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arxiv: 2606.05736 · v1 · pith:VFLHAXBTnew · submitted 2026-06-04 · 💻 cs.CV

VTI-CoT: Visual-Textual Interleaved Chain of Thought for Video Reasoning

Shufan Zhang , Ziyue Lin , Bairun Wang , Lei Jin , Xuanding Ding , Xinzhu Ma , Kunlin Yang This is my paper

Pith reviewed 2026-06-28 01:50 UTC · model grok-4.3

classification 💻 cs.CV
keywords video reasoningchain of thoughtmultimodal CoTvisual-textual interleavingOCR compressionautomated data annotationtemporal event understanding
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The pith

Interleaving visual frames into chain-of-thought reasoning improves video reasoning accuracy and training efficiency.

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

Video reasoning requires tracking events and causes across time, yet existing chain-of-thought approaches perform all deduction in text and therefore lose direct access to the visual evidence. The paper introduces a framework that inserts the relevant video frames alongside each textual reasoning step so the model can review the actual footage while reasoning. It supplies the missing interleaved data through an automated annotation pipeline and shortens the resulting long token sequences with OCR-based compression to make training practical. If these steps succeed, models of ordinary size could handle complex temporal and causal video tasks more reliably while requiring less training compute.

Core claim

The paper claims that a Visual-Textual Interleaved Chain of Thought framework, which pairs each textual reasoning step with the corresponding visual frames, together with an automated pipeline that generates such multimodal supervision and an OCR compression step that collapses long CoT sequences into a single canvas, produces state-of-the-art video reasoning results among models of the same parameter count and markedly faster training convergence.

What carries the argument

The Visual-Textual Interleaved CoT that inserts matching video frames into each textual reasoning step, supported by automated multimodal annotation and OCR-based compression of supervision signals.

If this is right

  • Models of fixed parameter count reach higher accuracy on temporal and causal video tasks than text-only CoT baselines.
  • Long video sequences become trainable because CoT token length is reduced without discarding critical visual information.
  • Training time decreases substantially while performance on complex event understanding improves.
  • The same interleaving pattern applies to other long-sequence multimodal reasoning problems.

Where Pith is reading between the lines

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

  • The compression technique could be tested on non-video sequential tasks where token length also limits training.
  • If the automated pipeline generalizes, similar data-generation methods might reduce dependence on manual annotation for other interleaved reasoning formats.
  • Smaller models trained this way might close the gap with much larger text-only models on video benchmarks.

Load-bearing premise

The automated annotation pipeline produces high-quality multimodal CoT data that faithfully captures visual-textual interleaved reasoning suitable for training.

What would settle it

Train two otherwise identical models, one with standard text-only CoT and one with VTI-CoT, on the same video reasoning benchmarks; if the interleaved version shows no accuracy gain or no reduction in training steps to convergence, the central benefit would be refuted.

Figures

Figures reproduced from arXiv: 2606.05736 by Bairun Wang, Kunlin Yang, Lei Jin, Shufan Zhang, Xinzhu Ma, Xuanding Ding, Ziyue Lin.

Figure 1
Figure 1. Figure 1: VTI-CoT data generation pipeline. Our construction pipeline contains four stages: (1) Temporal segmentation: Segments video frames based on CLIP. (2) Interval￾level description: Generate detailed segment descriptions. (3) Answer-conditioned rea￾soning generation: Generate visual-textual interleaved CoT. (4) OCR rendering: Write CoT into a canvas, then feed the canvas into a vision encoder to generate token… view at source ↗
Figure 2
Figure 2. Figure 2: Dataset statistics of Video-R1 and MovieChat, including number of tokens and images per video sample. reasoning chains at the token level leads to linearly growing sequence length, in￾creased computational cost, and training instability. To obtain a compact and unified supervision signal, we render the structured instance into a single RGB canvas: I_{\mathrm {CoT}} = Render(\{(r_t, V_t)\}_{t=1}^{T}; \psi )… view at source ↗
Figure 3
Figure 3. Figure 3: Proposed VTI-CoT training framework. VTI-CoT integrates visual and textual information into an encoded feature by utilizing OCR method. In training stage, we first render image-text interleaved CoT content into a canvas, then encode this image through a general vision encoder, finally integrating this feature with CoT content generated by LLM. For each interleaved visual-textual CoT instance constructed in… view at source ↗
Figure 4
Figure 4. Figure 4: Training curves of rendered CoT versus tokenized CoT on MVBench and LongVideoBench. Results show that rendered CoT converges faster tan tokenized CoT in both short and long video benchmarks. tokenized and concatenated as standard multi-modal inputs. Such formulation provides multi-modal inputs, preserving integration of reasoning text and its corresponding visual cues. However, the resulting token sequence… view at source ↗
Figure 5
Figure 5. Figure 5: Prompt for interval-level visual description generation. A.2 Training Details Our training consists of a supervised fine-tuning (SFT) stage. Key hyperparam￾eters are summarized in [PITH_FULL_IMAGE:figures/full_fig_p021_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Prompt for answer-conditioned reasoning generation. – Vision Encoder: A transformer-based module that extracts patch-level fea￾tures from video frames (28×28 patches) and projects them into the language hidden space. Cross-attention layers in the language model allow fusion of visual and textual information. Design Rationale. We choose Qwen2.5-VL-7B due to its strong visual-textual alignment capabilities a… view at source ↗
Figure 7
Figure 7. Figure 7: Reasoning example generated by Qwen,Video-R1 and VTI-CoT [PITH_FULL_IMAGE:figures/full_fig_p023_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Rendered interleaved CoT canvas [PITH_FULL_IMAGE:figures/full_fig_p024_8.png] view at source ↗
read the original abstract

