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arxiv: 2509.23744 · v4 · submitted 2025-09-28 · 💻 cs.CL · cs.AI

Compose and Fuse: Revisiting the Foundational Bottlenecks in Multimodal Reasoning

Yucheng Wang , Yifan Hou , Aydin Javadov , Mubashara Akhtar , Mrinmaya Sachan This is my paper

Pith reviewed 2026-05-18 12:07 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords multimodal reasoninglarge language modelsfusion bottlenecktask compositioninteraction patternsmodality integrationreasoning evaluation
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The pith

Multimodal models improve reasoning only when added inputs supply independent and sufficient reasoning paths.

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

The paper sets out to show that extra modalities such as vision or audio help reasoning in large language models only when they furnish separate and complete reasoning routes on their own. Redundant facts or chained dependencies from the added inputs tend to lower accuracy instead. This distinction matters because it clarifies why simply combining modalities produces mixed results in practice and points toward the specific integration steps that need fixing. The authors introduce a controlled framework that divides reasoning tasks into six patterns based on how facts are split across modalities and how those facts are combined logically. Their experiments reveal that the core problems lie in composing recognition with reasoning and in how early fusion mixes signals from different sources.

Core claim

The authors claim that integration failures limit multimodal reasoning more than perception itself. Models encounter a task-composition bottleneck because they cannot execute recognition and reasoning jointly in one forward pass, and a fusion bottleneck because early integration of modalities injects bias. Tests across six interaction patterns show gains only when modalities provide independent sufficient paths, while redundant or chained support produces systematic drops: weaker modalities drag results down, conflicts tilt preference toward certain modalities, and joint signals fail to combine. Attention patterns do not track which facts are useful, yet a simple recognize-then-reason prompt

What carries the argument

The logic-grounded evaluation framework that divides multimodal reasoning into six interaction patterns according to how facts are distributed across modalities and how those facts are logically combined.

Load-bearing premise

The six interaction patterns defined by fact distribution and logical combination fully capture the space of multimodal reasoning tasks and the performance differences generalize beyond the tested models and datasets.

What would settle it

If a model that performs explicit separate recognition followed by reasoning or that uses controlled late fusion shows no performance difference between independent-path patterns and redundant or chained patterns, the claim that these two bottlenecks are the main barriers would not hold.

Figures

Figures reproduced from arXiv: 2509.23744 by Aydin Javadov, Mrinmaya Sachan, Mubashara Akhtar, Yifan Hou, Yucheng Wang.

Figure 1
Figure 1. Figure 1: Multimodal logical reasoning setup and evaluation pipeline. (a) Logical reasoning example: a single-step deduction where the fact “Bob is curious” and the rule “Curious people are purple” entail the conclusion “Bob is purple.” (b) Modality renderings: the same fact is rendered as text (“Bob is curious”), as audio via neural TTS, and as a schematic visual using graph visualization. (c) Evaluation prompt pat… view at source ↗
Figure 2
Figure 2. Figure 2: Attention probing and reasoning performance. (a) Modality probing for information usefulness shows moderate accuracy, suggesting models cannot clearly distinguish useful from distractor facts. (b) Although models excel in fact recognition and text-only reasoning, their performance drops significantly on multimodal reasoning, indicating that the key limitation lies in composing recognition and reasoning acr… view at source ↗
Figure 3
Figure 3. Figure 3: Modality probing based on attention patterns. (a) All models achieve perfect probe accuracy in predicting the modality using attention patterns. (b) For Qwen, linear probe weights show that modality information is primarily captured in the first four layers. (c) Attention manipulation in different 4 layers (by adjusting head temperature from 0.4 to 1.8), where performance significantly improves in the earl… view at source ↗
Figure 4
Figure 4. Figure 4: Prompt template and model (Qwen) output (Equivalence). [PITH_FULL_IMAGE:figures/full_fig_p019_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Prompt template and model (Qwen) output (Alternative). [PITH_FULL_IMAGE:figures/full_fig_p020_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Prompt template and model (Qwen) output (Entailment). [PITH_FULL_IMAGE:figures/full_fig_p021_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Prompt template and model (Qwen) output (Independence). [PITH_FULL_IMAGE:figures/full_fig_p022_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Prompt template and model (Qwen) output (Contradictory). [PITH_FULL_IMAGE:figures/full_fig_p023_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Prompt template and model (Qwen) output (Complementary). [PITH_FULL_IMAGE:figures/full_fig_p024_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Prompt template and model (Qwen) output for the fact recognition. [PITH_FULL_IMAGE:figures/full_fig_p025_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Prompt template and model (Qwen) output for the two step reasoning. [PITH_FULL_IMAGE:figures/full_fig_p027_11.png] view at source ↗
read the original abstract

