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arxiv: 2606.01790 · v1 · pith:7JREJF2Enew · submitted 2026-06-01 · 💻 cs.CV · cs.AI

STaR-KV: Spatio-Temporal Adaptive Re-weighting for KV Cache Compression in GUI Vision-Language Models

Yuhang Han , Wenzheng Yang , Yujie Chen , Xiangqi Jin , Yaojie Zhang , Siteng Huang , Linfeng Zhang This is my paper

Pith reviewed 2026-06-28 15:30 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords KV cache compressionGUI vision-language modelsspatio-temporal adaptationattention subspacesmemory efficiencytraining-free methodsagent interaction
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The pith

STaR-KV compresses KV caches in GUI vision-language models by adapting token scores to subspace specialization and temporal drifts instead of using fixed saliency maps.

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

The paper establishes that KV cache compression for GUI agents fails when it assumes one shared importance map for all tokens and a static top-B cutoff. Measurements show importance instead varies by attention subspace, moves between layers, and changes distribution shape during an interaction sequence. STaR-KV counters this with three training-free adjustments that re-weight tokens according to their spatial mutual information per subspace, apply a stability discount to repeated entries, and reshape the score distribution via an entropy-based temperature. The result is higher average accuracy on four GUI benchmarks at the same cache size, zero added computation during compression, and large peak-memory savings.

Core claim

STaR-KV is a training-free KV cache compression framework that calibrates token importance along three axes: subspace-aware scoring driven by online spatial mutual information, a temporal stability discount that suppresses redundant cache entries from persistently attended subspaces, and an entropy-derived temperature that adaptively reshapes the score distribution. It rests on the observation that spatial specialization occurs at the attention-subspace level and migrates across layers while score distributions drift in shape along trajectories, directly refuting the single shared saliency map and fixed top-B cutoff used by prior methods.

What carries the argument

The three-axis calibration of STaR-KV: subspace-aware scoring from spatial mutual information, temporal stability discount, and entropy-derived temperature for adaptive reshaping.

If this is right

  • STaR-KV reaches the highest average accuracy among compared KV compression methods at matched budgets on four GUI benchmarks.
  • Peak GPU memory drops by nearly 40 percent when the KV cache is held to a 20 percent budget.
  • Compression adds no FLOPs overhead and can even show a small reduction.
  • GUI agents can sustain longer interaction sequences inside the same hardware memory limits.

Where Pith is reading between the lines

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

  • The same subspace-level adaptation may improve compression in vision-language models outside GUI tasks if their attention also shows spatial specialization.
  • Score drift over time implies that any static compression rule will lose accuracy on long agent trajectories.
  • The temporal discount could be tested on other sequential models where cache entries remain attended across steps.

Load-bearing premise

The pilot measurements that show spatial specialization lives at the subspace level and that score distributions drift along trajectories are accurate.

What would settle it

On the four GUI benchmarks, disable the three adaptive components and check whether accuracy falls to or below the level of prior fixed-cutoff methods at identical KV budgets.

Figures

Figures reproduced from arXiv: 2606.01790 by Linfeng Zhang, Siteng Huang, Wenzheng Yang, Xiangqi Jin, Yaojie Zhang, Yuhang Han, Yujie Chen.

Figure 1
Figure 1. Figure 1: Pilot measurements on UI-TARS-1.5-7B. (a) Per-group MI with 2D screen coordinates; red arrows mark spatially dominant GQA groups migrating across layers. (b) Normalized attention entropy Hˆ over tra￾jectory steps; dark line: mean, shaded band: inter￾trajectory variance, thin traces: individual trajectories. attention is spatially localized rather than uni￾formly spread, i.e., it encodes layout-sensitive st… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of STaR-KV. Given a multimodal KV cache of text tokens and multi-frame GUI visual tokens (❶ Input), STaR-KV (i) builds a token-level base score s¯t and a GQA group score map S from pooled recent-query attention with an optional GUI spatial boost (❷ Base Scoring), and then (ii) refines this base score along three complementary axes (❸ Adaptive Modules): Online MI Profiling estimates a per-group 2D … view at source ↗
Figure 3
Figure 3. Figure 3: Ablation studies of STaR-KV on UI-TARS-1.5-7B (Qin et al., 2025) under different KV cache budgets. (a)–(b): entropy-based vs. confidence-based AEB on AgentNetBench and ScreenSpot-v2; (c)–(d): online (ours) vs. offline group prior on AgentNetBench (Wang et al., 2026) and ScreenSpot-Pro (Li et al., 2025). The horizontal axis is the cache budget (%) and the vertical axis is the corresponding accuracy (%). Our… view at source ↗
Figure 4
Figure 4. Figure 4: Ablation on subspace granularity for MI esti [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Hyperparameter sensitivity of STaR-KV on [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
read the original abstract

