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arxiv: 2509.25699 · v3 · submitted 2025-09-30 · 💻 cs.CV

AIM-CoT: Active Information-driven Multimodal Chain-of-Thought for Vision-Language Reasoning

Xiping Li , Jianghong Ma This is my paper

Pith reviewed 2026-05-18 13:26 UTC · model grok-4.3

classification 💻 cs.CV
keywords multimodal chain-of-thoughtvision-language reasoningevidence selectionactive visual probingdynamic attention triggervisual question answeringinterleaved multimodal CoT
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The pith

AIM-CoT improves vision-language reasoning by using context-enhanced attention, active probing, and dynamic triggers to choose and time visual evidence insertions.

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

The paper seeks to strengthen interleaved multimodal chain-of-thought methods for tasks such as visual question answering. Current approaches rely on attention maps that become unreliable when short text queries meet rich images, and they use fixed rules for when to bring in visual details. AIM-CoT counters both problems through three targeted changes that first enrich attention maps with surrounding text context, then actively search for the most useful image patches via an information-seeking process, and finally insert those patches exactly when the model begins shifting focus toward visuals. A sympathetic reader would care because more reliable grounding of each reasoning step in actual image content could raise accuracy without changing the underlying vision-language model.

Core claim

AIM-CoT improves both evidence selection and insertion triggering in I-MCoT via CAG to mitigate granularity imbalance, AVP for proactive selection via information foraging, and DAT to activate insertions on attention shift, leading to consistent superiority across three benchmarks and four backbones.

What carries the argument

The AIM-CoT framework, which uses Context-enhanced Attention-map Generation (CAG) to reduce text-image detail mismatch, Active Visual Probing (AVP) to select patches through information foraging, and Dynamic Attention-shift Trigger (DAT) to time insertions when model focus moves to visuals.

If this is right

  • Reasoning chains become more grounded in specific image regions rather than generic attention patterns.
  • Insertion timing adapts automatically to each step of the model's internal focus shifts.
  • Performance lifts appear across different model sizes without retraining the base vision-language model.
  • The same selection and trigger logic can be applied to other interleaved multimodal tasks beyond VQA.

Where Pith is reading between the lines

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

  • Similar active selection and dynamic timing ideas could extend to pure language chain-of-thought by probing for external knowledge at attention shifts.
  • If the information-foraging step proves cheap, it might allow lighter models to match heavier ones on visual grounding without extra parameters.
  • The approach implicitly treats visual evidence as a limited resource whose cost is justified only at moments of genuine uncertainty.

Load-bearing premise

Attention signals from vision-language models are unreliable mainly because brief textual queries cannot match the detail level of images, and fixed rules for inserting visual evidence cannot adapt to the model's changing needs during reasoning.

What would settle it

Running the same three benchmarks with the four backbones and finding no consistent gains in final answer accuracy or in the quality of selected evidence patches would show the three new components do not deliver the claimed improvements.

Figures

Figures reproduced from arXiv: 2509.25699 by Jianghong Ma, Xiping Li.

Figure 1
Figure 1. Figure 1: The images of the 22nd question on LLaVA-W benchmark, which is a close-up photo of a meal at ICHIRAN. The left and right figures are respectively the original image and the first three sets of regions selected by the Top-K strategy (red, purple, and blue, respectively). A detailed explanation is shown in Appendix B. To acquire a holistic comprehension of these two questions, our empirical analysis is con￾d… view at source ↗
Figure 2
Figure 2. Figure 2: The visual￾ization of regions se￾lected by the informa￾tion gain-guided strat￾egy. To continue with the example in Section 3.1, [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: An overview of our AVP module, which iteratively selects [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: An illustration of the entire process of context enhancement by the CAG module, using [PITH_FULL_IMAGE:figures/full_fig_p016_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Experimental results of the sensitivity analysis of the hyper-parameter [PITH_FULL_IMAGE:figures/full_fig_p021_5.png] view at source ↗
read the original abstract

Interleaved-Modal Chain-of-Thought (I-MCoT) advances vision-language reasoning, such as Visual Question Answering (VQA). This paradigm integrates specially selected visual evidence from the input image into the context of Vision-Language Models (VLMs), enabling them to ground their reasoning logic in these details. Accordingly, the efficacy of an I-MCoT framework relies on identifying what to see (evidence selection) and when to see it (triggering of insertions). However, existing methods fall short in both aspects. First, for selection, they rely on attention signals, which are unreliable -- particularly under severe granularity imbalance between the brief textual query and the informative image. Second, for triggering, they adopt static triggers, which fail to capture the VLMs' dynamic needs for visual evidence. To this end, we propose a novel I-MCoT framework, Active Information-driven Multi-modal Chain-of-Thought (AIM-CoT), which aims to improve both evidence selection and insertion triggering via: (1) Context-enhanced Attention-map Generation (CAG) to mitigate granularity imbalance via textual context enhancement; (2) Active Visual Probing (AVP) to proactively select the most informative evidence via an information foraging process; and (3) Dynamic Attention-shift Trigger (DAT) to precisely activate insertions when VLM's attention shifts from text to visual context. Experiments across three benchmarks and four backbones demonstrate AIM-CoT's consistent superiority. Our code is available at https://anonymous.4open.science/r/AIMCoT.

