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arxiv: 2606.22565 · v1 · pith:TGHOHXQZnew · submitted 2026-06-21 · 💻 cs.CL · cs.AI· cs.CV

Look Light, Think Heavy: What Multimodal Chain-of-Thought Reasoning Can and Cannot Do

Zhuoran Jin , Kejian Zhu , Hongbang Yuan , Yupu Hao , Pengfei Cao , Yubo Chen , Kang Liu , Jun Zhao This is my paper

Pith reviewed 2026-06-26 10:25 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.CV
keywords multimodal chain-of-thoughtvisual reasoningperception tasksreflection patternsreasoning modelsobject countingvisual grounding
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The pith

Multimodal chain-of-thought improves verbal reasoning tasks but reduces accuracy on perception tasks and loses visual reflection over time.

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

The paper evaluates chain-of-thought prompting across 12 multimodal tasks and 22 models to determine when the technique helps or hurts. It finds that CoT boosts performance on mathematical, scientific, and multi-image reasoning but lowers results on visual grounding and object counting. Existing open-source reasoning models show only small gains overall. The central pattern identified is that verbal reflection rises and falls during a response while visual reflection steadily drops, pointing to visual introspection as the main limit.

Core claim

Models exhibit a Look Light, Think Heavy pattern in which verbal reflection increases then decreases during reasoning while visual reflection consistently diminishes, making sustained visual introspection the primary bottleneck for multimodal CoT.

What carries the argument

Analysis of verbal versus visual reflection patterns extracted from model outputs across perception and reasoning tasks.

If this is right

  • CoT prompting should be applied selectively rather than by default in multimodal systems.
  • Training focused mainly on mathematical reasoning leaves other multimodal capabilities underdeveloped.
  • Future multimodal models need mechanisms to keep visual attention active across multiple reasoning steps.

Where Pith is reading between the lines

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

  • If visual reflection can be preserved, perception-task performance may rise without sacrificing reasoning gains.
  • Task-specific routing that turns CoT on only for reasoning categories could become a practical design choice.
  • The pattern suggests that current vision encoders are not yet integrated deeply enough into the step-by-step reasoning loop.

Load-bearing premise

The 12 chosen tasks and 22 models represent the full range of multimodal perception and reasoning, and output reflection counts accurately reflect the model's internal processes.

What would settle it

A new model or training method that maintains high visual reflection counts through entire reasoning traces on the same perception tasks while also raising accuracy.

Figures

Figures reproduced from arXiv: 2606.22565 by Hongbang Yuan, Jun Zhao, Kang Liu, Kejian Zhu, Pengfei Cao, Yubo Chen, Yupu Hao, Zhuoran Jin.

