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REVIEW 3 major objections 3 minor 1 cited by

Current multimodal models fail at spotting changes across paired videos

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T0 review · deepseek-v4-flash

2026-08-03 14:53 UTC pith:A3YCGTKU

load-bearing objection Useful paired-video benchmark with real novelty; headline gap rests on unverified question answerability and human protocol — fixable, worth reviewing. the 3 major comments →

arxiv 2512.18735 v2 pith:A3YCGTKU submitted 2025-12-21 cs.CV cs.AI

M³-Verse: A "Spot the Difference" Challenge for Large Multimodal Models

Kewei Wei , Bocheng Hu , Jie Cao , Xiaohan Chen , Zhengxi Lu , Wubing Xia , Weili Xu , Jiaao Wu
show 8 more authors
Junchen He Mingyu Jia Ciyun Zhao Ye Sun Yizhi Li Zhonghan Zhao Jian Zhang Gaoang Wang
This is my paper
classification cs.CV cs.AI
keywords multimodal benchmarkvideo question answeringstate change reasoningegocentric videospatial intelligencehallucination evaluationtemporal reasoning
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper introduces M3-Verse, a benchmark that tests whether large multimodal models can detect and reason about changes in an indoor scene by comparing two first-person videos taken before and after a state change. It comprises 270 scenes, 2,932 multiple-choice questions, and over 50 subtasks spanning spatial, temporal, attribute, and reasoning skills. The central finding is a large gap: the best model scores 45.14%, while humans score 89.75%. The paper argues this gap reveals a missing capability in current models that is essential for embodied intelligence, and proposes a simple captioning-and-text-reasoning baseline that raises scores by several points.

Core claim

On its own terms, the paper establishes that state-of-the-art large multimodal models cannot reliably compare two observations of the same scene to identify what changed. With paired egocentric videos of 270 indoor scenes before and after a programmatic change, and 2,932 questions, the best evaluated model reaches 45.14% accuracy versus 89.75% for human evaluators. The benchmark also shows that models are particularly prone to hallucination-type questions, that larger scale does not guarantee better performance, and that converting videos into hierarchical text captions before answering improves inter-state reasoning scores. The paper treats M3-Verse as a shared testbed for a capability exis

What carries the argument

The central object is the paired two-state video: each scene is recorded by an embodied agent exploring it once before and once after an object-level change, so answering inter-state questions requires comparing the two observations rather than recognizing a single state. Questions are generated from simulator metadata through a template pipeline with hierarchical entity grounding to avoid ambiguous references, then filtered in two stages (text-only answerability and multimodal triviality). The proposed HCTR baseline works by turning each video into time-stamped clip captions, summarizing them, and answering from text alone.

Load-bearing premise

The benchmark assumes every question can be answered from the paired videos alone, but the QA pairs come from simulator metadata and the filtering only removes text-answerable questions, never verifying that the correct answer is visually recoverable from the frames.

What would settle it

Give a fresh group of human annotators the same 2,932 questions with access only to the videos and not to any simulator metadata, and compare their accuracy to the reported 89.75%; a significant drop would show that the human baseline and the model gap are partly an artifact of metadata, not pure visual understanding.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

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If this is right

  • If the gap is real, current multimodal models are not reliable for embodied applications where an agent must check what changed after an action.
  • The finding that inter-state reasoning improves under hierarchical captioning suggests that explicitly serializing video content into text helps models track change.
  • Hallucination-type questions expose a systematic tendency of models to commit to an option even when the correct answer is 'no correct option is listed'.
  • Denser frame sampling improves performance, implying state-change perception is partly limited by temporal resolution rather than pure reasoning.
  • Model scale alone does not close the gap; architectural and instruction-tuning choices matter more.

Where Pith is reading between the lines

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

  • A key unstated risk is that the ground-truth answers are derived from simulator metadata, and the filtering pipeline only removes questions answerable from text, not questions unanswerable from the video pixels; if many questions ask for metric distances or areas that a first-person video cannot convey, the human baseline may overstate what is visually recoverable.
  • The benchmark's release of depth maps, instance masks, and operation logs invites future work that uses these as supervision; a model trained on them might be expected to far exceed the reported 45%.
  • The intra-state tasks being harder than inter-state tasks for many models is counterintuitive and may reflect a 'needle in a haystack' localization problem; a fair test of state-change reasoning would be to control for localization difficulty.
  • One testable extension: measure human performance on the same videos without any access to simulator metadata or question templates; if human accuracy drops substantially, the benchmark's visual-grounding claim would need qualification.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 3 minor