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 presents AIM-CoT, an Active Information-driven Multimodal Chain-of-Thought framework for improving vision-language reasoning in tasks like VQA. Building on Interleaved-Modal Chain-of-Thought (I-MCoT), it targets two key challenges: unreliable attention-based evidence selection due to granularity imbalance between brief text queries and rich images, and static triggers for inserting visual evidence. The proposed solutions are Context-enhanced Attention-map Generation (CAG) for better attention via context, Active Visual Probing (AVP) for information foraging-based selection, and Dynamic Attention-shift Trigger (DAT) for dynamic activation on attention shifts. The framework is evaluated on three benchmarks using four different backbones, claiming consistent superiority, with code released.

Significance. This work has potential significance in advancing multimodal CoT reasoning by introducing active, information-driven mechanisms for evidence handling. The emphasis on dynamic triggering and proactive selection addresses real limitations in current VLM applications. Credit is given for releasing code, which aids reproducibility. If the empirical gains are robust and attributable to the specific components rather than added complexity, it could influence future I-MCoT designs. However, the overall impact depends on stronger validation of the core assumptions.

major comments (2)
  1. Abstract: The assumption that attention signals are unreliable due to granularity imbalance is central to motivating CAG, but the manuscript does not describe any controlled measurements (e.g., comparing attention entropy or focus concentration with and without CAG) to isolate this causal link, raising the possibility that gains arise from generic context enhancement instead.
  2. Abstract: No quantitative results, error bars, ablation details, or specific performance numbers are provided despite claims of 'consistent superiority,' which hinders assessment of the practical magnitude of improvements and the robustness of comparisons to baselines.
minor comments (1)
  1. The code repository link uses an anonymous service; consider providing a more permanent or standard GitHub link in the final version for better accessibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments and for recognizing the potential of AIM-CoT. We address each major comment point by point below, clarifying the manuscript's content and proposing targeted revisions where appropriate.

read point-by-point responses

  1. Referee: Abstract: The assumption that attention signals are unreliable due to granularity imbalance is central to motivating CAG, but the manuscript does not describe any controlled measurements (e.g., comparing attention entropy or focus concentration with and without CAG) to isolate this causal link, raising the possibility that gains arise from generic context enhancement instead.

    Authors: We appreciate this observation regarding the motivation for CAG. The abstract and introduction highlight the granularity imbalance between brief text queries and rich images as a source of unreliable attention signals, drawing from established observations in the VLM literature. While the abstract itself does not present quantitative controlled measurements such as attention entropy or focus concentration comparisons, the full manuscript includes attention visualizations and ablation studies that demonstrate CAG's contribution to more focused evidence selection. To directly address the concern and isolate the effect from generic context enhancement, we will add a concise description of these quantitative analyses to the abstract in the revised version. revision: yes

  2. Referee: Abstract: No quantitative results, error bars, ablation details, or specific performance numbers are provided despite claims of 'consistent superiority,' which hinders assessment of the practical magnitude of improvements and the robustness of comparisons to baselines.

    Authors: We agree that the abstract's high-level summary of 'consistent superiority' would benefit from greater specificity. The abstract is intentionally concise due to length constraints, but the manuscript provides detailed quantitative results, including performance numbers across three benchmarks and four backbones, along with ablation studies, in the experimental sections and tables. We will revise the abstract to incorporate key quantitative highlights and robustness indicators while retaining full details, error bars, and ablations in the main text. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical method proposal with independent experimental validation

full rationale

The paper presents AIM-CoT as an applied engineering framework consisting of three concrete components (CAG for context-enhanced attention, AVP for information-foraging selection, and DAT for dynamic triggering) to address practical shortcomings in existing I-MCoT methods. These components are motivated by stated assumptions about attention reliability and static triggers, then evaluated through comparative experiments on three benchmarks and four backbones. No equations, fitted parameters, or first-principles derivations appear in the provided text; the central claims rest on observed performance gains rather than any quantity that reduces by construction to its own inputs, self-citations, or renamed empirical patterns. The derivation chain is therefore self-contained as a standard empirical contribution.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated. The work is an empirical engineering proposal rather than a theoretical derivation.

pith-pipeline@v0.9.0 · 5809 in / 1119 out tokens · 27935 ms · 2026-05-18T13:26:40.750826+00:00 · methodology

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Forward citations

Cited by 1 Pith paper

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  1. Beyond Semantic Relevance: Counterfactual Risk Minimization for Robust Retrieval-Augmented Generation

    cs.CL 2026-05 unverdicted novelty 6.0

    CoRM-RAG uses a cognitive perturbation protocol to simulate biases and trains an Evidence Critic to retrieve documents that support correct decisions even under adversarial query changes.