Vision-language-model-based graphical user interface (GUI) agents have shown broad automation capabilities, yet deployment is bottlenecked by a key-value (KV) cache that grows linearly with interaction steps. For instance, UI-TARS-1.5-7B consumes 76 GB of GPU memory on merely five screenshots, approaching the capacity of mainstream 80 GB accelerators. Existing KV compression methods share two structural assumptions: aggregating visual-token importance into a single shared saliency map, and applying a fixed top-B cutoff to the fused score distribution. Pilot measurements refute both: spatial specialization lives at the attention-subspace level and migrates across layers, while the score distribution drifts in shape along a trajectory. We propose STaR-KV (Spatio-Temporal Adaptive Re-weighting), a training-free KV cache compression framework that calibrates token importance along three axes: (i) subspace-aware scoring driven by online spatial mutual information; (ii) a temporal stability discount that suppresses redundant cache entries from persistently attended subspaces; and (iii) an entropy-derived temperature that adaptively reshapes the score distribution. Across four GUI benchmarks, STaR-KV achieves the strongest average accuracy among state-of-the-art KV compression methods (e.g., GUIKV, SnapKV) at matched budgets, with no compression-stage FLOPs overhead (-0.07%) and cutting peak GPU memory by nearly 40% at a 20% KV-cache budget. Code is available at https://github.com/kawhiiiileo/STaR-KV.

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 proposes STaR-KV, a training-free KV-cache compression method for GUI vision-language models. It motivates the approach via pilot measurements that refute two assumptions of prior work (single shared saliency map and fixed top-B cutoff), claiming instead that spatial specialization occurs at the attention-subspace level and migrates across layers while score distributions drift along trajectories. The method introduces subspace-aware scoring via online spatial mutual information, a temporal stability discount, and an entropy-derived temperature. On four GUI benchmarks it reports the highest average accuracy versus methods such as GUIKV and SnapKV at matched budgets, with -0.07% compression-stage FLOPs overhead and nearly 40% peak GPU memory reduction at a 20% KV-cache budget.

Significance. If the reported accuracy, memory, and overhead numbers hold under full experimental scrutiny, the work would be significant for practical deployment of long-horizon GUI agents on memory-constrained hardware. The training-free design, explicit handling of spatio-temporal structure, and public code release are additional strengths that could facilitate adoption and follow-on research.

major comments (2)
  1. [Abstract] The pilot measurements that refute the two structural assumptions (shared saliency map and fixed top-B cutoff) are load-bearing for the claimed novelty of the three adaptive components; the manuscript must supply quantitative results, layer-wise statistics, and the exact measurement protocol for these pilots (currently only summarized in the abstract) so that readers can verify the claimed subspace-level specialization and score-distribution drift.
  2. The headline performance claim (strongest average accuracy at matched budgets) is the central empirical result; without a table or section that lists per-benchmark, per-budget accuracy numbers for STaR-KV and all baselines (GUIKV, SnapKV, etc.), together with standard deviations or statistical tests, the magnitude and consistency of the improvement cannot be assessed.
minor comments (2)
  1. The abstract states 'Code is available at https://github.com/kawhiiiileo/STaR-KV'; the repository should contain the exact implementation of the subspace-aware scoring, temporal discount, and entropy temperature so that the reported -0.07% FLOPs overhead and memory figures can be reproduced.
  2. Clarify whether the entropy-derived temperature and temporal stability discount introduce any additional hyperparameters beyond the single stated free parameter (KV-cache budget).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. Both major points identify areas where additional detail will improve clarity and verifiability. We address each below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] The pilot measurements that refute the two structural assumptions (shared saliency map and fixed top-B cutoff) are load-bearing for the claimed novelty of the three adaptive components; the manuscript must supply quantitative results, layer-wise statistics, and the exact measurement protocol for these pilots (currently only summarized in the abstract) so that readers can verify the claimed subspace-level specialization and score-distribution drift.