Summary. The paper introduces M3-Verse, a benchmark for multi-state video understanding. For 270 ProcTHOR indoor scenes, the authors create paired egocentric videos of a scene before and after a programmatic state change (540 videos total), and automatically generate 2,932 multiple-choice QA pairs from simulator metadata using template-based generation. The questions are divided into intra-state and inter-state categories, span 50+ subtasks across four capabilities, and include 23.2% hallucination-type questions. After a two-stage LLM/LMM filtering pipeline and human review, the authors evaluate 16 LMMs, 6 text-only LLMs, a random baseline, and a human baseline. The best LMM (GPT-5) reaches 45.14% overall, compared with 89.75% for humans. The paper also proposes Hierarchical Captioning and Text-based Reasoning (HCTR), which improves several base models by converting video QA into caption-based text QA.

Significance. If the benchmark is valid, it fills a real gap: current benchmarks mostly test static or single-state video understanding, while M3-Verse targets the ability to compare two observations of the same scene and reason about state transitions. The construction has notable strengths: answers are grounded in simulator state rather than human annotations; the authors release both the construction pipeline and the full benchmark data; the evaluation covers a wide range of models including text-only controls; and the frame-sampling analysis is a useful sanity check. However, the central claim—that the human–model gap reflects a deficit in multi-state visual reasoning—depends on two unverified assumptions: that every question is visually answerable from the paired videos, and that the human baseline was collected under the same visual-only protocol. The presence of text-only models scoring far above random also indicates possible residual textual leakage. These issues are fixable but load-bearing.

major comments (3)
  1. [Section 3.2; Appendix A.4; Tables 4–6] The benchmark generation never verifies that the ground-truth answer is visually recoverable from the paired egocentric videos. Stage 1 (Fig. 15) only removes questions answerable from text, question, options, and answer; Stage 2 removes questions a lightweight LMM can answer; neither step checks observability. Many templates in Tables 4–6 ask for quantities that a first-person video cannot reliably support: scene area, room area, metric distances (e.g., 'distance between object1 and object2'), object height/length, room visit counts, exact visit order, and total observation times. If a substantial fraction of the 2,932 questions are not answerable from the provided frames, then low model scores conflate ungroundable queries with failures of state-change reasoning, and the headline human–model gap is not interpretable. I request an observability audit (per-template, with examples) or a r
  2. [Appendix A.4; Table 2] The human baseline of 89.75% is presented without a protocol. The appendix only says that 12 reviewers were recruited to 're-examine and screen the data' and to 'evaluate human performance,' but does not state whether the reviewers watched the paired videos, whether they had access to simulator metadata or backend logs, what options/format they saw, or how disagreement was resolved. Since the human number anchors the central 'stark performance gap' conclusion, this is load-bearing. The authors should specify the exact human evaluation protocol and, ideally, report a separate visual-only human result collected under the same constraints as the models (same frames, same options, no metadata).
  3. [Table 2; Appendix A.4 Stage 1] The text-only LLMs perform substantially above random even though Stage 1 is supposed to remove text-answerable questions. For example, Qwen3-14B reaches 33.78% and Qwen3-32B reaches 33.76% versus a random baseline of 24.14% (Table 2). Since these models receive no video input, any above-random accuracy indicates residual textual cues, option-order patterns, or language priors that the vision-free filtering (Fig. 15) did not remove. This weakens the claim that the benchmark isolates visual understanding and the conclusion that 'vision is essential.' I recommend measuring a stronger text-only ceiling (e.g., an LLM prompted with all options and the question, with option order ablated) and reporting per-template leakage statistics.
minor comments (3)
  1. [Table 2] Model names are inconsistent/misspelled: 'LLaV A-OneVision-1.5-8B' should be 'LLaVA-OneVision-1.5-8B'. Also 'InternVL3.5-2B-Instruct*' etc. are fine, but the footnote markers are not explained in the caption.
  2. [Section 4.2, finding 6] Typo: 'folllwing' should be 'following'. In Section 4.1 the bullet list contains 'r4we', likely a stray artifact. Please proofread.
  3. [Section 5; Figures 5–6] The HCTR comparisons may not be apples-to-apples in compute/frame budget. The base models are evaluated with a fixed number of sampled frames (e.g., 200 per video), while HCTR uses clip captions with different and potentially larger total frame usage. Please state the total number of frames consumed by each HCTR variant and, if possible, compare against a base model with matched frame count.