Reference graph

Works this paper leans on

25 extracted references · 25 canonical work pages · cited by 1 Pith paper · 8 internal anchors

  1. [1]

    GPT-4 Technical Report

    Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774,

    work page internal anchor Pith review Pith/arXiv arXiv

  2. [2]

    M3cot: A novel bench- mark for multi-domain multi-step multi-modal chain-of-thought.arXiv preprint arXiv:2405.16473,

    Qiguang Chen, Libo Qin, Jin Zhang, Zhi Chen, Xiao Xu, and Wanxiang Che. M3cot: A novel bench- mark for multi-domain multi-step multi-modal chain-of-thought.arXiv preprint arXiv:2405.16473,

    work page arXiv

  3. [3]

    The free-energy principle: a unified brain theory? , volume =

    doi: 10.1038/nrn2787. 10 Under review as a conference paper at ICLR 2026 Jun Gao, Yongqi Li, Ziqiang Cao, and Wenjie Li. Interleaved-modal chain-of-thought. InProceedings of the Computer Vision and Pattern Recognition Conference, pp. 19520–19529,

    work page doi:10.1038/nrn2787 2026

  4. [4]

    Mrfd: Multi-region fusion decoding with self-consistency for mitigating hallucinations in lvlms.arXiv preprint arXiv:2508.10264,

    Haonan Ge, Yiwei Wang, Ming-Hsuan Yang, and Yujun Cai. Mrfd: Multi-region fusion decoding with self-consistency for mitigating hallucinations in lvlms.arXiv preprint arXiv:2508.10264,

    work page arXiv

  5. [5]

    What does bert learn about the structure of language? InACL 2019-57th Annual Meeting of the Association for Computational Linguistics,

    Ganesh Jawahar, Benoît Sagot, and Djamé Seddah. What does bert learn about the structure of language? InACL 2019-57th Annual Meeting of the Association for Computational Linguistics,

    work page 2019

  6. [6]

    Xuanyu Lei, Zonghan Yang, Xinrui Chen, Peng Li, and Yang Liu

    doi: 10.1145/1390681.1390689. Xuanyu Lei, Zonghan Yang, Xinrui Chen, Peng Li, and Yang Liu. Scaffolding coordinates to promote vision-language coordination in large multi-modal models.arXiv preprint arXiv:2402.12058,

    work page doi:10.1145/1390681.1390689

  7. [7]

    Yibing Liu, Yangyang Guo, Jianhua Yin, Xuemeng Song, Weifeng Liu, Liqiang Nie, and Min Zhang

    URL https:// llava-vl.github.io/blog/2024-01-30-llava-next/. Yibing Liu, Yangyang Guo, Jianhua Yin, Xuemeng Song, Weifeng Liu, Liqiang Nie, and Min Zhang. Answer questions with right image regions: A visual attention regularization approach.ACM Trans. Multimedia Comput. Commun. Appl., 18(4), March

    work page 2024

  8. [8]

    doi: 10.1145/3498340

    ISSN 1551-6857. doi: 10.1145/3498340. URLhttps://doi.org/10.1145/3498340. Chancharik Mitra, Brandon Huang, Trevor Darrell, and Roei Herzig. Compositional chain-of-thought prompting for large multimodal models. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14420–14431,

    work page doi:10.1145/3498340

  9. [9]

    Peter Pirolli and Stuart Card

    doi: 10.1037//0033-295X.101.4.608. Peter Pirolli and Stuart Card. Information foraging.Psychological review, 106(4):643,

    work page doi:10.1037//0033-295x.101.4.608

  10. [10]

    Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

    Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V Le, Ed H Chi, Denny Zhou, et al. Challenging big-bench tasks and whether chain-of-thought can solve them.arXiv preprint arXiv:2210.09261,

    work page internal anchor Pith review Pith/arXiv arXiv

  11. [11]

    Chameleon: Mixed-Modal Early-Fusion Foundation Models

    11 Under review as a conference paper at ICLR 2026 Chameleon Team. Chameleon: Mixed-modal early-fusion foundation models.arXiv preprint arXiv:2405.09818,