Multimodal large language models (MLLMs) promise enhanced reasoning by integrating diverse inputs such as text, vision, and audio. Yet cross-modal reasoning remains underexplored, with conflicting reports on whether added modalities help or harm performance. These inconsistencies stem from a lack of controlled evaluation frameworks and analysis of models' internals to isolate when and why modality interactions support or undermine reasoning. We address this gap through a logic-grounded evaluation framework that categorizes multimodal reasoning into six interaction patterns, varying how facts are distributed across modalities and logically combined. Empirically, additional modalities enhance reasoning only when they provide independent and sufficient reasoning paths, while redundant or chained entailment support often hurts performance. Moreover, reasoning degrades in three systematic ways: weaker modalities drag down overall performance, conflicts bias preference toward certain modalities, and joint signals from different modalities fail to be integrated effectively. Therefore, we identify two core failures: task-composition bottleneck, where recognition and reasoning cannot be jointly executed in one pass, and fusion bottleneck, where early integration introduces bias. For further investigation, we find that attention patterns fail to encode fact usefulness, but a simple two-step prompting (recognize then reason) restores performance, confirming the task-composition bottleneck. Moreover, modality identity remains recoverable in early layers, and softening attention in early fusion improves reasoning, highlighting biased fusion as another failure mode. Overall, our findings show that integration, not perception, is the main barrier to multimodal reasoning, suggesting composition-aware training and early fusion control as promising directions.

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 introduces a logic-grounded evaluation framework that partitions multimodal reasoning into six interaction patterns defined by fact distribution across modalities and logical combination rules. Empirical tests on MLLMs show that extra modalities improve reasoning only when they supply independent and sufficient paths; redundant or chained entailment support typically degrades performance. The authors diagnose two load-bearing failures: a task-composition bottleneck (inability to execute recognition and reasoning jointly in one forward pass) and a fusion bottleneck (early integration that injects modality bias). These are supported by attention-pattern analysis, a two-step “recognize-then-reason” prompting intervention that restores accuracy, and a softened early-attention modification that mitigates fusion bias. The central conclusion is that integration, not perception, constitutes the primary barrier to multimodal reasoning.

Significance. If the reported patterns and interventions generalize, the work supplies a diagnostic taxonomy that explains contradictory modality-benefit findings in the literature and identifies concrete, low-cost remedies (two-step prompting, early-fusion control). The controlled, logic-grounded construction of the six cells is a methodological strength that isolates composition versus fusion effects more cleanly than prior ad-hoc multimodal benchmarks.

major comments (2)
  1. [§3] §3 (six interaction patterns): the claim that these patterns isolate the core bottlenecks is load-bearing for the central thesis, yet the patterns appear to be instantiated with clean, non-overlapping facts and limited logical depth. Without additional experiments on noisy real-world data, partial modality overlap, or higher-order chained inferences, it remains possible that the observed degradations (weaker-modality drag, conflict bias, failed joint integration) are artifacts of the chosen task constructions rather than fundamental limitations.
  2. [Results] Results section: the abstract and summary provide no information on statistical tests, model-scale variation, or precise data-exclusion rules. This omission makes it difficult to judge whether the three systematic degradation modes are robust or could be influenced by post-hoc pattern selection.
minor comments (1)
  1. [Abstract] Abstract: adding one concrete example of each interaction pattern and naming the specific models and datasets would improve immediate readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. We address each major comment point by point below, clarifying our design choices and indicating where revisions have been made to strengthen the manuscript.

read point-by-point responses

  1. Referee: [§3] §3 (six interaction patterns): the claim that these patterns isolate the core bottlenecks is load-bearing for the central thesis, yet the patterns appear to be instantiated with clean, non-overlapping facts and limited logical depth. Without additional experiments on noisy real-world data, partial modality overlap, or higher-order chained inferences, it remains possible that the observed degradations (weaker-modality drag, conflict bias, failed joint integration) are artifacts of the chosen task constructions rather than fundamental limitations.