Circularity Check

0 steps flagged

No circularity: benchmark labels are simulator ground truth, model scores are measured, and the disclosed difficulty filter does not reduce the reported human-model gap to a construction.

full rationale

M3-Verse is a data-construction and evaluation paper, not a derivation of predictions from first principles. QA labels are generated from AI2-THOR/ProcTHOR metadata ('we employ an automated, template-based pipeline to generate QA pairs from the rich scene metadata'), and the reported model scores are empirical measurements against those fixed labels. The two-stage filtering (Appendix A.4) removes text-answerable questions and then questions a lightweight LMM can answer; this is explicitly disclosed dataset curation for difficulty, not a fitted parameter being relabeled as a prediction. The central human-model gap (89.75% vs 45.14%) is not forced by that filter: GPT-5/Gemini were not the filter model, and human accuracy is an external anchor. HCTR is a captioning baseline evaluated against the same fixed ground truth, so its improvement is not circular. There is no load-bearing self-citation chain or imported uniqueness/ansatz. Concerns about whether every question is visually answerable from egocentric video, and about the exact human testing protocol, are benchmark-validity questions, not circular reductions.

Axiom & Free-Parameter Ledger

0 free parameters · 3 axioms · 0 invented entities

No free parameters are fitted to data; the benchmark is generated from simulator ground truth. The key assumptions are about visual groundability and the fairness of the human baseline.

axioms (3)
  • domain assumption AI2-THOR/ProcTHOR synthetic scenes are a valid proxy for real-world visual understanding of state changes.
    The benchmark is entirely simulated; transfer to real video is assumed, not demonstrated.
  • domain assumption All 2,932 QA pairs are visually answerable from the paired egocentric videos; objects and attributes referenced in questions are observable in the frames.
    The generation pipeline uses simulator metadata and only filters out text-answerable questions; it does not verify visual answerability. If false, many questions cannot be answered from the videos, undermining the benchmark's validity.
  • domain assumption The human baseline was obtained under comparable conditions (watching the same videos) without access to ground-truth metadata.
    The paper states 12 reviewers screened data and evaluated human performance, but does not specify whether they had access to metadata or answers; high human accuracy may be inflated if not.

pith-pipeline@v1.3.0-alltime-deepseek · 23850 in / 13899 out tokens · 143309 ms · 2026-08-03T14:53:53.991165+00:00 · methodology

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Cite this review

Pith. "Pith review of $M^3-Verse$: A "Spot the Difference" Challenge for Large Multimodal Models." pith.science (2026). https://pith.science/paper/A3YCGTKU

@misc{pith2026251218735,
  author       = {Pith},
  title        = {Pith review of: $M^3-Verse$: A "Spot the Difference" Challenge for Large Multimodal Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/A3YCGTKU}},
  note         = {Machine review of arXiv:2512.18735}
}
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read the original abstract

Modern Large Multimodal Models (LMMs) have demonstrated extraordinary ability in static image and single-state spatial-temporal understanding. However, their capacity to comprehend the dynamic changes of objects within a shared spatial context between two distinct video observations, remains largely unexplored. This ability to reason about transformations within a consistent environment is particularly crucial for advancements in the field of spatial intelligence. In this paper, we introduce $M^3-Verse$, a Multi-Modal, Multi-State, Multi-Dimensional benchmark, to formally evaluate this capability. It is built upon paired videos that provide multi-perspective observations of an indoor scene before and after a state change. The benchmark contains a total of 270 scenes and 2,932 questions, which are categorized into over 50 subtasks that probe 4 core capabilities. We evaluate 16 state-of-the-art LMMs and observe their limitations in tracking state transitions. To address these challenges, we further propose a simple yet effective baseline that achieves significant performance improvements in multi-state perception. $M^3-Verse$ thus provides a challenging new testbed to catalyze the development of next-generation models with a more holistic understanding of our dynamic visual world. You can get the construction pipeline from https://github.com/Wal-K-aWay/M3-Verse_pipeline and full benchmark data from https://www.modelscope.cn/datasets/WalKaWay/M3-Verse.

Figures

Figures reproduced from arXiv: 2512.18735 by Bocheng Hu, Ciyun Zhao, Gaoang Wang, Jiaao Wu, Jian Zhang, Jie Cao, Junchen He, Kewei Wei, Mingyu Jia, Weili Xu, Wubing Xia, Xiaohan Chen, Ye Sun, Yizhi Li, Zhengxi Lu, Zhonghan Zhao.

