DIVE: Dynamic Iterative Visual Evidence Construction for Efficient Vision-Language Models

Chen Zhong; Guangyi Yang; Jiepan Li; Wei He; Xiao An; Zijie Wang

REVIEW 2 major objections 5 minor 58 references

DIVE: Dynamic Iterative Visual Evidence Construction for Efficient Vision-Language Models

T0 review · 2 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read DIVE claims that one-shot visual-token scoring is the wrong basis for pruning in vision-language models: iterative select-update-re-evaluate preserves 98.2% of full-model accuracy while cutting 88.9% of visual tokens.

desk verdict Solid, useful empirical systems paper for VLM token pruning; the mechanism ablation is overstated because the 'No Update' control is degenerate, but the headline result survives on external baselines. read the letter →

arxiv 2608.04496 v1 pith:LWZ6PLF3 submitted 2026-08-05 cs.CV cs.LG

Chen Zhong , Xiao An , Zijie Wang , Jiepan Li , Guangyi Yang , Wei He This is my paper · ORCID

classification cs.CVcs.LG

keywords vision-languagemodelsvisualtokenpruningiterativeselectionresidualfeedbackpromptconditioninginferenceefficiencytraining-freesubset

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper argues that one-shot visual-token scoring is the wrong basis for pruning in vision-language models because a token's usefulness depends on which tokens are already retained. It introduces DIVE, a training-free procedure that constructs the retained set one token at a time: pick the highest-scoring remaining token, discount the visual and text evidence that token explains, and re-score the rest. On LLaVA-1.5-7B this preserves 98.2% of the full model's average performance while dropping 88.9% of the visual tokens, and the advantage holds across the LLaVA family, Qwen2-VL, and video-capable models. The source of the gain is set-dependent residual feedback, not a better static importance score.

What carries the argument

The load-bearing object is the one-sided feedback operator $F_u(z) = [\langle z, u \rangle]_+ u$, applied to the visual and query residuals after every selection. It removes only the component of each remaining residual that is positively aligned with the newly selected token's direction, so the update is monotone in residual energy and leaves orthogonal or negatively aligned components untouched. Because the same direction $u_t$ is applied to both the visual state $X^{(t)}$ and the prompt state $Q^{(t)}$, the discount covers prompt directions that the selected token has already addressed, coupling the two modalities inside the selection score.

What would settle it

Two direct checks would settle the claim: measure the top singular values of the cross-covariance between the row-normalized visual and text hidden states at the pruning layer, since near-zero values would mean the language-guidance term carries no signal, or find any benchmark and token budget where DIVE's frozen-score ranking matches the iterative procedure's accuracy, contradicting the reported 97.2% versus 62.0% gap at 64 tokens.

Watch

Extended reading notes

Core claim

The central discovery is that a visual token's marginal value is state-dependent: a high-scoring patch loses most of its worth once the retained set already covers the same region, while a previously secondary patch becomes valuable. DIVE operationalizes this by keeping evolving visual residuals $x_i^{(t)}$ and prompt residuals $q_m^{(t)}$, initialized by row-wise $\ell^2$ normalization of the visual and text rows of the same multimodal hidden-state sequence, taken after two Transformer blocks. At each step the score $g_t(i) = \exp(\lambda \phi_i^{(t)}) \|x_i^{(t)}\|_2^2$ multiplies residual energy by a prompt-alignment factor; the selected token supplies the feedback direction $u_t$, and both residuals are discounted by subtracting $\eta$ times their positive projection onto $u_t$. Repeating this select–update–re-evaluate loop for $K$ steps yields a compact set of complementary, prompt-relevant evidence, and the original token representations are restored to input order before the frozen VLM processes them.

Load-bearing premise

The prompt-alignment weight assumes that row-normalized visual and text hidden states from the same multimodal sequence share a coordinate space, so inner products measure prompt relevance; if those representations are not actually aligned, the language-guidance term could steer selection toward irrelevant patches.

