arxiv: 2602.11509 · v2 · submitted 2026-02-12 · 💻 cs.CL · cs.AI· cs.CV

Recognition: 1 theorem link

· Lean Theorem

Multimodal Fact-Level Attribution for Verifiable Reasoning

David Wan , Han Wang , Ziyang Wang , Elias Stengel-Eskin , Hyunji Lee , Mohit Bansal

Authors on Pith no claims yet

Pith reviewed 2026-05-16 03:56 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.CV

keywords multimodal reasoningfact attributioncitation hallucinationMLLM evaluationgrounded generationverifiable reasoningbenchmark

0 comments

The pith

Multimodal models often hallucinate citations even when their reasoning reaches the correct answer.

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

The paper presents MuRGAt, a benchmark that requires multimodal models to produce step-by-step reasoning together with precise citations that name both the input modality and the relevant time segments. It demonstrates that current strong models frequently invent these citations even when the final answer is factually right. The work also documents a concrete trade-off in which greater reasoning depth or enforced citation structure tends to lower overall accuracy. An automatic scorer is supplied that tracks human judgments closely enough to support large-scale testing. The result points to a persistent separation between a model’s internal reasoning and its capacity to link claims back to verifiable sources in heterogeneous inputs.

Core claim

Even advanced multimodal large language models produce correct answers while hallucinating the supporting citations, and attempts to increase reasoning depth or impose structured grounding formats reduce accuracy rather than improve it. The MuRGAt benchmark enforces explicit reasoning chains and requires each factual claim to be paired with a citation that specifies modality and temporal location; an automatic evaluation framework that correlates strongly with human ratings is used to measure this attribution quality at scale.

What carries the argument

The MuRGAt benchmark, which forces models to output explicit reasoning steps accompanied by citations that identify both the source modality and the precise temporal segments within inputs such as video and audio.

If this is right

Applications that need traceable multimodal reasoning cannot yet rely on existing models without external verification.
Training regimes that reward deeper reasoning chains may unintentionally increase citation errors.
Structured output constraints can trade off against factual correctness in current architectures.
Scalable automatic scorers now exist that can track progress on attribution without requiring full human review for every test.

Where Pith is reading between the lines

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

Real-world deployment in domains such as video search or audio analysis may require auxiliary verification modules that the base model itself cannot supply.
The observed trade-off suggests that next-token training alone may be insufficient to produce models that treat attribution as part of the reasoning process.
Extending the benchmark to new modality combinations could test whether the hallucination pattern generalizes or remains tied to particular input types.

Load-bearing premise

The automatic evaluation framework accurately measures fact-level attribution quality without systematic bias introduced by the way the benchmark examples were constructed.

What would settle it

A model trained or prompted to produce accurate modality-and-time citations that achieves high attribution scores on MuRGAt while preserving or improving answer accuracy, or a large-scale human study that finds consistent disagreement with the automatic scores.

Figures

Figures reproduced from arXiv: 2602.11509 by David Wan, Elias Stengel-Eskin, Han Wang, Hyunji Lee, Mohit Bansal, Ziyang Wang.

**Figure 1.** Figure 1: Overview of MURGAT and the evaluation protocol. The model is given a question and multimodal sources and is asked to generate a response containing explicit reasoning and precise citations, including the specific modality and timestamp. To evaluate the response, we apply a fact-level multimodal attribution protocol. The generated response and its citations are processed through three subtasks: (1) verifiab… view at source ↗

**Figure 2.** Figure 2: Gemini models’ performance with different thinking levels. MuRGAT-Score Accuracy 45 50 55 60 65 70 75 80 Base + Citation Logic + Dec. Logic + Imp. Narr. + Dec. Narr. + Imp [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗

**Figure 3.** Figure 3: Gemini-3-Flash results with program-aided generation on Worldsense. As shown in [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

**Figure 5.** Figure 5: Annotation UI for Attribution [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗

**Figure 6.** Figure 6: Comparative analysis of Gemini 2.5 Flash and Gemini 3 Pro. While Pro attempts higher-level narrative synthesis (e.g., spatial layouts and song titles), it suffers from lower grounding precision compared to Flash’s minimalist, observation-first approach. E. Prompts E.1. Automatic Evaluation We provide the prompts used for atomic fact decomposition in [PITH_FULL_IMAGE:figures/full_fig_p021_6.png] view at source ↗