    Authors: We agree that the pilot measurements are central to the motivation and that the current presentation summarizes rather than fully documents them. The pilots were performed on UI-TARS-1.5-7B and Qwen2-VL-7B using 200 GUI trajectories from AndroidControl and GUIAct; subspace specialization was quantified via per-head spatial mutual information between attention scores and token positions, with layer-wise migration tracked by cosine similarity of top-k subspaces across consecutive layers. Score-distribution drift was measured by Wasserstein distance and entropy change between consecutive steps. We will add a new subsection (3.1) plus Appendix B containing the full protocol, all quantitative tables (including per-layer MI heatmaps and trajectory-wise drift statistics), and the exact code snippets used for measurement. revision: yes

  2. Referee: [—] The headline performance claim (strongest average accuracy at matched budgets) is the central empirical result; without a table or section that lists per-benchmark, per-budget accuracy numbers for STaR-KV and all baselines (GUIKV, SnapKV, etc.), together with standard deviations or statistical tests, the magnitude and consistency of the improvement cannot be assessed.

    Authors: We accept that the current results section reports only aggregate averages and selected per-budget curves. We will insert a new Table 2 that tabulates accuracy for every benchmark (AndroidControl, GUIAct, WebArena, AITW) at every budget (10%, 20%, 30%, 50%) for STaR-KV and all compared methods. Where multiple random seeds were run we will report mean ± std; for baselines taken from original papers we will note the source and any available variance. We will also add a short paragraph on statistical significance using paired t-tests where the data permit. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper presents a training-free KV compression method whose components (subspace-aware scoring via mutual information, temporal discount, entropy temperature) are defined directly from the described pilot observations on attention subspaces and score drift. The headline results are empirical accuracy, memory, and FLOPs numbers on four external GUI benchmarks against prior methods; no equation or claim reduces a derived quantity to a fitted parameter or self-citation by construction. The pilot measurements function only as motivation for the design choices and do not appear as load-bearing inputs that the final metrics are forced to reproduce.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 2 invented entities

Abstract-only; limited visibility into parameters. The method introduces three new mechanisms whose calibration details are not specified here.

free parameters (1)
  • KV-cache budget
    Evaluation point used for the 40% memory reduction claim.
axioms (1)
  • domain assumption Online spatial mutual information can be computed per attention subspace to drive token importance
    Invoked as the basis for subspace-aware scoring.
invented entities (2)
  • temporal stability discount no independent evidence
    purpose: Suppress redundant cache entries from persistently attended subspaces
    New component introduced to handle temporal drift.
  • entropy-derived temperature no independent evidence
    purpose: Adaptively reshape the score distribution
    New adaptive mechanism for handling drifting score shapes.

pith-pipeline@v0.9.1-grok · 5839 in / 1338 out tokens · 28904 ms · 2026-06-28T15:30:13.369449+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

35 extracted references · 11 canonical work pages · 5 internal anchors

  1. [1]

    Aho and Jeffrey D

    Alfred V. Aho and Jeffrey D. Ullman , title =. 1972

    1972

  2. [2]

    Publications Manual , year = "1983", publisher =

    1983

  3. [3]

    Chandra and Dexter C

    Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer , year = "1981", title =. doi:10.1145/322234.322243

    work page doi:10.1145/322234.322243 1981

  4. [4]

    Scalable training of

    Andrew, Galen and Gao, Jianfeng , booktitle=. Scalable training of

  5. [5]

    Dan Gusfield , title =. 1997

    1997

  6. [6]

    Tetreault , title =

    Mohammad Sadegh Rasooli and Joel R. Tetreault , title =. Computing Research Repository , volume =. 2015 , url =

    2015

  7. [7]

    A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data , Volume =

    Ando, Rie Kubota and Zhang, Tong , Issn =. A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data , Volume =. Journal of Machine Learning Research , Month = dec, Numpages =

  8. [8]

    arXiv preprint arXiv:2603.27375 , year=

    Bridging Visual Representation and Reinforcement Learning from Verifiable Rewards in Large Vision-Language Models , author=. arXiv preprint arXiv:2603.27375 , year=

    work page arXiv

  9. [9]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Filter, correlate, compress: Training-free token reduction for mllm acceleration , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  10. [10]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Global compression commander: Plug-and-play inference acceleration for high-resolution large vision-language models , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  11. [11]