Editorial extensions

If this is right

At matched token budgets, DIVE outperforms static top-k baselines (FastV, SparseVLM, VisionZip, PruneSID) and greedy set-selection methods (MMTok, CDPruner, SCOPE) across the eight image benchmarks and on MVBench and VideoMME.
DIVE's No Update variant, which freezes the same scores into a one-shot ranking, falls from 97.2% to 62.0% average relative performance at 64 tokens, isolating iterative residual feedback as the source of the gain.
Residual feedback reduces within-set redundancy, measured by mean maximum cosine similarity among retained tokens, and the reduction is largest at the tightest 64-token budget.
The selection overhead is mostly amortized: the full update schedule adds 30.7% to prefill latency but only 1.8% to end-to-end latency with 64 retained tokens and 32 generated tokens.
Fixed hyperparameters ($\lambda=1.5$, $\eta=0.8$, top-3 prompt pooling, pruning after block 2) transfer across backbones, resolutions, image and video tasks, and token budgets without retuning.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The shared-representation assumption could be stress-tested at deeper layers: if DIVE's language-guidance contribution shrinks when selection happens after more than two blocks, the early-layer alignment is doing the work rather than the residual feedback per se.
The one-sided projection update is a greedy orthogonalization of the visual and query residuals, which suggests a formal connection to coverage maximization or submodular objectives that the paper does not claim.
Because DIVE only replaces the token-selection step and leaves the VLM frozen, it should compose with other inference accelerators such as KV-cache eviction, quantization, or speculative decoding.
For models with no hidden-state access, a black-box proxy of residual evidence—for instance attention-weighted saliency—could carry the same select-update-re-evaluate idea to API-only VLMs.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Two things to know. First, DIVE is a clean, well-specified iterative selection method for visual-token pruning in VLMs, and its benchmark claims hold up on the reported numbers. Second, the paper's internal ablation for the mechanism is weaker than it looks: the 'No Update' control selects by prompt alignment alone, so the 35-point gap to full DIVE does not show that residual re-evaluation beats a reasonable static scorer.

The new thing is the select-update-re-evaluate loop (Eqs. 1–3): after picking a token, DIVE removes the positively aligned component from both visual and query residuals, then re-scores. That coupling of visual and textual residual feedback is not in the cited static or greedy baselines. The method is training-free, the complexity analysis in Appendix A.4 is correct, and the experiments are broad: LLaVA-1.5/NeXT, Qwen2-VL, image and video, several token budgets, plus an efficiency table and latency–performance sweeps. The comparison against greedy coverage and diversity methods in Table 10 is the right control, and DIVE leads there too.

Soft spots, in proportion. The stress-test note is right: with row-normalized initialization, the frozen score at t=0 is exp(λ·prompt-alignment), so 'No Update' is not a meaningful static top-k baseline. The paper itself concedes this, saying it is 'not a competitive static pruning baseline'; that makes the causal claim in Section 4.4 weaker than the prose suggests. But this does not sink the paper. The headline claim rests on Table 1 and Table 10 against real static and greedy methods, where DIVE wins at equal token budgets. So the flaw is in the mechanism-validity narrative, not in the practical result.

Two smaller issues. There are no error bars or repeated runs anywhere, so a few of the small margins at high budgets are unquantified. And λ, η, and h were selected after looking at Table 7 on the same LLaVA-1.5-7B 64-token setting; the sensitivity table shows performance is stable across values, so this is mild, but it should be stated as such. The shared-representation assumption for comparing visual and text inner products is plausible and partially validated by the w/o L-G ablation, which only loses about a point.

Who this is for: anyone working on efficient VLM inference or token pruning. The paper is honest, reproducible in principle (code link included), and the method is simple enough to be drop-in. It does not change the field, but it is a genuinely useful benchmark point.

Recommendation: send to peer review. A serious referee should ask for a real static one-shot baseline (e.g., one-shot scoring with residual norms, no updates), error bars, and a cleaner statement of the hyperparameter selection. With those, the mechanism claim could be made solid. As it stands, the practical claim is already solid.

Referee Report

2 major / 5 minor

Summary. DIVE proposes a training-free framework for visual-token pruning in vision-language models (VLMs). Instead of scoring all visual tokens once and retaining the top-k, DIVE iteratively selects the highest-scoring remaining token, updates both visual and textual residual states by subtracting the one-sided projection onto the selected token's direction, and re-evaluates the remaining candidates. The selection layer is fixed after the first two LLM blocks, and the retained tokens are restored to their original order before being passed to the frozen VLM. Experiments across LLaVA-1.5, LLaVA-NeXT, Qwen2-VL, and LLaVA-OV on eight image and two video benchmarks report that DIVE preserves a higher fraction of the full-token model's average performance than existing static or greedy methods at matched token budgets; the headline claim is 98.2% retained average performance with 64 of 576 visual tokens. The paper includes component ablations, parameter sensitivity, a residual-energy proposition, complexity analysis, and a qualitative analysis of selected tokens.