**Figure 7.** Figure 7: Comparison of attribution strategies. On WorldSense, Post-hoc Attribution improves Recall by grounding descriptive scene elements missed by the Base model. Conversely, on VideoMMMU, the Post-hoc pass often results in “Citation Salad,” incorrectly mapping specific technical steps to generic introductory frames. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_7.png] view at source ↗

**Figure 8.** Figure 8: Qualitative comparison of four program-aided generation variants. Narrative variants struggle with exact quantification due to hallucinated or vague counts. Logic Declarative succeeds by sampling known logical intervals. Logic Imperative fails due to error propagation (over-counting candidates). 23 [PITH_FULL_IMAGE:figures/full_fig_p023_8.png] view at source ↗

**Figure 9.** Figure 9: Prompt for Atomic Decomposition. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_9.png] view at source ↗

**Figure 10.** Figure 10: Prompt for Decontextualization. 25 [PITH_FULL_IMAGE:figures/full_fig_p025_10.png] view at source ↗

**Figure 11.** Figure 11: Prompt for Baseline Generation with Citation. Prompt for Verification Worthiness (Simple) You are an expert evaluator for Multimodal Grounding. Your task is to determine if the Sentence contains CHECK-WORTHY information. INPUTS: 1. Sentence: The text generation to evaluate. GUIDELINES: Output YES (Check-Worthy) if the sentence describes ANY specific, verifiable content in the video/audio (actions, objects… view at source ↗

**Figure 12.** Figure 12: Prompt for Verification Worthiness (Simple Binary). 26 [PITH_FULL_IMAGE:figures/full_fig_p026_12.png] view at source ↗

**Figure 13.** Figure 13: Prompt for Verification Worthiness (Chain-of-Thought). Prompt for Verification Worthiness (JSON) You are an expert evaluator for Multimodal Grounding. Classify if the Sentence contains CHECK-WORTHY information. GUIDELINES: • YES: The sentence describes ANY specific, verifiable content in the video/audio (actions, objects, quantities, text, visual attributes). • NO: The sentence consists ENTIRELY of metada… view at source ↗

**Figure 14.** Figure 14: Prompt for Verification Worthiness (JSON Output). Prompt for Atomic Entailment (Simple) You are an expert evaluator for Multimodal Grounding. Determine if the provided Media Content entails the Atomic Fact. GUIDELINES: • YES (Supported): The provided media segments (images/audio) contain clear evidence that fully supports the fact. • NO (Not Supported): The media contradicts the fact, or the necessary inf… view at source ↗

**Figure 15.** Figure 15: Prompt for Entailment (Simple Binary). 27 [PITH_FULL_IMAGE:figures/full_fig_p027_15.png] view at source ↗

**Figure 16.** Figure 16: Prompt for Entailment (Chain-of-Thought). Prompt for Atomic Entailment (JSON) You are an expert evaluator for Multimodal Grounding. Verify if the Atomic Fact is supported by the Media Content. GUIDELINES: • YES: Strong evidence exists in the media. • NO: Evidence is missing, unrelated, or contradictory. TASK: Atomic Fact: {fact} OUTPUT FORMAT: Return a single JSON object: { "evidence description": "string… view at source ↗

**Figure 17.** Figure 17: Prompt for Entailment (JSON Output). Prompt for Baseline Generation Carefully watch the provided video and listen strictly to the corresponding audio. Your task is to select the best option that answers the question, based exclusively on the provided content. Before stating your final answer, you must provide a step-by-step reasoning process. Output Format: Reasoning: [Your step-by-step reasoning] Answer:… view at source ↗

**Figure 18.** Figure 18: Prompt for Baseline Generation (No Citations). 28 [PITH_FULL_IMAGE:figures/full_fig_p028_18.png] view at source ↗

**Figure 19.** Figure 19: Prompt for Post-hoc Citation Attribution and Correction. 29 [PITH_FULL_IMAGE:figures/full_fig_p029_19.png] view at source ↗

read the original abstract

Multimodal large language models (MLLMs) are increasingly used for real-world tasks involving multi-step reasoning and long-form generation, where reliability requires grounding model outputs in heterogeneous input sources and verifying individual factual claims. However, existing multimodal grounding benchmarks and evaluation methods focus on simplified, observation-based scenarios or limited modalities and fail to assess attribution in complex multimodal reasoning. We introduce MuRGAt (Multimodal Reasoning with Grounded Attribution), a benchmark for evaluating fact-level multimodal attribution in settings that require reasoning beyond direct observation. Given inputs spanning video, audio, and other modalities, MuRGAt requires models to generate answers with explicit reasoning and precise citations, where each citation specifies both modality and temporal segments. To enable reliable assessment, we introduce an automatic evaluation framework that strongly correlates with human judgments. Benchmarking with human and automated scores reveals that even strong MLLMs frequently hallucinate citations despite correct reasoning. Moreover, we observe a key trade-off: increasing reasoning depth or enforcing structured grounding often degrades accuracy, highlighting a significant gap between internal reasoning and verifiable attribution.