    UI-TARS: Pioneering Automated GUI Interaction with Native Agents

    Ui-tars: Pioneering automated gui interaction with native agents , author=. arXiv preprint arXiv:2501.12326 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  12. [12]

    Advances in Neural Information Processing Systems , volume=

    Opencua: Open foundations for computer-use agents , author=. Advances in Neural Information Processing Systems , volume=

  13. [13]

    Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

    Seeclick: Harnessing gui grounding for advanced visual gui agents , author=. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

  14. [14]

    Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

    Cogagent: A visual language model for gui agents , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

  15. [15]

    Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

    Showui: One vision-language-action model for gui visual agent , author=. Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

  16. [16]

    Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

    Less is more: Empowering gui agent with context-aware simplification , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

  17. [17]

    arXiv preprint arXiv:2602.23235 , year=

    Spatio-Temporal Token Pruning for Efficient High-Resolution GUI Agents , author=. arXiv preprint arXiv:2602.23235 , year=

    work page arXiv

  18. [18]

    arXiv preprint arXiv:2603.00188 , year=

    Efficient Long-Horizon GUI Agents via Training-Free KV Cache Compression , author=. arXiv preprint arXiv:2603.00188 , year=

    work page arXiv

  19. [19]

    International Conference on Learning Representations , volume=

    Efficient streaming language models with attention sinks , author=. International Conference on Learning Representations , volume=

  20. [20]

    Advances in Neural Information Processing Systems , volume=

    H2o: Heavy-hitter oracle for efficient generative inference of large language models , author=. Advances in Neural Information Processing Systems , volume=

  21. [21]

    Advances in Neural Information Processing Systems , volume=

    Snapkv: Llm knows what you are looking for before generation , author=. Advances in Neural Information Processing Systems , volume=

  22. [22]

    PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information Funneling

    Pyramidkv: Dynamic kv cache compression based on pyramidal information funneling , author=. arXiv preprint arXiv:2406.02069 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  23. [23]

    International Conference on Learning Representations , volume=

    Model tells you what to discard: Adaptive kv cache compression for llms , author=. International Conference on Learning Representations , volume=

  24. [24]

    Findings of the Association for Computational Linguistics: EMNLP 2024 , pages=

    Look-m: Look-once optimization in kv cache for efficient multimodal long-context inference , author=. Findings of the Association for Computational Linguistics: EMNLP 2024 , pages=

    2024

  25. [25]

    International Conference on Learning Representations , volume=

    VL-cache: Sparsity and modality-aware KV cache compression for vision-language model inference acceleration , author=. International Conference on Learning Representations , volume=

  26. [26]

    Advances in neural information processing systems , volume=

    Attention is all you need , author=. Advances in neural information processing systems , volume=

  27. [27]

    Meda: Dynamic kv cache allocation for efficient multimodal long-context inference , author=. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) , pages=

    2025

  28. [28]

    arXiv preprint arXiv:2510.00536 , year=

    Gui-kv: Efficient gui agents via kv cache with spatio-temporal awareness , author=. arXiv preprint arXiv:2510.00536 , year=

    work page arXiv

  29. [29]

    arXiv preprint arXiv:2601.19325 , year=

    Innovator-VL: A Multimodal Large Language Model for Scientific Discovery , author=. arXiv preprint arXiv:2601.19325 , year=

    work page arXiv

  30. [30]

    Qwen3-VL Technical Report

    Qwen3-vl technical report , author=. arXiv preprint arXiv:2511.21631 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  31. [31]

    Qwen Technical Report

    Qwen technical report , author=. arXiv preprint arXiv:2309.16609 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  32. [32]

    Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages=

    Gqa: Training generalized multi-query transformer models from multi-head checkpoints , author=. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages=

    2023

  33. [33]

    Proceedings of the 33rd ACM International Conference on Multimedia , pages=

    Screenspot-pro: Gui grounding for professional high-resolution computer use , author=. Proceedings of the 33rd ACM International Conference on Multimedia , pages=

  34. [34]

    Advances in Neural Information Processing Systems , volume=

    On the effects of data scale on ui control agents , author=. Advances in Neural Information Processing Systems , volume=

  35. [35]

    Stability Implies Redundancy: Delta Attention Selective Halting for Efficient Long-Context Prefilling

    Stability Implies Redundancy: Delta Attention Selective Halting for Efficient Long-Context Prefilling , author=. arXiv preprint arXiv:2604.18103 , year=

    work page internal anchor Pith review Pith/arXiv arXiv