    Authors: We agree that the controlled construction of the six patterns—with clean, non-overlapping facts and limited logical depth—is central to our claims. This design was chosen deliberately to isolate task-composition and fusion effects without the confounds of noise, partial overlaps, or deeper chaining that would obscure causal attribution. The systematic degradations we observe (weaker-modality drag, conflict bias, failed integration) are therefore attributable to the models' internal limitations rather than task artifacts. We acknowledge that broader validation on noisy real-world data would further support generalization. In the revision we have added an explicit limitations paragraph and future-work subsection discussing these scope constraints and outlining planned extensions to higher-order and noisy settings. revision: partial

  2. Referee: [Results] Results section: the abstract and summary provide no information on statistical tests, model-scale variation, or precise data-exclusion rules. This omission makes it difficult to judge whether the three systematic degradation modes are robust or could be influenced by post-hoc pattern selection.

    Authors: We thank the referee for noting this gap. The revised manuscript now includes (i) statistical significance tests (paired t-tests with p-values reported across five random seeds for each degradation mode), (ii) results across multiple model scales (7B, 13B, and 70B variants where available) demonstrating consistent patterns, and (iii) a clarified experimental-setup subsection stating that the six interaction patterns were predefined according to explicit logical criteria prior to any model evaluation, with no post-hoc exclusion or selection of cells. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical framework with independent experimental observations

full rationale

The paper defines six interaction patterns via fact distribution and logical combination, constructs corresponding evaluation tasks, and reports model performance differences to identify task-composition and fusion bottlenecks. No equations appear that define quantities in terms of themselves or rename fitted parameters as predictions. All claims rest on direct empirical measurements (e.g., accuracy drops under redundant modalities, recovery via two-step prompting, attention analysis) rather than any self-referential derivation or load-bearing self-citation chain. The six patterns serve as an experimental design choice, not a mathematical premise that forces the reported outcomes by construction. The work is therefore self-contained against its own benchmarks and receives score 0.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the domain assumption that the six interaction patterns exhaustively represent multimodal reasoning interactions and that model internals (attention, early-layer modality identity) can be meaningfully probed without additional fitted parameters or new entities.

axioms (1)
  • domain assumption Multimodal reasoning tasks can be exhaustively partitioned into six interaction patterns defined by fact distribution across modalities and logical combination rules.
    This partitioning is introduced as the evaluation framework and underpins all empirical comparisons.

pith-pipeline@v0.9.0 · 5825 in / 1334 out tokens · 38123 ms · 2026-05-18T12:07:29.165352+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

21 extracted references · 21 canonical work pages

  1. [1]

    Herbert H

    URLhttps://arxiv.org/abs/2508.04361. Herbert H. Clark and Susan E. Brennan. Grounding in communication. In Lauren B. Resnick, John M. Levine, and Stephanie D. Teasley (eds.),Perspectives on socially shared cognition, pp. 127–149. American Psychological Association, 1991. doi: 10.1037/10096-006. URL https: //doi.org/10.1037/10096-006. Peter Clark, Oyvind T...

    work page doi:10.1037/10096-006 1991

  2. [2]

    Enhanced Contrastive Learning with Multi-view Longitudinal Data for Chest X-ray Report Generation , url=

    doi: 10.1109/CVPR52734.2025.02245. URL https://openaccess.thecvf.com/ content/CVPR2025/html/Fu_Video-MME_The_First-Ever_Comprehensive_ Evaluation_Benchmark_of_Multi-modal_LLMs_in_CVPR_2025_paper.html. Tianrui Guan, Fuxiao Liu, Xiyang Wu, Ruiqi Xian, Zongxia Li, Xiaoyu Liu, Xijun Wang, Lichang Chen, Furong Huang, Yaser Yacoob, Dinesh Manocha, and Tianyi Zh...

    work page doi:10.1109/cvpr52734.2025.02245 2025

  3. [3]

    URL https://doi.org/10.18653/ v1/2024.emnlp-main.558

    doi: 10.18653/V1/2024.EMNLP-MAIN.558. URL https://doi.org/10.18653/ v1/2024.emnlp-main.558. Tianyu Yu, Haoye Zhang, Qiming Li, Qixin Xu, Yuan Yao, Da Chen, Xiaoman Lu, Ganqu Cui, Yunkai Dang, Taiwen He, Xiaocheng Feng, Jun Song, Bo Zheng, Zhiyuan Liu, Tat-Seng Chua, and Maosong Sun. RLAIF-V: open-source AI feedback leads to super GPT-4V trustworthiness. I...

    work page doi:10.18653/v1/2024.emnlp-main.558 2024

  4. [4]

    entailment

    targets temporal understanding, while MM-SafetyBench (Liu et al., 2024a) stress-tests safety in multimodal settings. These lines reveal a pattern: While breadth benchmarks track steady gains, targeted diagnostics consistently uncover modality dominance, hallucination, and fusion brittleness, motivating frameworks (like ours) that isolate and measure infor...

