§4.1] The abstract refers to “Qwen3-style decoders” without clarifying whether any other modifications (e.g., RoPE scaling, GQA grouping) differ from the public Qwen2/Qwen3 checkpoints; this should be stated once in §4.1.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on the empirical evaluation and implementation details. We agree that additional statistical rigor and verification ablations would improve the manuscript and will revise accordingly.

read point-by-point responses

  1. Referee: [Abstract, §4] Abstract and §4 (Experiments): the central empirical claim of superiority (lowest perplexity, +2.3 accuracy points, outperformance of strong baselines) is presented without error bars, number of random seeds, or statistical significance tests. This directly affects the load-bearing assertion that Depth-Attention is reliably better than the listed baselines.

    Authors: We acknowledge this limitation in the current manuscript. In the revised version, we will rerun experiments with multiple random seeds, report means and standard deviations for key metrics, and include statistical significance tests (e.g., t-tests) comparing Depth-Attention to baselines to substantiate the superiority claims. revision: yes

  2. Referee: [§3, §4.1] §3 (Method) and §4.1 (Training details): the description states that depth-mixed values replace original values “in place” with no additional persistent state, yet the paper does not provide an explicit ablation or measurement confirming that the extra depth-wise attention (over ~24–32 layers) does not alter KV-cache eviction behavior or numerical stability under realistic batching and quantization.

    Authors: The depth attention is computed per position over a fixed small set of prior layers and overwrites the value in the standard KV cache without increasing its size, so eviction behavior (which is sequence-length based) remains unchanged. That said, we did not include explicit measurements for quantization or batching effects. We will add such an ablation in the revision, evaluating perplexity and accuracy under different quantization schemes and batch configurations to confirm stability. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper introduces an architectural change (Depth-Attention) that reuses existing Q/K/V attention machinery to mix values across layers at the same token position, then reports empirical results on perplexity and downstream tasks for models from 360M to 3B parameters. All central claims are experimental comparisons against vanilla Transformers and other cross-layer baselines; no derivation chain, first-principles prediction, or fitted parameter is presented whose output is definitionally identical to its input. The efficiency statements (reuse of cache slots, <0.01% extra FLOPs, no added persistent state) follow directly from the stated implementation without circular reduction. No self-citation load-bearing steps or ansatz smuggling appear in the abstract or described mechanism.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper introduces a new architectural operation but does not introduce fitted free parameters, new physical entities, or non-standard mathematical axioms beyond the usual transformer attention assumptions.

axioms (1)
  • standard math Standard multi-head attention computation and residual stream addition are available as building blocks.
    The method is defined by extending the existing attention module rather than replacing it.

pith-pipeline@v0.9.1-grok · 5828 in / 1422 out tokens · 30645 ms · 2026-06-28T05:49:55.597867+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

23 extracted references · 16 canonical work pages · 11 internal anchors

  1. [1]

    InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 4895–4901

    Gqa: Training generalized multi-query transformer models from multi-head checkpoints. InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 4895–4901. Thomas Bachlechner, Bodhisattwa Prasad Majumder, Henry Mao, Gary Cottrell, and Julian McAuley

    2023

  2. [2]

    Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

    Think you have solved question answering? try arc, the ai2 reasoning challenge.arXiv preprint arXiv:1803.05457. Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, and 1 others

    work page internal anchor Pith review Pith/arXiv arXiv

  3. [3]

    The Pile: An 800GB Dataset of Diverse Text for Language Modeling

    The pile: An 800gb dataset of diverse text for language modeling.arXiv preprint arXiv:2101.00027. Angeliki Giannou, Shashank Rajput, Jy-yong Sohn, Kangwook Lee, Jason D Lee, and Dimitris Papail- iopoulos

    work page internal anchor Pith review Pith/arXiv arXiv

  4. [4]

    InFindings of the association for computational linguistics: ACL-IJCNLP 2021, pages 929–943

    Realformer: Transformer likes residual attention. InFindings of the association for computational linguistics: ACL-IJCNLP 2021, pages 929–943. Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger

    2021

  5. [5]

    Scaling Laws for Neural Language Models

    Scaling laws for neural language models. arXiv preprint arXiv:2001.08361. Jonas Knupp, Jan Hendrik Metzen, Jeremias Bohn, Georg Groh, and Kristian Kersting

    work page internal anchor Pith review Pith/arXiv arXiv 2001

  6. [6]

    Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy

    Depth- recurrent attention mixtures: Giving latent reasoning the attention it deserves.arXiv preprint arXiv:2601.21582. Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy

    work page arXiv

  7. [7]

    RACE: Large-scale ReAding Comprehension Dataset From Examinations

    Race: Large-scale reading comprehension dataset from examinations.arXiv preprint arXiv:1704.04683. Aixin Liu, Bei Feng, Bin Wang, Bingxuan Wang, Bo Liu, Chenggang Zhao, Chengqi Dengr, Chong Ruan, Damai Dai, Daya Guo, and 1 others

    work page internal anchor Pith review Pith/arXiv arXiv

  8. [8]

    DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

    Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model.arXiv preprint arXiv:2405.04434. Gaurav Menghani, Ravi Kumar, and Sanjiv Kumar

    work page internal anchor Pith review Pith/arXiv arXiv

  9. [9]

    arXiv preprint arXiv:2411.07501

    Laurel: Learned augmented residual layer. arXiv preprint arXiv:2411.07501. Matteo Pagliardini, Amirkeivan Mohtashami, Francois Fleuret, and Martin Jaggi

    work page arXiv

  10. [10]