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

MuRGAt adds a benchmark for fact-level citation in complex multimodal reasoning and flags that models hallucinate attributions even when the reasoning holds, but the auto-eval correlation needs more scrutiny to rule out format bias.

read the letter

The paper's core finding is that strong MLLMs still produce hallucinated citations when asked to attribute facts across video, audio, and other sources in multi-step reasoning, and that pushing for deeper reasoning or rigid grounding formats tends to lower accuracy. This is the main thing to take away: a practical reliability gap that existing simplified grounding benchmarks miss. MuRGAt itself is the new piece. It requires models to generate answers plus explicit citations that name both modality and temporal segments, then supplies an automatic scorer that tracks human judgments. Running existing models on it surfaces the hallucination pattern and the accuracy trade-off. That setup is a step forward for anyone who needs to test verifiable outputs rather than just final-answer correctness. The correlation between automatic and human scores is the main supporting evidence for the claims, and the absence of circularity in the evaluation is a plus. The soft spot is the lack of detail on how strong that correlation actually is, what sample size was used, and whether the scorer was checked against citation styles outside the benchmark's rigid format. The stress-test concern lands here: because the required output format is so specific, both the automatic metric and human raters could be picking up surface compliance instead of true grounding fidelity, especially where temporal segments in video or audio are ambiguous. No inter-annotator numbers or out-of-distribution tests are mentioned in the abstract, so the reported hallucination rates and trade-off could be partly benchmark-specific. This work is for groups building or auditing multimodal systems that need traceable reasoning. Readers who care about evaluation methods for long-form multimodal generation will get the most from the benchmark and the empirical observations. It deserves a serious referee because the problem it targets is real and the new data points are useful, even if the evaluation validation needs expansion in revision.

Referee Report

2 major / 1 minor

Summary. The paper introduces MuRGAt, a benchmark for fact-level attribution in multimodal reasoning over video, audio, and other modalities. Models must produce answers with explicit reasoning and citations that specify both modality and temporal segments. An automatic evaluation framework is presented that is claimed to correlate strongly with human judgments. Benchmarking of MLLMs shows frequent citation hallucinations even when reasoning is correct, along with a trade-off in which greater reasoning depth or enforced structured grounding reduces accuracy.

Significance. If the automatic evaluator is shown to be a reliable proxy, the results identify a practically important gap between MLLM reasoning capability and verifiable attribution, with direct implications for applications that require traceable multimodal outputs. The depth-versus-accuracy trade-off observation could inform future training objectives that jointly optimize reasoning and grounding.

major comments (2)

[Automatic evaluation framework description] The abstract asserts that the automatic evaluation framework 'strongly correlates with human judgments,' yet supplies no quantitative details on correlation coefficient, sample size, inter-annotator agreement, or validation on out-of-distribution citation formats. Because the central claims (hallucination rates and the depth/grounding trade-off) rest entirely on this framework, the missing metrics constitute a load-bearing gap in the empirical support.
[MuRGAt benchmark construction] The benchmark requires citations in a rigid modality+temporal-segment format. This design choice risks that both the automatic scorer and human raters primarily reward surface-format compliance rather than true grounding fidelity, especially for temporally ambiguous video and audio segments. No ablation or out-of-distribution test is reported to rule out this bias.

minor comments (1)

[Abstract] The abstract would be strengthened by reporting at least one concrete quantitative result (e.g., correlation value or hallucination percentage) rather than qualitative statements alone.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our work. We address each major comment below, providing clarifications from the full manuscript and committing to revisions that strengthen the empirical support without altering our core claims.

read point-by-point responses

Referee: [Automatic evaluation framework description] The abstract asserts that the automatic evaluation framework 'strongly correlates with human judgments,' yet supplies no quantitative details on correlation coefficient, sample size, inter-annotator agreement, or validation on out-of-distribution citation formats. Because the central claims (hallucination rates and the depth/grounding trade-off) rest entirely on this framework, the missing metrics constitute a load-bearing gap in the empirical support.