Significance. If the reported results hold, DIVE provides a practical, training-free improvement for efficient VLM inference, with consistent gains across model families and token budgets. The paper is generally careful: it releases code, gives a precise algorithm including initialization and update formulas, proves the monotone residual-energy decrease (Appendix A.3), reports an explicit complexity expression (Appendix A.4), and compares against both static top-k methods and greedy subset-selection methods (Table 10). The main scientific claim, that set-dependent residual feedback is superior to one-shot scoring at equal budgets, is plausible and practically supported by the external comparisons against FastV, VisionZip, PruneSID, MMTok, CDPruner, and SCOPE. However, the paper's own controlled evidence for the mechanism is weaker than presented: the 'No Update' ablation is degenerate, and the hyperparameter configuration was fixed based on the same LLaVA-1.5-7B 64-token setting used in the headline result. These issues do not invalidate the empirical comparisons, but they do need to be addressed before the causal claim about iterative re-evaluation can be accepted.

major comments (2)

[§3.1, Eq. (5), Eq. (8), Table 6] The 'No Update' ablation does not isolate the effect of iterative residual feedback. Because the algorithm initializes every visual residual by row-wise L2 normalization, ||x_i^(0)||_2 = 1 for all candidates, so at t=0 the score in Eq. (8) reduces to g_0(i) = exp(lambda * phi_0(i)). The frozen one-shot ranking used by 'No Update' is therefore purely a prompt-alignment ranking and contains no visual residual-energy term. The 61.97% versus 97.20% gap in Table 6 conflates the removal of iterative updates with the removal of the visual residual signal. The manuscript's own statement that No Update is 'rather than serving as a competitive static pruning baseline' concedes this point, but Section 4.4 still concludes that 'set-dependent residual feedback is necessary within the DIVE formulation.' That conclusion is not supported by the current ablation. Please add a static control that uses both prompt alignment and a meaningful visual-saliency term (for example, the original pre-normalization norm or an attention-based score) so that the comparison isolates the benefit of re-evaluating after each selection.
[Appendix C.2 and §3.3] The global configuration lambda = 1.5, eta = 0.8, h = 3 was selected after inspecting the LLaVA-1.5-7B 64-token setting, and the sensitivity table (Table 7) is computed on the same benchmarks that appear in the main results. The text states only that the configuration was 'fixed' and then 'examined'; it does not state whether a separate validation split was used or whether the authors observed the test-set results before choosing lambda, eta, and h. As written, the headline 98.2% figure for the 64-token LLaVA-1.5-7B setting is a result of searching over the parameter grid on that same benchmark set. This does not necessarily invalidate the cross-backbone and cross-budget findings, especially since the sensitivity range in Table 7 is modest (96.06% to 97.20%), but the selection protocol must be disclosed so the reader can interpret how much of the reported advantage is a tuned operating point.

minor comments (5)

[Table 6 and Figure 4] The text refers to 'No Update' in Table 6 and 'Frozen Score' in Figure 4 for the same zero-update schedule; please unify the terminology to avoid confusion.
[Table 5] Several numeric entries are visually corrupted by missing spaces, for example '13.5962.8577.0' and '78.8585.7' in the SparseVLM and DIVE rows. Please regenerate the table with clean alignment.
[§3.2] The assumption that visual and textual hidden states from the same multimodal sequence can be compared directly by inner products is stated but not validated. The ablation in Table 6 shows that removing language guidance costs only about one relative percentage point (96.13% vs. 97.20% for DIVE), so the prompt-alignment term is a minor contributor to the overall result; the paper should either provide a probe validating the alignment or temper the language about the importance of prompt-conditioned scoring.
[Appendix B.1] The average metric in Eq. (16) is computed from unrounded benchmark scores, which is good, but Table 1 and Table 6 use different subsets of benchmarks for the same 64-token LLaVA-1.5-7B setting (eight vs. six). Please state this explicitly in the table captions or the text so the different Avg. values (98.21% vs. 97.20%) are not perceived as inconsistent.
[Figure 4] Panel (c) reports 'decode-normalized E2E' but the axis label and legend do not make clear that decoding time is replaced by the No-Update mean; Appendix B.4 describes this, but the figure itself should carry a short note.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: DIVE's claims are empirical benchmark results, not derivations from fitted or self-cited inputs.