    Deep contextualized word representations

    Deep contextualized word representations. arxiv 2018.arXiv preprint arXiv:1802.05365,

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  11. [11]

    Attention Residuals

    Attention residuals.arXiv preprint arXiv:2603.15031. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin

    work page internal anchor Pith review Pith/arXiv arXiv

  12. [12]

    Crowdsourcing Multiple Choice Science Questions

    Crowdsourcing multiple choice science questions.arXiv preprint arXiv:1707.06209. Da Xiao, Qingye Meng, Shengping Li, and Xingyuan Yuan

    work page internal anchor Pith review Pith/arXiv arXiv

  13. [13]

    Shufang Xie, Huishuai Zhang, Junliang Guo, Xu Tan, Jiang Bian, Hany Hassan Awadalla, Arul Menezes, Tao Qin, and Rui Yan

    Muddformer: Breaking residual bot- tlenecks in transformers via multiway dynamic dense connections.arXiv preprint arXiv:2502.12170. Shufang Xie, Huishuai Zhang, Junliang Guo, Xu Tan, Jiang Bian, Hany Hassan Awadalla, Arul Menezes, Tao Qin, and Rui Yan

    work page arXiv

  14. [14]

    Zhenda Xie, Yixuan Wei, Huanqi Cao, Chenggang Zhao, Chengqi Deng, Jiashi Li, Damai Dai, Huazuo Gao, Jiang Chang, Kuai Yu, and 1 others

    Residual: Transformer with dual residual connections.arXiv preprint arXiv:2304.14802. Zhenda Xie, Yixuan Wei, Huanqi Cao, Chenggang Zhao, Chengqi Deng, Jiashi Li, Damai Dai, Huazuo Gao, Jiang Chang, Kuai Yu, and 1 others

    work page arXiv

  15. [15]

    mHC: Manifold-Constrained Hyper-Connections

    mhc: Manifold-constrained hyper-connections. arXiv preprint arXiv:2512.24880. Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, and Tieyan Liu

    work page internal anchor Pith review Pith/arXiv arXiv

  16. [16]

    Qwen3 Technical Report

    Qwen3 technical report.arXiv preprint arXiv:2505.09388. Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi

    work page internal anchor Pith review Pith/arXiv arXiv

  17. [17]

    HellaSwag: Can a Machine Really Finish Your Sentence?

    Hellaswag: Can a machine really finish your sentence?arXiv preprint arXiv:1905.07830. Boyi Zeng, Shixiang Song, Siyuan Huang, Yixuan Wang, He Li, Ziwei He, Xinbing Wang, Zhouhan Lin, and 1 others

    work page internal anchor Pith review Pith/arXiv arXiv 1905

  18. [18]

    Zhanchao Zhou, Tianyi Wu, Zhiyun Jiang, Fares Obeid, and Zhenzhong Lan

    Ancre: Adaptive neural connection reassignment for efficient depth scaling.arXiv preprint arXiv:2602.09009. Zhanchao Zhou, Tianyi Wu, Zhiyun Jiang, Fares Obeid, and Zhenzhong Lan

    work page arXiv

  19. [19]

    InInternational Conference on Learning Representations, volume 2025, pages 97183–97219

    Hyper-connections. InInternational Conference on Learning Representations, volume 2025, pages 97183–97219. 13 Depth-Attention: Cross-Layer Value Mixing for Language Models A. Main Experiment Details For the main experiments in section 4, we first summarize the baseline-specific settings in Table

    2025

  20. [20]

    mHC denotes manifold hyperconnec- tion. We choose the baseline-specific hyperparameters by following the recommended or commonly used practical configurations in the corresponding papers, while keeping the base architecture, data, optimizer, training budget, sequence length, and precision fixed across methods. For Dense- Former Pagliardini et al. (2024), ...

    2024

  21. [21]

    All theoretical estimates report the additional cost beyond a vanilla Transformer decoder. B.1. Common Counting Assumptions We use the following notation. Let𝐿 be the number of Transformer layers,𝑑model be the hidden dimension, and𝑑kv be the total key-value dimension used by the grouped-query attention cache. For our 3B Qwen3-style configuration, we use 𝐿...

    2048

  22. [22]

    Depth-Attention For a target layerℓ, Depth-Attention mixes value states from a depth source setDℓ

    15 Depth-Attention: Cross-Layer Value Mixing for Language Models B.2. Depth-Attention For a target layerℓ, Depth-Attention mixes value states from a depth source setDℓ. Let 𝑀ℓ =|D ℓ | denote the number of depth sources at layerℓ. In our implementation, the depth source set is maintained with stride𝑠=24, giving the layer-wise asymptotic cost 𝑂((ℓ/𝑠+1)𝑑 kv)...

    2048

  23. [23]

    Configuration / Hyperparameter 360M 500M 710M Model configuration Architecture Qwen-style decoder Qwen-style decoder Qwen-style decoder Hidden size 960 1152 1280 Intermediate size 2540 3072 3072 Number of hidden layers 24 24 32 Number of attention heads 15 18 20 Number of key-value heads 15 18 20 GQA group size 1 1 1 Activation function SiLU SiLU SiLU Voc...

    2048