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REVIEW 2 major objections 2 minor 26 references

EG-VQA: Benchmarking Verifiable Video Question Answering with Grounded Temporal Evidence

T0 review · 2 major / 2 minor · reviewed 2026-06-26 · grok-4.3

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Pith's one-line read Video question answering models often produce correct answers while failing to identify the supporting video segments.

desk verdict EG-VQA adds a benchmark and metric for evidence grounding in VideoQA, but the claims rest on unverified annotation quality. read the letter →

arxiv 2606.24797 v1 pith:MZSUF3J3 submitted 2026-06-23 cs.CV cs.AI

Linpeng Huang , Weixing Chen , Zexin Chen , Yang Liu , Liang Lin This is my paper · ORCID
classification cs.CVcs.AI
keywords VideoQuestionAnsweringVideo-LLMsEvidenceGroundingTemporalLocalizationBenchmarkConstructionEG-F1MetricCounterfactualReasoning
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper builds EG-VQA, a benchmark of 2,067 videos and 11,838 QA pairs where each question comes with explicit temporal evidence annotations. It introduces the EG-F1 metric that scores both temporal overlap and semantic match between predicted and ground-truth evidence. Experiments show that strong proprietary Video-LLMs exhibit large gaps between answer accuracy and evidence localization. The authors then train EG-Reasoner with direct supervision on the evidence annotations and report state-of-the-art open-source results that approach proprietary performance, especially on counterfactual reasoning questions.

What carries the argument

EG-VQA benchmark containing fine-grained temporal evidence annotations per QA pair, scored by the Evidence-Grounded F1 (EG-F1) metric that combines temporal alignment and semantic consistency.

What would settle it

A model that scores high on answer accuracy but low on EG-F1 when its predicted evidence intervals are compared against the ground-truth annotations would directly contradict the claimed discrepancy.

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Extended reading notes

Core claim

Even strong proprietary Video-LLMs exhibit a significant discrepancy between answer correctness and faithful localization of temporal evidence. The EG-VQA benchmark requires models to jointly reason and localize evidence, evaluated by the EG-F1 metric that jointly measures temporal alignment and semantic consistency. Training EG-Reasoner with explicit evidence supervision produces state-of-the-art open-source performance and competitiveness with proprietary systems, with the largest gains on reasoning-intensive tasks.

Load-bearing premise

The fine-grained evidence annotations accurately and comprehensively represent the supporting temporal evidence for each QA pair.

Editorial extensions

If this is right

  • Scaling model size alone is insufficient for robust video understanding.
  • Structured evidence supervision improves both reliability and interpretability of VideoQA outputs.
  • Gains from evidence training are especially large on counterfactual and other reasoning-intensive questions.
  • Open-source models can reach performance levels competitive with proprietary systems when trained with explicit evidence labels.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Future VideoQA training pipelines may need to treat evidence localization as a primary objective rather than an afterthought.
  • The same annotation and metric approach could be applied to other temporal reasoning domains such as action anticipation or event prediction.
  • Explicit evidence supervision might allow automated detection of cases where a model hallucinates supporting video content.
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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

2 major / 2 minor

Summary. The manuscript introduces EG-VQA, a benchmark with 2,067 videos and 11,838 QA pairs each annotated with fine-grained temporal evidence segments. It defines the EG-F1 metric to jointly assess answer correctness and evidence localization against these annotations. Experiments indicate that even strong proprietary Video-LLMs show low EG-F1 despite potentially correct answers, revealing a gap in faithful grounding; the authors propose EG-Reasoner trained with explicit evidence supervision, reporting SOTA results among open-source models especially on counterfactual reasoning tasks.

Significance. If the annotations prove reliable, the work is significant for shifting VideoQA evaluation from answer-only metrics to verifiable evidence grounding, demonstrating that scaling alone is insufficient and that explicit supervision yields measurable gains. The new benchmark and EG-F1 metric provide concrete tools for future interpretable Video-LLM development.

major comments (2)
  1. [Dataset construction / annotation protocol] The central claim that proprietary models exhibit a fundamental discrepancy between answer correctness and faithful evidence localization (and that EG-Reasoner closes this gap) depends on EG-F1 being a valid measure of grounding quality. This in turn requires the 11,838 human annotations to be exhaustive and unambiguous. The manuscript provides no inter-annotator agreement statistics, coverage analysis, or adjudication protocol in the dataset construction section, leaving open the possibility that low EG-F1 scores reflect annotation incompleteness rather than model failure.
  2. [Experimental results] Table reporting main results (likely Table 2 or 3): without reported statistical tests, variance across runs, or breakdown of EG-F1 components (temporal overlap vs. semantic consistency), it is unclear whether the reported gains for EG-Reasoner over baselines are robust or whether the discrepancy for proprietary models is statistically significant.
minor comments (2)
  1. [Abstract] The abstract states 'particularly pronounced gains are observed on reasoning-intensive tasks' but does not quantify the differential improvement; a per-task breakdown would strengthen the claim.
  2. [Metric definition] Notation for EG-F1 is introduced without an explicit formula in the main text; including the precise definition (e.g., how temporal IoU and semantic match are combined) would improve reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their insightful comments, which highlight important aspects of dataset validation and experimental analysis. We address each major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Dataset construction / annotation protocol] The central claim that proprietary models exhibit a fundamental discrepancy between answer correctness and faithful evidence localization (and that EG-Reasoner closes this gap) depends on EG-F1 being a valid measure of grounding quality. This in turn requires the 11,838 human annotations to be exhaustive and unambiguous. The manuscript provides no inter-annotator agreement statistics, coverage analysis, or adjudication protocol in the dataset construction section, leaving open the possibility that low EG-F1 scores reflect annotation incompleteness rather than model failure.

