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arxiv: 2506.09522 · v3 · pith:BEF2TMCEnew · submitted 2025-06-11 · 💻 cs.CV · cs.AI· cs.CL

Revisit What You See: Revealing Visual Semantics in Vision Tokens to Guide LVLM Decoding

Beomsik Cho , Jaehyung Kim This is my paper

Pith reviewed 2026-05-19 09:39 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.CL
keywords vision tokensLVLMsdecodingvisual semanticshallucinationstraining-free methodmultimodal generation
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The pith

Vision tokens encode usable semantics that project into text space to steer LVLM decoding.

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

The paper demonstrates that vision tokens in large vision-language models retain meaningful visual details even when the model hallucinates. These details sit in the same representational space as text and can be surfaced by limiting the vocabulary during projection. The authors build a decoding procedure that chooses the single most relevant vision token at each generation step and uses its projection to adjust the next-token probabilities. Because the procedure needs no training, it adds little overhead while keeping the output more faithful to the image. If correct, the approach would let existing models produce more accurate answers at lower compute cost across standard multimodal tasks.

Core claim

Vision tokens provide meaningful visual information even when hallucinations occur, and their semantics are encoded in the textual space and become explicit under appropriate vocabulary constraints. ReVisiT exploits this fact by projecting the selected vision token into the text token distribution and using the resulting distribution to refine the model's output at every decoding step.

What carries the argument

ReVisiT, the training-free procedure that selects the most relevant vision token at each step through context-aware constrained divergence minimization and projects it to adjust the language-model output distribution.

If this is right

  • Text generated by the model aligns more closely with the visual input on standard multimodal benchmarks.
  • Decoding runs use up to half the compute of current state-of-the-art methods while matching or exceeding their accuracy.
  • No additional training is required, so the method can be applied directly to already-deployed LVLMs.
  • Hallucinations decrease because the output distribution is explicitly pulled toward the visual evidence at each step.

Where Pith is reading between the lines

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

  • The same projection idea could be tested on other multimodal generators that mix discrete tokens from different modalities.
  • If vision tokens already carry the needed semantics, future model designs might reduce the number of vision tokens without losing performance.
  • The approach raises the question of whether similar constrained projections would help in purely language models that have access to external knowledge tokens.

Load-bearing premise

Vision token semantics are already encoded in textual space and become explicit enough under vocabulary constraints that their projection can guide decoding without introducing new errors.

What would settle it

A controlled run on the same five benchmarks in which the constrained projection step is added but accuracy does not rise or hallucination rates stay the same or worsen relative to the unmodified baseline.

Figures

Figures reproduced from arXiv: 2506.09522 by Beomsik Cho, Jaehyung Kim.

Figure 1
Figure 1. Figure 1: An overview of ReVisiT. Given an input image and text prompt, the LVLM first encodes the image into vision tokens through a vision encoder and a cross-modal projector. ReVisiT re-purposes these vision tokens as reference informers to guide the text generation process. At each decoding step, ReVisiT (1) constrains the vocabulary V to V t cons, (2) projects vision token embeddings into V t cons and selects m… view at source ↗
Figure 2
Figure 2. Figure 2: Motivation of ReVisiT. We qualitatively analyzed various vision tokens. Dotted arrows represent vision token projection over specified vocabulary set. For each box, representing text token distribution, we annotated top-5 probable text tokens. Left part illustrate the effectiveness of vocabu￾lary constraint, whereas right part shows the distribution shift during ReVisiT. See Appendix C.1 for a detailed dis… view at source ↗
Figure 3
Figure 3. Figure 3: Inference speed. Compari￾son of per-token inference latency across different decoding strategies for LLaVA￾1.5-7B (left y-axis) and Qwen2.5-VL-7B (right y-axis), with standard deviations visualized as error bars. Inference speed improvement. To evaluate the infer￾ence efficiency of ReVisiT compared to baseline decoding strategies, we measure the per-token computational time. All measurements are conducted … view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative example. The input image is a cartoon-style illustration contrasting classical statistical learning and neural network reasoning via a visual metaphor, emphasizing the shift from theoretical rigor to the heuristic of “stacking more layers.” We compare the generated responses of vanilla greedy decoding, M3ID, and ReVisiT, highlighting how ReVisiT better captures the intended visual analogy compa… view at source ↗
Figure 5
Figure 5. Figure 5: Without vocabulary subset case study. Qualitative case study from Qwen2.5-VL-7B. w/o subset refers to ablation result of without vocabulary subset constraint, whereas w/ subset refers to our proposed ReVisiT. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Additional qualitative example. The input image is a illustration showing a bear, a cat, and a rabbit seated around a table with a plate of donuts. We compare the responses of vanilla greedy decoding and ReVisiT to the question, “What are the animals in the painting and what are they doing?” While the greedy output introduces a hallucinated detail (“cookie”) and assigns actions not visually supported (e.g.… view at source ↗
read the original abstract

Large Vision Language Models (LVLMs) achieve strong performance across multimodal tasks by integrating visual perception with language understanding. However, how vision information contributes to the model's decoding process remains under-explored, as reflected in frequent hallucinations. Through a series of analyses, we found that (i) vision tokens provide meaningful visual information even when hallucinations occur, and (ii) their semantics are encoded in the textual space and become explicit under appropriate vocabulary constraints. Building on these observations, we propose ReVisiT, a simple training-free decoding method that guides text generation in LVLMs by Referencing Vision Tokens. Our approach leverages the semantic information embedded within vision tokens by projecting them into the text token distribution. Specifically, ReVisiT dynamically selects the most relevant vision token at each decoding step via context-aware constrained divergence minimization. Then, ReVisiT uses its constrained projection to refine the output distribution to better incorporate visual semantics. Across five benchmarks on recent LVLMs, ReVisiT achieves competitive or superior results to state-of-the-art decoding baselines while reducing computational cost by up to $2\times$

