OMHBench: Benchmarking Balanced and Grounded Omni-Modal Multi-Hop Reasoning

Changhyeon Kim; Ingyu Bang; Jihun Choi; Richeng Xuan; Sanghwan Bae; Seokgyu Jang; Seunghee Kim; Taeuk Kim

arxiv: 2508.16198 · v3 · submitted 2025-08-22 · 💻 cs.CL

OMHBench: Benchmarking Balanced and Grounded Omni-Modal Multi-Hop Reasoning

Seunghee Kim , Ingyu Bang , Seokgyu Jang , Changhyeon Kim , Sanghwan Bae , Jihun Choi , Richeng Xuan , Taeuk Kim This is my paper

Pith reviewed 2026-05-18 21:20 UTC · model grok-4.3

classification 💻 cs.CL

keywords OMHBenchomni-modal reasoningmulti-hop reasoningbenchmarkmultimodal modelsgrounded reasoningspeech modalityreasoning path sensitivity

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The pith

A benchmark with balanced reasoning paths across text, vision, and speech reveals large performance gaps and path sensitivity in current models.

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

The paper creates OMHBench to address limitations in prior evaluations of models that process text, vision, and speech, where shortcuts and biased paths often let models avoid true integration. It offers 6,144 questions built so that reasoning must draw jointly from all three modalities in balanced ways. Testing 13 leading models finds that proprietary versions outperform open-source ones, yet even the stronger models change results sharply when the reasoning path shifts, producing uneven grounding across modalities. Models show particular weakness with speech input. This matters because it indicates that many models may not yet combine information from different sources symmetrically, which limits their usefulness for complex real-world queries that mix modalities.

Core claim

OMHBench consists of 6,144 questions with balanced reasoning paths that are jointly grounded across text, vision, and speech. Evaluation of 13 state-of-the-art models shows a large performance gap between proprietary and open-source models and high sensitivity to reasoning path variations, resulting in asymmetric omni-modal grounding, with models struggling especially on the speech modality.

What carries the argument

The balanced reasoning paths in OMHBench that require joint grounding across text, vision, and speech without allowing modality shortcuts.

If this is right

A large performance gap exists between proprietary and open-source models on these tasks.
Even proprietary models remain highly sensitive to changes in reasoning path order or emphasis.
This sensitivity produces asymmetric grounding, so models do not draw on modalities evenly.
Current models struggle particularly when the speech modality must be used.
Existing evaluation frameworks with biased paths fail to measure true omni-modal capability.

Where Pith is reading between the lines

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

Training approaches could add more examples that force consistent results no matter the path order.
The benchmark could guide targeted fixes for weak modality integration in future models.
Similar balance requirements might strengthen tests in other mixed-input domains such as video with narration.

Load-bearing premise

The 6,144 questions achieve truly balanced reasoning paths that are jointly grounded across the three modalities without introducing shortcuts or new biases.

What would settle it

Demonstrating that models achieve consistent performance across different reasoning path variations on the benchmark questions.

read the original abstract

Multimodal Large Language Models (MLLMs) have increasingly supported omni-modal processing across text, vision, and speech. However, existing evaluation frameworks for such models suffer from critical limitations, including modality shortcuts and biased reasoning paths. To address these challenges, we propose OMHBench, a novel benchmark designed to rigorously evaluate omni-modal multi-hop reasoning. It consists of 6,144 questions with balanced reasoning paths that are jointly grounded across all three modalities. Extensive evaluation of 13 state-of-the-art models reveals that (1) a large performance gap exists between proprietary and open-source MLLMs and (2) even proprietary models exhibit high sensitivity to reasoning path variations, resulting in asymmetric omni-modal grounding. Notably, models struggle when processing the speech modality, underscoring the need for balanced, multi-hop evaluation of omni-modal intelligence.

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.

Desk Editor's Note private letter to a colleague

OMHBench adds a benchmark for joint text-vision-speech multi-hop reasoning and flags real weaknesses in current models, but the construction details are too light to fully back the balance claims.

read the letter

OMHBench puts forward 6,144 questions meant to force models to use text, vision, and speech together in multi-hop chains without easy shortcuts from any one modality. The evaluation of 13 models shows a clear gap between proprietary and open-source systems, plus high sensitivity to how the reasoning path is varied and notably weaker performance on speech. That is the core new piece: a deliberate attempt at balanced, jointly grounded questions across three modalities rather than the usual text-heavy or vision-heavy setups. The paper does a solid job of running the models and surfacing these patterns, which line up with what many people already suspect about current MLLMs. It gives a useful snapshot of where the field stands on integrated reasoning. The soft spot is the missing evidence on construction. The abstract asserts balance and no modality shortcuts, but without numbers on per-modality contribution rates, path-length distributions, or ablation results that show equal drops when any one modality is removed, it is hard to know whether the reported sensitivities are genuine or partly an artifact of how the questions were written. If the full paper has those checks and they hold up, the findings strengthen; if not, the asymmetry claims rest on an unverified assumption. The work is aimed at people building or testing omni-modal systems who care about evaluation that matches real mixed-input use cases. A reader who wants concrete numbers on current model gaps will get something from it. I would send this to peer review because the idea is timely and the model results are worth discussing, though the referee would need to press on the validation steps before the benchmark can be trusted as a standard.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces OMHBench, a benchmark consisting of 6,144 questions designed to evaluate omni-modal multi-hop reasoning in MLLMs across text, vision, and speech with balanced and jointly grounded reasoning paths. It reports results from evaluating 13 state-of-the-art models, claiming a large performance gap between proprietary and open-source MLLMs, high sensitivity to reasoning path variations even among proprietary models that produces asymmetric omni-modal grounding, and particular difficulties when processing the speech modality.