Authors: We agree that the abstract should be self-contained with key metrics. Section 4.3 of the full manuscript details the human validation study, including correlation analysis between automatic and human scores. We will revise the abstract to summarize these quantitative results (correlation coefficient, sample size, inter-annotator agreement) and add a short statement on OOD citation format validation to make the support for our claims explicit. revision: yes
Referee: [MuRGAt benchmark construction] The benchmark requires citations in a rigid modality+temporal-segment format. This design choice risks that both the automatic scorer and human raters primarily reward surface-format compliance rather than true grounding fidelity, especially for temporally ambiguous video and audio segments. No ablation or out-of-distribution test is reported to rule out this bias.

Authors: We acknowledge the risk of surface-format bias in the rigid citation requirement. The format was deliberately chosen to support precise fact-level attribution in multi-step reasoning, as flexible formats proved insufficient for verifiable claims in pilot studies. To directly address the concern, we will add an ablation comparing rigid versus flexible citation formats and include targeted human evaluations on temporally ambiguous segments in the revised manuscript, demonstrating that scores reflect grounding fidelity. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark evaluation of existing models

full rationale

The paper introduces MuRGAt as a new benchmark requiring explicit reasoning and modality+temporal citations, plus an automatic scorer that correlates with human judgments. It then applies this to evaluate off-the-shelf MLLMs, reporting observed hallucination rates and accuracy trade-offs. These are direct empirical measurements on new test cases rather than any derivation that reduces by construction to fitted parameters, self-definitions, or self-citation chains. No equations or load-bearing steps are shown to be equivalent to their inputs; the central claims rest on external model behavior against the introduced benchmark.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an empirical benchmark introduction paper. No mathematical derivations, fitted parameters, or physical axioms are involved. The contribution consists of the new dataset construction rules and evaluation protocol.

pith-pipeline@v0.9.0 · 5496 in / 1012 out tokens · 62201 ms · 2026-05-16T03:56:40.589373+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/RealityFromDistinction.lean reality_from_one_distinction unclear

?

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

We introduce MuRGAt ... automatic evaluation framework that strongly correlates with human judgments ... even strong MLLMs frequently hallucinate citations despite correct reasoning ... trade-off: increasing reasoning depth or enforcing structured grounding often degrades accuracy

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

13 extracted references · 13 canonical work pages · 3 internal anchors

[1]

Gemma 3 Technical Report

URL https://aclanthology.org/2023. emnlp-main.398/. Gemma Team. Gemma 3 technical report, 2025. URL https://arxiv.org/abs/2503.19786. Google. Gemini 3.https://deepmind.google/models/gemini/, 2025. Hendricks, L. A., Wang, O., Shechtman, E., Sivic, J., Dar- rell, T., and Russell, B. Localizing moments in video with natural language, 2017. URL https://arxiv....

work page internal anchor Pith review Pith/arXiv arXiv doi:10.18653/v1/2025.findings-emnlp 2023
[2]

Video-MMMU: Evaluating Knowledge Acquisition from Multi-Discipline Professional Videos

URL https://aclanthology.org/2025. findings-emnlp.318/. Hu, K., Wu, P., Pu, F., Xiao, W., Zhang, Y ., Yue, X., Li, B., and Liu, Z. Video-mmmu: Evaluating knowledge acqui- sition from multi-discipline professional videos, 2025b. URLhttps://arxiv.org/abs/2501.13826. Huang, B., Wang, X., Chen, H., Song, Z., and Zhu, W. Vtimellm: Empower llm to grasp video mo...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.1145/3571730 2025
[3]

Lost in the Middle: How Language Models Use Long Contexts

doi: 10.1162/tacl a 00453. URL https:// aclanthology.org/2022.tacl-1.10/. Lee, H., Joo, S., Kim, C., Jang, J., Kim, D., On, K.-W., and Seo, M. How well do large language models truly ground?, 2024. URL https://arxiv.org/abs/ 2311.09069. Lei, J., Yu, L., Bansal, M., and Berg, T. L. Tvqa: Localized, compositional video question answering, 2019. URL https://...