Figure 1
Figure 1. Figure 1: Main findings of multimodal CoT reasoning. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Comparison between direct answer and CoT. Y-axis shows the performance gain of CoT. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comparison between non-reasoning models and reasoning models. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Examples of visual and textual reasoning probes for mathematics and logical reasoning tasks. reasoning models exhibit similar paradigms and limitations in their reasoning over visual informa￾tion, based on both external reflection behaviours and internal attention mechanisms. 4.1 Visual Reasoning Bottleneck in Multimodal Reasoning To investigate the role of visual reasoning in multi￾modal CoT, we first ana… view at source ↗
Figure 5
Figure 5. Figure 5: Error analysis of CoT in mathematical and logical reasoning. [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Correlation between overall task performance [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Visual reflection and verbal reflection be￾haviours in multimodal CoT. 4.2 Reflection Behaviours in Multimodal Chain-of-Thought Given that visual reasoning is a primary limi￾tation in multimodal CoT, we further examine what factors constrain models’ ability to reason over visual information. As reflection and self￾verification are critical capabilities of reasoning models (DeepSeek-AI et al., 2025; OpenAI,… view at source ↗
Figure 8
Figure 8. Figure 8: Step-wise distribution of visual and verbal reflection in CoT. The two rows show MathVista and MathVista with missing critical visual information. More results are provided in [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Attention visualizations of Kimi-VL-A3B [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: An example of the comprehensive evaluation task with both direct and CoT responses. [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: An example of the OCR task with both direct and CoT responses. [PITH_FULL_IMAGE:figures/full_fig_p018_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: An example of the visual grounding task with both direct and CoT responses. [PITH_FULL_IMAGE:figures/full_fig_p019_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: An example of the hallucination task with both direct and CoT responses. [PITH_FULL_IMAGE:figures/full_fig_p020_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: An example of the knowledge-base VQA task with both direct and CoT responses. [PITH_FULL_IMAGE:figures/full_fig_p021_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: An example of the object counting task with both direct and CoT responses. [PITH_FULL_IMAGE:figures/full_fig_p022_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: An example of the mathematical reasoning task with both direct and CoT responses. [PITH_FULL_IMAGE:figures/full_fig_p023_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: An example of the scientific reasoning task with both direct and CoT responses. [PITH_FULL_IMAGE:figures/full_fig_p024_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: An example of the logical reasoning task with both direct and CoT responses. [PITH_FULL_IMAGE:figures/full_fig_p025_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: An example of the algorithmic reasoning task with both direct and CoT responses. [PITH_FULL_IMAGE:figures/full_fig_p026_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: An example of the spatial reasoning task with both direct and CoT responses. [PITH_FULL_IMAGE:figures/full_fig_p027_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: An example of the multi-image reasoning task with both direct and CoT responses. [PITH_FULL_IMAGE:figures/full_fig_p028_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Step-wise distribution of visual and verbal reflection in CoT. D Implementation Details We use vllm 1 for open-source MLLM inference. All experiments are conducted on 4×A100 80GB GPUs. For all models, we set the temperature to 0.7 as the generation hyperparameter. To better understand the failure cases of multimodal CoT reasoning, we manually classify the errors into the following categories: (1) Visual R… view at source ↗
Figure 23
Figure 23. Figure 23: Correlation between overall task perfor [PITH_FULL_IMAGE:figures/full_fig_p037_23.png] view at source ↗
Figure 25
Figure 25. Figure 25: Attention visualizations of Kimi-VL-A3B [PITH_FULL_IMAGE:figures/full_fig_p038_25.png] view at source ↗
Figure 28
Figure 28. Figure 28: Attention visualizations of Qwen3-VL-30B [PITH_FULL_IMAGE:figures/full_fig_p039_28.png] view at source ↗
Figure 27
Figure 27. Figure 27: Attention visualizations of Qwen3-VL-8B [PITH_FULL_IMAGE:figures/full_fig_p039_27.png] view at source ↗
Figure 29
Figure 29. Figure 29: Refusing to answer when images lack key information. [PITH_FULL_IMAGE:figures/full_fig_p040_29.png] view at source ↗
Figure 30
Figure 30. Figure 30: Leveraging external tools for visual localization and algorithm execution. [PITH_FULL_IMAGE:figures/full_fig_p041_30.png] view at source ↗
Figure 31
Figure 31. Figure 31: Leveraging external tools for visual localization and algorithm execution. [PITH_FULL_IMAGE:figures/full_fig_p042_31.png] view at source ↗
read the original abstract

Chain-of-Thought (CoT) has become a standard method for improving reasoning capabilities in large language models (LLMs) by eliciting step-by-step thinking, but its effectiveness in multimodal tasks remains unclear. In this paper, we aim to systematically investigate the key question: What can multimodal Chain-of-Thought reasoning do, and where and why does it fall short? To this end, we evaluate 12 multimodal tasks across perception and reasoning categories using both 14 non-reasoning models and 8 reasoning models. Our analysis reveals several important findings: (1) CoT is not a free lunch and should be used selectively depending on the specific requirements of each task. For perception tasks, CoT can lead to undesirable side effects, such as reduced performance in visual grounding and object counting. In contrast, it proves effective for reasoning tasks involving mathematical, scientific, and multi-image reasoning; (2) Compared to original models, existing open-source multimodal reasoning models often yield only marginal overall improvements, possibly due to an overemphasis on mathematical reasoning at the expense of broader capabilities; (3) Visual reasoning remains a key bottleneck for current multimodal CoT, as models exhibit a Look Light, Think Heavy pattern where verbal reflection rises and falls during reasoning, whereas visual reflection consistently diminishes. These findings suggest that while multimodal CoT handles verbal reflection relatively well, it lacks the ability to maintain deep visual introspection throughout the reasoning process.

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

3 major / 2 minor

Summary. The paper evaluates multimodal Chain-of-Thought reasoning on 12 tasks spanning perception and reasoning categories using 14 non-reasoning and 8 reasoning multimodal models. It reports that CoT harms performance on perception tasks such as visual grounding and object counting, yields only marginal gains for existing open-source reasoning models, and identifies visual reasoning as the primary bottleneck via a 'Look Light, Think Heavy' pattern in which verbal reflection varies while visual reflection consistently diminishes across reasoning steps.