full rationale

The paper's central claim is that its select-update-re-evaluate mechanism preserves VLM accuracy better than static top-k pruning. This is supported by direct benchmark comparisons (Tables 1-4 and 10-14) against external methods, not by a derivation in which an output quantity is defined in terms of the claimed result. The scoring function (Eq. 1 / Eq. 8) is a fixed, parameterized heuristic: lambda and eta are chosen once (lambda=1.5, eta=0.8) and held constant across backbones and budgets, with a sensitivity analysis (Table 7) showing that the averaged result is stable. That is hyperparameter selection, not fitting a prediction target. The No Update ablation is indeed a weak control: because initial residuals are row-wise L2-normalized (Eq. 5), the frozen one-shot score g_0(i)=exp(lambda*phi_0(i)) reduces to pure prompt alignment with no visual-residual term, and the paper itself concedes that the variant is 'rather than serving as a competitive static pruning baseline.' This undermines the internal causal claim about residual feedback in Table 6, but it is a soundness/experimental-design problem, not circularity: the paper does not define DIVE's accuracy in terms of No Update, and the external comparisons with FastV, VisionZip, PruneSID, MMTok, CDPruner, and SCOPE at matched budgets (Tables 1 and 10) independently support the practical claim. No load-bearing self-citation or imported uniqueness theorem appears: the cited related work is contextual, and DIVE's residual update is an original construction with a self-contained Proposition 1. The paper's global statement 'DIVE retains 98.2% of the uncompressed model's average performance' is a measured benchmark ratio (Eq. 16), not a consequence of its own assumptions. Therefore there is no circular step under the stated definitions.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central method rests on two empirical domain assumptions: cross-modal hidden states are inner-product comparable, and one-sided residual projection is a good proxy for evidence coverage. Hyperparameters lambda, eta, h, and l are set once and tested for sensitivity. No new physical or conceptual entities are introduced.

free parameters (5)

lambda (prompt-conditioning strength) = 1.5
Controls the trade-off between residual visual energy and prompt alignment in Eq. (1). Fixed across all experiments; the sensitivity table on the LLaVA-1.5-7B 64-token setting informed the choice.
eta (feedback strength) = 0.8
Update coefficient in Eq. (3). Chosen to conservatively discount aligned evidence; Table 7 shows average performance between 96.58% and 97.20% across eta values.
h (top-h pooling size) = 3
Number of largest candidate-query alignments averaged in Eq. (7). Fixed as min(3, M); the sensitivity table shows values from 96.68% to 97.20%.
selection layer l = 2
DIVE performs selection after the first two LLM blocks (Appendix B.2). This is fixed across all backbones and not swept, which affects the availability and alignment of representations.
epsilon threshold = 1e-6
Clamp denominator in Eq. (5) and degenerate-residual break condition in Algorithm 1 line 5; a numerical safeguard.

assumptions (4)

domain assumption Visual and text token hidden states at the selection layer lie in a shared representation space, so L2-normalized inner products measure prompt relevance.
Load-bearing for the prompt-alignment term in Section 3.1, Eqs. (4)-(7). If false, language guidance could be uninformative or harmful. The w/o L-G ablation partially validates this by showing a small but consistent gain from the language term.
domain assumption One-sided projection feedback in Eqs. (2)-(3) reduces visual redundancy and updates the unresolved prompt direction appropriately for downstream VLM accuracy.
The mechanism is a design choice, not derived from an objective. Appendix A.3 proves only monotone residual-energy decrease, not that this maximizes task performance.
domain assumption Pruning after two LLM blocks and restoring original token order preserves the information needed by the frozen later layers.
The paper fixes l=2 for all models without a layer sweep; this is an architectural assumption about where reliable cross-modal representations emerge.
standard math The norm identities in Proposition 1 and the complexity analysis in Appendix A.4 are standard.
The residual-energy update proof and the O(MNd + K((N+M)d+MN)) selection cost are straightforward algebra and arithmetic.