Figure 1
Figure 1. Figure 1: Limitation of current LMM benchmarks. Existing benchmarks concentrate on evaluating a model’s performance on an isolated [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of M3 -Verse. Intra-State: can be answered using information from a single state, Inter-State: requires information from both ‘before’ and ‘after’ states to be answered. These categories are further structured to evaluate four key capabilities: Spatial Understanding, Temporal Understanding, Attribute Recognition and Reasoning [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: M3 -Verse Statistic. (a) M3 -Verse are classifiable into five primary categories according to the task, which are then fur￾ther subdivided into more than 50 types of sub-tasks. (b) M3 - Verse falls into four main categories, each including intra-state and inter-state tasks. A single question isn’t designed to test just one capability, but rather to evaluate multiple abilities in a com￾pound manner [PITH_F… view at source ↗
Figure 4
Figure 4. Figure 4: An analysis of the impact of the number of sampled [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Performance comparison of various models with and without [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Performance comparison of experimental performance [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Distribution of scene areas. This chart shows the distri [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Distribution of option number in M3 -Verse. It consists of multiple-choice questions, and the number of options for each question varies from 2 to 7. A.1. Examples Some QA examples are shown in [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗
Figure 11
Figure 11. Figure 11: M3 -Verse Examples DAM Prompt The masked object is a(n) {object category}. Describe it in detail, focusing on its type, shape, color and physical properties [PITH_FULL_IMAGE:figures/full_fig_p013_11.png] view at source ↗
Figure 13
Figure 13. Figure 13: Prompt for consolidating multiple object descriptions. [PITH_FULL_IMAGE:figures/full_fig_p014_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Prompt for comparative analysis of multiple objects. [PITH_FULL_IMAGE:figures/full_fig_p014_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Vision-free Answerability Check Prompt. Ambiguity Removal Prompt You are a QA quality control assistant. Your task is to filter out QAs where the options are ambiguous. Your evaluation should solely focus on identifying if the options are unclear or ambiguous based on the provided text and options. Do not consider visual aspects, question wording, or correctness of the answer at this stage. Evaluate the f… view at source ↗
Figure 17
Figure 17. Figure 17: Linguistic Enhancement Prompt. the physical and semantic constraints inherent to the AI2- THOR [12] simulator. This rigorous process guarantees that every altered scene remains physically plausible and logically coherent. We introduce a diverse set of con￾trolled modifications to simulate realistic alterations. These changes encompass: • Object Extrinsic Changes: Altering the presence or pose of objects, … view at source ↗
Figure 16
Figure 16. Figure 16: Ambiguity Removal Prompt. able points. To enhance path efficiency and safety, the initial rectan￾gles are contracted inward. A trajectory that closely follows the outer boundaries of the reachable coordinates is prob￾lematic for two main reasons. First, it results in unnecessar￾ily long paths, reducing exploration efficiency. Second, it introduces a significant risk of collision with nearby objects or wal… view at source ↗
Figure 18
Figure 18. Figure 18: Evaluation Prompt for Vision-Blind LLMs [ [PITH_FULL_IMAGE:figures/full_fig_p019_18.png] view at source ↗
Figure 20
Figure 20. Figure 20: Clip Caption Generation Prompt. Captions Summarization Prompt Please synthesize the following descriptions of different segments of a video into a single, coherent, and detailed overall caption for the entire video. Segment Descriptions: {captions for all the clips that make up this complete video} Focus on the environment spatial layout, objects, including their spa￾tial layout, detailed information, and… view at source ↗
Figure 21
Figure 21. Figure 21: Captions Summarization Prompt. HCTR answer Prompt Based on the following context information, answer the question: Context: {context} Question: {question} Options: {options text} Return ONLY the option letter(s) (e.g., “A”, “B”, “C”). For multiple correct options, concatenate the letters (e.g., “ABC”). AVOID ANY OTHER OUTPUT CONTENT [PITH_FULL_IMAGE:figures/full_fig_p020_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: C.2. Ablation Study We performed an ablation study on the HCTR method to evaluate the effect of varying clip lengths on different mod￾els. For InternVL3.5-4B and InternVL3.5-8B [30], we as￾sessed clip lengths of 50, 100, and 200, uniformly sampling 50 frames from each clip. For the Qwen3-VL series, we evaluated clip lengths of 100 and 200 for the Qwen3-VL￾4B model and a length of 200 for the Qwen3-VL-8B m… view at source ↗

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