Significance. If the empirical patterns hold under more rigorous controls, the work supplies a useful large-scale benchmark of multimodal CoT behavior and isolates a concrete failure mode (sustained visual introspection) that future architectures must address. The breadth of 22 models and 12 tasks is a strength that supports generalizability claims.

major comments (3)
  1. [§4] §4 (Reflection Pattern Analysis): The central claim that visual reasoning is the key bottleneck rests on post-hoc parsing of generated CoT text to quantify 'visual reflection.' No attention visualization, hidden-state probing, or causal ablation (e.g., forcing continued visual token references) is reported to confirm that reduced visual content in outputs reflects an internal reasoning failure rather than a generation bias or prompt artifact. This measurement choice is load-bearing for the 'Look Light, Think Heavy' conclusion.
  2. [§3.2] §3.2 (Task and Metric Details): Performance differences for perception tasks are described as directional but the manuscript provides no error bars, statistical significance tests, or controls for confounding factors such as output length or prompt phrasing. Without these, it is unclear whether the reported harms of CoT on visual grounding and counting are robust.
  3. [§3.1] §3.1 (Model and Task Selection): The claim that the 12 tasks and 22 models are representative of multimodal perception/reasoning capabilities is asserted without a justification or sensitivity analysis showing that results are stable under alternative task subsets or model families.
minor comments (2)
  1. [Abstract] Abstract and §4: The term 'visual reflection' is used without an explicit operational definition or example of the parsing rules applied to model outputs.
  2. Figure captions and tables: Several result tables lack explicit column definitions for the reflection metrics, making it difficult to reproduce the 'rises and falls' versus 'consistently diminishes' patterns.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and indicate where revisions have been or will be made to strengthen the manuscript.

read point-by-point responses

  1. Referee: [§4] §4 (Reflection Pattern Analysis): The central claim that visual reasoning is the key bottleneck rests on post-hoc parsing of generated CoT text to quantify 'visual reflection.' No attention visualization, hidden-state probing, or causal ablation (e.g., forcing continued visual token references) is reported to confirm that reduced visual content in outputs reflects an internal reasoning failure rather than a generation bias or prompt artifact. This measurement choice is load-bearing for the 'Look Light, Think Heavy' conclusion.

    Authors: We acknowledge that the 'Look Light, Think Heavy' analysis relies on post-hoc parsing of output text rather than internal model probes. This text-based quantification was chosen for its scalability across 22 diverse models and to directly measure observable reflection behavior in generated reasoning chains. We agree this does not constitute causal evidence of internal failure modes. In the revised manuscript we have expanded §4 with (i) the exact parsing rules and inter-annotator agreement, (ii) an explicit limitations paragraph noting the absence of attention or probing experiments, and (iii) a clearer statement that the pattern describes output-level behavior while still highlighting visual introspection as an empirical bottleneck. We view full causal ablations as valuable future work but outside the scope of the current large-scale benchmark. revision: partial

  2. Referee: [§3.2] §3.2 (Task and Metric Details): Performance differences for perception tasks are described as directional but the manuscript provides no error bars, statistical significance tests, or controls for confounding factors such as output length or prompt phrasing. Without these, it is unclear whether the reported harms of CoT on visual grounding and counting are robust.

    Authors: We agree that the original presentation of perception-task results lacked sufficient statistical controls. In the revised version we have added (i) error bars (standard deviation across three independent generations per model-task pair), (ii) paired statistical tests (Wilcoxon signed-rank) for the key perception tasks, and (iii) a supplementary analysis that matches CoT and non-CoT outputs by length to rule out length-related confounds. Prompt phrasing was already held constant; we now explicitly state this in §3.2. revision: yes

  3. Referee: [§3.1] §3.1 (Model and Task Selection): The claim that the 12 tasks and 22 models are representative of multimodal perception/reasoning capabilities is asserted without a justification or sensitivity analysis showing that results are stable under alternative task subsets or model families.

    Authors: The 12 tasks were deliberately chosen to cover canonical perception and reasoning categories drawn from established benchmarks (VQA, GQA, MathVista, etc.), and the 22 models span both open-source and proprietary families as well as reasoning-specialized variants. We have added a dedicated paragraph in §3.1 that justifies this selection by referencing prior multimodal evaluation surveys and reports a limited sensitivity check on two task subsets (perception-only and math/science-only) confirming that the main directional patterns remain stable. A exhaustive sensitivity analysis across all possible subsets is computationally prohibitive at this scale; we therefore present the current selection as a broad but not exhaustive sample. revision: partial

Circularity Check

0 steps flagged

No circularity: purely observational benchmarking with no derivations or self-referential claims

full rationale

The paper conducts an empirical evaluation of 22 models across 12 multimodal tasks, measuring performance and parsing generated CoT outputs for reflection patterns. No equations, parameters, or derivations are present. The central 'Look Light, Think Heavy' observation is a direct post-hoc analysis of output text, not a fitted prediction or self-citation-dependent result. All claims rest on the experimental data collected for this study rather than reducing to prior self-referential inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper is an empirical benchmarking study whose conclusions rest on the representativeness of its task and model selection rather than on new mathematical derivations or invented constructs.

axioms (1)
  • domain assumption The 12 tasks and 22 models are representative of multimodal CoT behavior
    General conclusions are drawn from this fixed set without explicit justification of coverage.

pith-pipeline@v0.9.1-grok · 5812 in / 1010 out tokens · 30069 ms · 2026-06-26T10:25:35.181096+00:00 · methodology

discussion (0)

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Reference graph

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