Significance. If the benchmark's questions are verifiably balanced and free of modality shortcuts or new biases, the work would offer a useful advance in rigorous evaluation of omni-modal capabilities, helping to diagnose current model limitations in multi-hop cross-modal reasoning and motivating improvements in speech handling and path robustness.

major comments (2)

[Abstract] Abstract: the assertion that the 6,144 questions have 'balanced reasoning paths that are jointly grounded across all three modalities' without modality shortcuts is presented without any supporting metrics (e.g., per-modality contribution frequencies, path-length distributions, or ablation results when individual modalities are masked), which is load-bearing for the central claims about performance gaps and asymmetric grounding.
[Evaluation] Evaluation section: the reported high sensitivity to reasoning path variations and asymmetric omni-modal grounding in proprietary models rests on the assumption that the benchmark paths are balanced and jointly grounded; without explicit construction details or validation that no modality shortcuts were introduced, these findings risk being artifacts of the dataset design rather than robust evidence of omni-modal deficits.

minor comments (2)

[Dataset Construction] Consider adding a dedicated subsection or appendix with quantitative statistics on reasoning path balance and modality grounding to strengthen the benchmark description.
[Experiments] Ensure all model names, versions, and prompting details are listed consistently in the experimental setup for reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their careful reading and constructive comments on OMHBench. The feedback highlights important points about the need for more explicit quantitative support for the benchmark's balance and grounding properties. We address each major comment below and indicate the revisions we will incorporate.

read point-by-point responses

Referee: [Abstract] Abstract: the assertion that the 6,144 questions have 'balanced reasoning paths that are jointly grounded across all three modalities' without modality shortcuts is presented without any supporting metrics (e.g., per-modality contribution frequencies, path-length distributions, or ablation results when individual modalities are masked), which is load-bearing for the central claims about performance gaps and asymmetric grounding.

Authors: The abstract is intentionally concise. Section 3 of the manuscript describes the benchmark construction pipeline, including the multi-stage process used to generate questions with balanced modality contributions and to verify joint grounding without introducing shortcuts (e.g., through iterative human verification and cross-modal consistency checks). To directly address the concern and make this evidence more accessible, we will revise the abstract to reference key supporting statistics and add a new table in the main text reporting per-modality contribution frequencies, path-length distributions, and results from modality-masking ablations. These additions will provide the requested empirical validation for the performance gap and asymmetric grounding observations. revision: yes
Referee: [Evaluation] Evaluation section: the reported high sensitivity to reasoning path variations and asymmetric omni-modal grounding in proprietary models rests on the assumption that the benchmark paths are balanced and jointly grounded; without explicit construction details or validation that no modality shortcuts were introduced, these findings risk being artifacts of the dataset design rather than robust evidence of omni-modal deficits.

Authors: The evaluation in Section 4 controls for path variations by generating alternative orderings and modality emphases over the same underlying facts, with grounding verified during dataset creation. We acknowledge that additional quantitative validation would strengthen the interpretation. In the revision we will expand the Evaluation section with further construction details and include new analyses (such as information contribution metrics per modality and shortcut detection ablations) to demonstrate that the observed sensitivities are not artifacts of the design. This will better isolate model limitations in omni-modal reasoning. revision: partial

Circularity Check

0 steps flagged

No circularity: benchmark proposal with independent dataset construction and external evaluations

full rationale

The paper introduces OMHBench as a new dataset of 6,144 questions for omni-modal multi-hop reasoning and reports empirical results from evaluating 13 MLLMs. No mathematical derivations, equations, fitted parameters, or predictions appear in the provided text. Claims about performance gaps and sensitivity rest directly on the new benchmark construction and model runs rather than reducing to prior self-citations, ansatzes, or inputs by construction. The work is self-contained against external benchmarks and does not invoke uniqueness theorems or rename known results in a circular fashion.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central contribution rests on the assumption that the newly created questions achieve genuine balance and grounding across modalities; no free parameters or invented entities are introduced.

axioms (1)

domain assumption The constructed questions provide balanced reasoning paths that are jointly grounded across text, vision, and speech without modality shortcuts.
This premise is required for the benchmark to validly measure the claimed sensitivities and performance gaps.

pith-pipeline@v0.9.0 · 5703 in / 1292 out tokens · 50524 ms · 2026-05-18T21:20:59.947151+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/AlexanderDuality.lean alexander_duality_circle_linking unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

CMR-SPB ... balanced reasoning paths that are jointly grounded across all three modalities ... six reasoning paths
IndisputableMonolith/Foundation/ArithmeticFromLogic.lean LogicNat induction unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

Path Balance Score (PBS) ... macro-average and standard deviation over N! reasoning paths

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