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 ReVisiT, a training-free decoding method for Large Vision-Language Models (LVLMs). It rests on two observations: vision tokens retain meaningful visual information even when hallucinations occur, and their semantics are encoded in textual space, becoming explicit under appropriate vocabulary constraints. The method dynamically selects the most relevant vision token at each step via context-aware constrained divergence minimization and projects it to refine the output text distribution. Across five benchmarks on recent LVLMs, ReVisiT is reported to match or exceed state-of-the-art decoding baselines while reducing computational cost by up to 2×.

Significance. If the experimental support holds, the work offers a lightweight, immediately deployable technique for better exploiting existing vision tokens during LVLM inference. The training-free design and reported efficiency gains address practical concerns around hallucination and compute in multimodal systems, and the underlying observations about vision-token semantics could stimulate further analysis of internal representations.

major comments (2)
  1. [Method] Method section: the precise formulation of the context-aware constrained divergence minimization (including how vocabulary constraints are defined and applied to the projection) is not provided with equations or pseudocode. This detail is load-bearing for verifying that the projection transfers visual semantics without net error increase or unintended bias in the refined distribution.
  2. [Experiments] Experiments section: the abstract and results claim competitive or superior performance with up to 2× cost reduction, yet the manuscript lacks reported statistical significance tests, exact baseline re-implementations, and ablation studies isolating the projection step. These omissions undermine confidence that the gains are robust rather than dependent on particular post-hoc choices.
minor comments (2)
  1. [Abstract] Abstract: the claim of 'up to 2×' computational cost reduction should specify the exact models, benchmarks, and measurement (e.g., FLOPs vs. wall-clock time) under which this holds.
  2. [Method] Notation: ensure consistent use of symbols for vision-token embeddings versus text-token distributions throughout the method description.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and detailed comments on our manuscript. We address each major comment below and will revise the paper to incorporate the suggested improvements for greater clarity and rigor.

read point-by-point responses

  1. Referee: [Method] Method section: the precise formulation of the context-aware constrained divergence minimization (including how vocabulary constraints are defined and applied to the projection) is not provided with equations or pseudocode. This detail is load-bearing for verifying that the projection transfers visual semantics without net error increase or unintended bias in the refined distribution.

    Authors: We appreciate the referee highlighting this gap. While the manuscript describes the high-level approach of context-aware constrained divergence minimization and the subsequent projection, we acknowledge that the detailed equations and pseudocode were not included. In the revised manuscript, we will add the full mathematical formulation, explicitly defining the vocabulary constraints and their application in the projection step. Pseudocode will also be provided to illustrate the dynamic selection and refinement process. This addition will enable verification that visual semantics are incorporated without introducing net error or bias. revision: yes

  2. Referee: [Experiments] Experiments section: the abstract and results claim competitive or superior performance with up to 2× cost reduction, yet the manuscript lacks reported statistical significance tests, exact baseline re-implementations, and ablation studies isolating the projection step. These omissions undermine confidence that the gains are robust rather than dependent on particular post-hoc choices.

    Authors: We agree that these additions would strengthen the experimental claims. In the revision, we will include statistical significance tests (such as paired t-tests) for the reported improvements across the five benchmarks. We will also specify the exact baseline re-implementations, including official code sources, versions, and hyperparameter choices used. Furthermore, we will expand the ablation studies to isolate the contribution of the projection step. These updates will be added to the experiments section to demonstrate robustness. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation is self-contained

full rationale

The paper reports two empirical observations obtained via separate analyses on vision token behavior in LVLMs, then constructs a training-free algorithmic procedure (context-aware constrained divergence projection followed by distribution refinement) that operates on those observations. No equations, fitted parameters, or self-citations are shown that reduce the claimed results to the inputs by construction. The method is presented as a direct procedural application rather than a tautological renaming or self-referential definition, satisfying the default expectation of an independent derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The approach rests on two domain assumptions extracted from the reported analyses; no free parameters or invented entities are mentioned in the abstract.

axioms (2)
  • domain assumption Vision tokens provide meaningful visual information even when hallucinations occur.
    Stated as finding (i) that underpins the decision to reference vision tokens during decoding.
  • domain assumption Vision token semantics are encoded in the textual space and become explicit under appropriate vocabulary constraints.
    Stated as finding (ii) that justifies the projection step into the text token distribution.

pith-pipeline@v0.9.0 · 5729 in / 1258 out tokens · 28571 ms · 2026-05-19T09:39:13.360925+00:00 · methodology

discussion (0)

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    What are the animals in the painting and what are they doing?

    **Y ello w La y er Cak e**: A slice of cak e wit h multiple y ello w and cr eam-color ed la y ers, placed on a black plat e. 3 . **Gr een La y er Cak e**: A slice of cak e wit h multiple gr een and cr eam-color ed la y ers, placed on a black plat e wit h a knif e r esting on t he side. 4 . **Patt erned R oll Cak e**: A r olled cak e wit h a patt erned sur...

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