work page internal anchor Pith review doi:10.1162/tacl 2022
[4]

coling-main.580

URL https://aclanthology.org/2023. findings-emnlp.467/. Liu, Y ., Fabbri, A., Zhao, Y ., Liu, P., Joty, S., Wu, C.-S., Xiong, C., and Radev, D. Towards interpretable and efficient automatic reference-based summarization eval- uation. In Bouamor, H., Pino, J., and Bali, K. (eds.), Proceedings of the 2023 Conference on Empirical Meth- ods in Natural Languag...

work page doi:10.18653/v1/2023.emnlp-main 2023
[5]

emnlp-main.1018/

URL https://aclanthology.org/2023. emnlp-main.1018/. Mahmood, A., Vayani, A., Naseer, M., Khan, S., and Khan, F. S. VURF: A general-purpose reasoning and self-refinement framework for video understanding. In Workshop on Video-Language Models @ NeurIPS 2024,

work page 2023
[6]

In: NeurIPS ML Safety Workshop (2022)

URL https://openreview.net/forum? id=S92QnVEzQP. Menick, J., Trebacz, M., Mikulik, V ., Aslanides, J., Song, F., Chadwick, M., Glaese, M., Young, S., Campbell- Gillingham, L., Irving, G., and McAleese, N. Teaching language models to support answers with verified quotes. arXiv preprint arXiv:2203.11147, 2022. Min, J., Buch, S., Nagrani, A., Cho, M., and Sc...

work page doi:10.18653/v1/2023 2022
[7]

ISBN 979-8-89176-332-6

URL https://aclanthology.org/2025. findings-emnlp.50/. Wang, X., Cheng, F., Wang, Z., Wang, H., Islam, M. M., Torresani, L., Bansal, M., Bertasius, G., and Crandall, D. Timerefine: Temporal grounding with time refining video llm, 2025b. URL https://arxiv.org/abs/ 2412.09601. Wang, X., Zhang, Y ., Zohar, O., and Yeung-Levy, S. Videoa- gent: Long-form video...

work page doi:10.18653/v1/2025.emnlp-main 2025
[8]

Yu, Z., Peng, R., Ding, K., Li, Y ., Peng, Z., Liu, M., Zhang, Y ., Yuan, Z., Xin, H., Huang, W., Wen, Y ., Zhang, G., and Liu, W

URL https://aclanthology.org/2025. emnlp-main.1428/. Wang, Z., Yu, S., Stengel-Eskin, E., Yoon, J., Cheng, F., Bertasius, G., and Bansal, M. Videotree: Adaptive tree- based video representation for llm reasoning on long videos. InProceedings of the Computer Vision and Pat- tern Recognition Conference (CVPR), pp. 3272–3283, June 2025e. Wang, Z., Zhou, H., ...

work page doi:10.18653/v1/2023.findings-emnlp 2025
[9]

ISBN 979-8-89176-256-5

URL https://aclanthology.org/2023. findings-emnlp.307/. Zhang, J., Bai, Y ., Lv, X., Gu, W., Liu, D., Zou, M., Cao, S., Hou, L., Dong, Y ., Feng, L., and Li, J. LongCite: Enabling LLMs to generate fine-grained ci- tations in long-context QA. In Che, W., Nabende, J., Shutova, E., and Pilehvar, M. T. (eds.),Findings of the Association for Computational Ling...

work page doi:10.18653/v1/2025.findings-acl 2023
[10]

The video shows

URL https://aclanthology.org/2025. findings-acl.264/. 13 Multimodal Fact-Level Attribution for Verifiable Reasoning (a)Annotation UI for Atomic Fact Decomposition. (b)Annotation UI for Verification Worthiness. A. Human Evaluation Details To validate our automatic metrics, we developed a multi-stage human annotation protocol. The protocol consists of three...

work page 2025
[11]

Does it describe any specific visual or audio details?

Analyze theSentence. Does it describe any specific visual or audio details?

work page
[12]

If it containsanyverifiable claim (even mixed with reasoning), mark it asYES

work page
[13]

reasoning

Only mark it asNOif it is purely structural, logical, or opinion-based without new visual information. OUTPUT FORMAT: Reasoning: [Analyze the sentence content.] Answer: [YES or NO] Figure 13.Prompt for Verification Worthiness (Chain-of-Thought). Prompt for Verification Worthiness (JSON) You are an expert evaluator for Multimodal Grounding. Classify if the...

work page