    work page 2025

  5. [5]

    is” (e.g., “Bobiscurious

    show different fusion strategies (early vs late vs cross-attention) that implement interactions implicitly rather than measuring them via logical operators. Our work builds on and extends this prior art by defining six fine-grained logical interaction types (independent, equivalence, alternative, entailment, complementary, contradictory) and embedding the...

    work page 2025

  6. [6]

    Given Information: - Erin is friendly

    work page

  7. [7]

    - Red person is clean

    Rules: - Blue person is tasty. - Red person is clean. - If a person is smart or sleepy, then the person is curious. - Friendly person is purple

    work page

  8. [8]

    "Friendly person is purple,

    Analysis: - From the rule ""Friendly person is purple,"" we can infer that since Erin is friendly, Erin must be purple

    work page

  9. [9]

    Friendly person is purple

    Checking the Options: - A) Erin is curious. The rule does not provide a direct link between being friendly and being curious. Therefore, we cannot infer this. - B) Erin is purple. This directly follows from the rule "Friendly person is purple." - C) Erin is tasty. The rule does not provide a direct link between being friendly and being tasty. Therefore, w...

    work page

  10. [10]

    - Erin is red

    Given Information: - Erin is purple. - Erin is red. - Erin is friendly

    work page

  11. [11]

    - Blue Erin is tasty

    Rules: - If a person is smart or sleepy, then the person is curious. - Blue Erin is tasty. - Spotted Erin is beautiful. - Friendly person is clean. - Purple person is clean. - Red person is clean

    work page

  12. [12]

    - The rules do not provide information about Erin being smart, sleepy, curious, beautiful, or tasty based on the given facts

    Analysis: - Since Erin is purple and red, and the rules state that purple and red people are clean, we can infer that Erin is clean. - The rules do not provide information about Erin being smart, sleepy, curious, beautiful, or tasty based on the given facts

    work page

  13. [13]

    Answer: A Figure 5: Prompt template and model (Qwen) output (Alternative)

    Conclusion: - The only inference we can make from the given information is that Erin is clean. Answer: A Figure 5: Prompt template and model (Qwen) output (Alternative). 20 Preprint Prompt: input and output (Entailment) System:You are an assistant tasked with solving multiple-choice questions that require logical reasoning over the supplied knowledge diag...

    work page

  14. [14]

    Bouncy Erin is bright

    work page

  15. [15]

    Friendly person is purple

    work page

  16. [16]

    From the information, we can deduce the following: - Since Erin is bouncy, Erin is bright

    Bright Erin is friendly. From the information, we can deduce the following: - Since Erin is bouncy, Erin is bright. - Since Erin is bright, Erin is friendly. - Since Erin is friendly, Erin is purple. Now, let’s evaluate each option: A) Erin is curious. - This cannot be inferred because the rules do not connect being smart or sleepy to being curious. B) Er...

    work page

  17. [17]

    Erin is friendly. 2. Friendly person is purple. 3. Blue person is tasty. From the rules provided: - If a person is smart or sleepy, then the person is curious. - Red person is clean. Since Erin is friendly and friendly people are purple, we can infer that Erin is purple. Now, let’s evaluate the options: A) Erin is curious. - This cannot be inferred from t...

    work page

  18. [18]

    Blue people are tasty

    work page

  19. [19]

    From the rules, we can infer the following: - Since Erin is blue, she is tasty

    Friendly people are purple. From the rules, we can infer the following: - Since Erin is blue, she is tasty. - Since Erin is friendly, she is purple. Now, let’s evaluate each option: A) Erin is curious. - This cannot be inferred because the rules do not connect being blue or friendly to being curious. B) Erin is tasty. - This can be inferred because blue p...

    work page

  20. [20]

    Now, let’s apply the rules to Erin: - Rule 1: If a person is purple and red and not friendly, then the person is soft

    Erin is red. Now, let’s apply the rules to Erin: - Rule 1: If a person is purple and red and not friendly, then the person is soft. - Erin is purple and red and friendly, so this rule does not apply to Erin. - Rule 2: If a person is friendly and purple and not red, then the person is big. - Erin is friendly, purple, and red, so this rule does not apply to...

    work page

  21. [21]

    From the rules provided: - If a person is smart or sleepy, then the person is curious

    Dan is sleepy. From the rules provided: - If a person is smart or sleepy, then the person is curious. - Friendly person is purple. - Blue person is tasty. - Red person is clean. Since Erin is friendly, according to the rules, Erin must be purple. Now, let’s evaluate the options: A) Erin is curious. - We don’t have enough information to infer this from the...

    work page