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Pith. "Pith review of DIVE: Dynamic Iterative Visual Evidence Construction for Efficient Vision-Language Models." pith.science (2026). https://pith.science/paper/LWZ6PLF3

@misc{pith2026260804496,
  author       = {Pith},
  title        = {Pith review of: DIVE: Dynamic Iterative Visual Evidence Construction for Efficient Vision-Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LWZ6PLF3}},
  note         = {Machine review of arXiv:2608.04496}
}

read the original abstract

Visual inputs in vision-language models (VLMs) are often encoded into substantially longer token sequences than text, making visual tokens a major bottleneck for efficient inference. Abundant recent methods address this bottleneck by scoring token importance and pruning low-scoring tokens in a single pass. However, one-shot scoring is insufficient because a token's prompt-relevant usefulness depends on the evidence already retained. Motivated by this insight, we introduce DIVE (Dynamic Iterative Visual Evidence Construction), a training-free framework that recasts visual-token pruning as dynamic evidence construction. DIVE repeatedly selects the remaining token with the highest residual-conditioned score, updates the visual and prompt residuals to discount the evidence already explained, and re-evaluates the remaining tokens. This select-update-re-evaluate process builds a retained set of complementary, prompt-relevant evidence. Experiments across eight image-understanding benchmarks show that DIVE consistently preserves performance across token budgets. With an 88.9% reduction in visual tokens, DIVE retains 98.2% of the uncompressed model's average performance. Code is available at https://github.com/Zhong-Chenchen/DIVE.git.

Figures

Figures reproduced from arXiv: 2608.04496 by the authors.

**Figure 2.** Overview of DIVE. Static top-K pruning fixes all candidate scores before constructing the retained subset. DIVE maintains a visual-text residual state and repeats three operations: select the token with the largest residual-conditioned score, update the state to discount the evidence explained by that token, and re-evaluate the remaining candidates. The selected tokens are finally restored to their original order an… view at source ↗

**Figure 3.** End-to-end performance–latency trade-offs across POPE, TextVQA, VizWiz, and GQA on LLaVA-1.5-7B. Latency denotes full-benchmark wall-clock time, including inference, prediction saving, and metric computation. All performance values are reported as accuracy (%). 4.3 Efficiency Analysis The reduction in the number of visual tokens only provides potential computational savings; for methods that require additional toke… view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: Mechanism validation of set-dependent residual feedback. (a) Correct-Answer Support: Relative gold-answer likelihood under a 64-token budget. Boxes and dots show the likelihood distributions, red curve reports the score. (b) RetainedSet Redundancy: Mean maximum cosine…

**Figure 5.** Figure 5: Evolution of DIVE’s dynamic fused score. Numbered green boxes mark tokens selected by the displayed steps. The score map is recomputed as the retained set grows, redirecting later selections toward visual content not yet represented [PITH_FULL_IMAGE:figures/full_fig_p…

**Figure 6.** Figure 6: Qualitative comparison of prompt-conditioned visual-token selection across original images, initial fused scores, [PITH_FULL_IMAGE:figures/full_fig_p020_6.png]

**Figure 7.** Figure 7: Qualitative comparison of real visual-token masks produced by FastV, VisionZip, PruneSID, and DIVE. Colored [PITH_FULL_IMAGE:figures/full_fig_p021_7.png]

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

Divprune: Diversity-based visual token pruning for large multimodal models , author=. Proceedings of the Computer Vision and Pattern Recognition Conference , pages=
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Highlighted Tokens

Don't Just Chase “Highlighted Tokens” in MLLMs: Revisiting Visual Holistic Context Retention , author=. Advances in Neural Information Processing Systems , volume=
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2026 , eprint=

Prune Redundancy, Preserve Essence: Vision Token Compression in VLMs via Synergistic Importance-Diversity , author=. 2026 , eprint=

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Findings of the Association for Computational Linguistics: NAACL 2025 , pages=

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2026 , eprint=

ResPrune: Text-Conditioned Subspace Reconstruction for Visual Token Pruning in Large Vision-Language Models , author=. 2026 , eprint=

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2026 , eprint=

ViTCoP: Accelerating Large Vision-Language Models via Visual and Textual Semantic Collaborative Pruning , author=. 2026 , eprint=

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Rethinking Token Reduction for Large Vision-Language Models , author=. 2026 , eprint=

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CHOICE: Benchmarking the Remote Sensing Capabilities of Large Vision-Language Models , author=. 2025 , eprint=

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LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal Models , author=. 2026 , eprint=

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Beyond Attention or Similarity: Maximizing Conditional Diversity for Token Pruning in MLLMs , author=. 2025 , eprint=

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2025 , eprint=

SCOPE: Saliency-Coverage Oriented Token Pruning for Efficient Multimodel LLMs , author=. 2025 , eprint=

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

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