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  12. [12]

    arXiv preprint arXiv:1905.05950 , year=

    Ian Tenney, Dipanjan Das, and Ellie Pavlick. Bert rediscovers the classical nlp pipeline.arXiv preprint arXiv:1905.05950,

    work page arXiv 1905

  13. [13]

    Analyzing the Structure of Attention in a Transformer Language Model

    Jesse Vig and Yonatan Belinkov. Analyzing the structure of attention in a transformer language model.arXiv preprint arXiv:1906.04284,

    work page internal anchor Pith review Pith/arXiv arXiv 1906

  14. [14]

    Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

    Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, et al. Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution.arXiv preprint arXiv:2409.12191,

    work page internal anchor Pith review Pith/arXiv arXiv

  15. [15]

    Self-Consistency Improves Chain of Thought Reasoning in Language Models

    Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdh- ery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171,

    work page internal anchor Pith review Pith/arXiv arXiv

  16. [16]

    Automatic Chain of Thought Prompting in Large Language Models

    Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. Automatic chain of thought prompting in large language models.arXiv preprint arXiv:2210.03493,

    work page internal anchor Pith review Pith/arXiv arXiv

  17. [17]

    Multimodal Chain-of-Thought Reasoning in Language Models

    Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, and Alex Smola. Multimodal chain-of-thought reasoning in language models.arXiv preprint arXiv:2302.00923,

    work page internal anchor Pith review Pith/arXiv arXiv

  18. [18]

    arXiv preprint arXiv:2306.12156 (2023) 31

    Xu Zhao, Wenchao Ding, Yongqi An, Yinglong Du, Tao Yu, Min Li, Ming Tang, and Jinqiao Wang. Fast segment anything.arXiv preprint arXiv:2306.12156,

    work page arXiv

  19. [19]

    What’s the name of the restaurant serving these dishes?

    A LLM USAGESTATEMENT In the preparation of this manuscript, we utilized a large language model (LLM) as a general-purpose writing assistance tool. The primary uses of the LLM were twofold: • Grammatical Correction: The LLM was employed to proofread the manuscript for grammatical errors, spelling mistakes, and awkward phrasing. This helped improve the over...

    work page 2026

  20. [20]

    This is a multiple-choice question. The question is based on the image provided

    Algorithm 1:Greedy Algorithm for Optimal Region Selection Input:total candidate setC, size of optimal selectionK Output:optimal selectionS 1R ∗ ←∅ 2fork←1,2,· · ·, Kdo 3U B ←H(Y|I, x, y <t, R∗) 4fori←1,2,· · ·, N+Mdo 5U C,i ←H(Y|I, x, y <t, R∗ ∪ {Ri}) 6IG({R i})←U B −U C,i 7R next ←argmax Ri∈C\R ∗ {IG({R i})} 8R ∗ ←R ∗ ∪ {Rnext} 9S←R ∗ 10returnS F DETAILE...

    work page 2025

  21. [21]

    Grid size

    For easier understanding, we provide further explanation of the hyper-parameters sr and sg as follows: in the A VP module, the input image is first divided into sg ×s g grids according to the set "Grid size" sg. Then, each region finally selected by A VP consists ofsr ×s r grids. H EMPIRICALANALYSIS OF THEAPPROXIMATESUBMODULARITY OF FUNCTIONF To offer a m...

    work page 2017

  22. [22]

    information gain

    As we can see, the Inequality 15 holds in most instances across all settings and datasets. Furthermore, to confirm the significance of the obtained results, we introduce the Binomial Test, an exact statistical procedure for assessing the extent to which experimental outcomes with a binary structure are attributable to chance alone. The p-values, presented...

    work page 2017

  23. [23]

    even when these hyper-parameters are kept at low levels (e.g., K= 3, N C = 6, which is the default setting). In conclusion, we can approximate the complexity of A VP as follows: O(K·N C ·L·∆N·(N+K·∆N)·H).(19) This demonstrates thatthe combination of architectural optimizations (batching and KV caching) and the high extractive efficiency of A VP ensures th...

    work page 2025

  24. [24]

    First, the A VP module does not introduce significant temporal costs to the AIMCoT framework, an efficiency attributable to batch processing and the KV Cache mechanism

    Two key observations can be drawn. First, the A VP module does not introduce significant temporal costs to the AIMCoT framework, an efficiency attributable to batch processing and the KV Cache mechanism. Second, AIMCoT achieves substantially superior performance as shown in Table 2, at a time cost comparable to that of the efficient baseline, which is les...

    work page 2026

  25. [25]

    The left figure illustrates that AIMCoT exhibits limited performance when the threshold, δ, is set too low. This underscores the importance of inserting visual information at critical moments: excessively frequent or inopportune visual insertions can disrupt the VLM’s reasoning process, leading to suboptimal performance. As δ increases, the model’s perfor...

    work page 2026