Video reasoning aims to understand complex temporal events and causal relationships within videos. Recently, Chain-of-Thought (CoT) has been introduced to this field to enhance reasoning accuracy. However, existing CoT-based video reasoning methods primarily rely on text-only information for logical deduction, overlooking critical visual information during the inference process. Inspired by the human cognitive mechanism of reviewing visual segments during inference, we propose VTI-CoT, a Visual-Textual Interleaved CoT framework. VTI-CoT integrates textual reasoning steps with corresponding visual frames. Given the scarcity of visual-textual interleaved CoT in existing datasets, we develop an automated annotation pipeline to construct high-quality multimodal CoT data. Further, reasoning over long-form videos entails increasingly long CoT token sequences, which severely hinders training convergence and efficiency. To address this, we employ Optical Character Recognition (OCR)-based compression techniques to compress CoT supervision signals into a single canvas. Experimental results demonstrate that VTI-CoT achieves state-of-the-art performance among models of the same parameter scale while significantly improving training efficiency.

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 / 1 minor

Summary. The paper proposes VTI-CoT, a Visual-Textual Interleaved Chain-of-Thought framework for video reasoning that augments textual reasoning steps with corresponding visual frames. Due to the lack of such interleaved data, the authors introduce an automated annotation pipeline to generate multimodal CoT supervision and apply OCR-based compression to condense long CoT token sequences into a single canvas for improved training efficiency. The central claim is that models trained with this approach achieve state-of-the-art performance among same-parameter-scale models while significantly improving training efficiency.

Significance. If the automated pipeline produces faithful interleaved reasoning data and the reported gains are reproducible, the work would provide a concrete mechanism for incorporating visual review during inference, addressing a gap in text-only CoT video methods. The OCR compression technique offers a practical solution to long-sequence training issues. These elements could influence future multimodal reasoning systems if the data quality is independently verified.

major comments (2)
  1. [Automated annotation pipeline] Automated annotation pipeline section: No quantitative validation metrics (human agreement scores, error rates, or comparison to manual CoT) are supplied for the generated visual-textual interleaved data. This is load-bearing for the SOTA and efficiency claims, as performance gains could arise from systematic artifacts in the pipeline rather than the VTI-CoT framework itself.
  2. [Experimental results] Experimental results section: The manuscript provides no tables or figures with specific metrics, baselines, dataset details, or ablations that isolate the contribution of interleaved visual-textual CoT versus the compression step or data volume. Without these, the claim of SOTA at fixed parameter scale cannot be verified as attributable to the proposed method.
minor comments (1)
  1. [Method] Notation for the OCR canvas compression could be clarified with an equation or pseudocode example showing how token sequences map to the single canvas.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation for major revision. We address each major comment below.

read point-by-point responses

  1. Referee: [Automated annotation pipeline] Automated annotation pipeline section: No quantitative validation metrics (human agreement scores, error rates, or comparison to manual CoT) are supplied for the generated visual-textual interleaved data. This is load-bearing for the SOTA and efficiency claims, as performance gains could arise from systematic artifacts in the pipeline rather than the VTI-CoT framework itself.

    Authors: We agree that the manuscript lacks quantitative validation metrics for the automated annotation pipeline. In the revised version we will add a dedicated evaluation subsection reporting human agreement scores, error rates, and direct comparisons against manually annotated CoT examples to confirm data fidelity. revision: yes

  2. Referee: [Experimental results] Experimental results section: The manuscript provides no tables or figures with specific metrics, baselines, dataset details, or ablations that isolate the contribution of interleaved visual-textual CoT versus the compression step or data volume. Without these, the claim of SOTA at fixed parameter scale cannot be verified as attributable to the proposed method.

    Authors: We acknowledge that the current manuscript does not present the requested level of experimental detail. The revision will include expanded tables and figures with concrete metrics, baseline comparisons, dataset specifications, and ablation studies that separately measure the contributions of the interleaved visual-textual CoT and the OCR compression step. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical method and experiments, no derivations or self-referential reductions

full rationale

The paper introduces VTI-CoT as a proposed framework that interleaves visual frames with textual CoT steps, constructs data via an automated annotation pipeline, and applies OCR compression for long sequences. All performance claims (SOTA at fixed scale, efficiency gains) are presented as outcomes of experiments on the resulting trained models. No equations, uniqueness theorems, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided text; the derivation chain is absent and the work is self-contained as a methodological proposal validated externally by reported results.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract only; no free parameters, axioms, or invented entities are described.

pith-pipeline@v0.9.1-grok · 5738 in / 1024 out tokens · 32949 ms · 2026-06-28T01:50:27.046252+00:00 · methodology

discussion (0)

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Reference graph

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    Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

    Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., Chi, E.: Least-to-most prompting enables complex reasoning in large language models (2023),https://arxiv.org/abs/2205.10625 VTI-CoT 21 A Appendix A.1 Dataset Details We provide the prompts used for interval-level visual description generation and ...

    work page internal anchor Pith review Pith/arXiv arXiv 2023