    Authors: We agree that the absence of inter-annotator agreement (IAA) statistics and detailed annotation protocols in the manuscript is a limitation. In the revised manuscript, we will add a dedicated subsection on the annotation process, including IAA scores computed using appropriate metrics (e.g., temporal IoU agreement), coverage analysis of evidence segments, and the adjudication protocol used for resolving disagreements. This will strengthen the validity of EG-F1 as a measure of grounding quality. revision: yes

  2. Referee: [Experimental results] Table reporting main results (likely Table 2 or 3): without reported statistical tests, variance across runs, or breakdown of EG-F1 components (temporal overlap vs. semantic consistency), it is unclear whether the reported gains for EG-Reasoner over baselines are robust or whether the discrepancy for proprietary models is statistically significant.

    Authors: We acknowledge that additional statistical analysis would improve the clarity of the results. In the revised version, we will include variance across multiple runs (e.g., standard deviations), perform statistical significance tests where appropriate, and provide a component-wise breakdown of EG-F1 to separate temporal overlap from semantic consistency. This will allow readers to better assess the robustness of the reported improvements. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity; new benchmark and metric are externally grounded in human annotations

full rationale

The paper introduces EG-VQA as a new benchmark with 11,838 human-annotated QA pairs and temporal evidence segments, defines EG-F1 as a metric comparing model outputs to those annotations, evaluates proprietary and open models on it, and trains EG-Reasoner with explicit supervision on the same data. No derivation step reduces by construction to its own inputs, no fitted parameters are relabeled as predictions, and no load-bearing claims rest on self-citations or imported uniqueness theorems. The central claims rest on the external human annotations and standard train/eval splits rather than self-referential fitting.

Assumptions & free parameters 0 free parameters · 1 assumptions · 2 invented entities

Central claims rest on the assumption that human-provided temporal evidence annotations are reliable and that the new EG-F1 metric faithfully captures grounding quality; no free parameters or external invented entities with independent evidence are described.

assumptions (1)
  • domain assumption The fine-grained temporal evidence annotations supplied with each QA pair are accurate and complete representations of supporting video content.
    All reported performance numbers and the EG-F1 metric depend directly on these annotations being valid.
invented entities (2)
  • EG-F1 metric
    purpose: Jointly measures temporal alignment and semantic consistency of predicted evidence against ground truth.
    New evaluation metric introduced to assess evidence grounding.
  • EG-Reasoner
    purpose: Evidence-grounded reasoning model trained with explicit supervision on the benchmark.
    Proposed model to address the identified grounding gap.

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Cite this review

Pith. "Pith review of EG-VQA: Benchmarking Verifiable Video Question Answering with Grounded Temporal Evidence." pith.science (2026). https://pith.science/paper/MZSUF3J3

@misc{pith2026260624797,
  author       = {Pith},
  title        = {Pith review of: EG-VQA: Benchmarking Verifiable Video Question Answering with Grounded Temporal Evidence},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MZSUF3J3}},
  note         = {Machine review of arXiv:2606.24797}
}
read the original abstract

Recent advances in Video Large Language Models (Video-LLMs) have yielded promising performance on video question answering (VideoQA). Nevertheless, existing benchmarks are predominantly evaluated through answer correctness, while the grounding of predictions in relevant video evidence remains largely unexamined. This disconnect between answer generation and evidence understanding motivates the construction of the Evidence-Grounded Video Question Answering Benchmark (EG-VQA), an open-ended evaluation protocol in which each QA pair is explicitly annotated with supporting temporal evidence, thereby requiring joint reasoning and precise evidence localization. EG-VQA is comprised of 2,067 videos and 11,838 QA pairs with fine-grained evidence annotations. To evaluate predicted evidence, Evidence-Grounded F1 (EG-F1) is introduced as a unified metric in which temporal alignment and semantic consistency against ground-truth evidence are jointly measured. Experimental evaluation reveals that even strong proprietary models struggle to accurately ground their predictions, exposing a fundamental discrepancy between answer correctness and faithful evidence localization. To bridge this gap, EG-Reasoner, an evidence-grounded reasoning model trained with explicit supervision, is proposed. State-of-the-art performance is achieved among open-source models, with results competitive against proprietary systems, particularly pronounced gains are observed on reasoning-intensive tasks such as counterfactual questions. These findings demonstrate that scaling alone is insufficient for robust video understanding and that structured evidence supervision is essential for the development of more reliable and interpretable VideoQA systems.

Figures

Figures reproduced from arXiv: 2606.24797 by the authors.

Figure 1
Figure 1. Overview of the proposed evidence-grounded VideoQA paradigm. (a) Existing VideoQA [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of the EG-VQA dataset construction pipeline. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Overview of the EG-Reasoner training framework. Given a video and question, the [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (13 more)
Figure 4
Figure 4. Figure 4: Comparison between answer cor￾rectness and evidence grounding. Model EG-VQA Strict Acc.↑ Relaxed Acc.↑ Avg. F1↑ Avg. EG-F1↑ EG-Reasoner (Ours) 26.88 42.71 37.89 12.28 w/o Evidence Reward 22.21 36.17 15.81 4.21 w/o Soft Evidence Reward 24.00 37.96 15.33 3.62 VideoChat-R…
Figure 5
Figure 5. Figure 5: Demonstrations of Baseline Models and Our EG-Reasoner. Hallucinated evidence is [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Overview of EG-VQA statistics and representative QA examples. [PITH_FULL_IMAGE:figures/full_fig_p016_6.png]
Figure 7
Figure 7. Figure 7: Evidence statistics of EG-VQA. (a) Distribution of evidence counts per question. (b) [PITH_FULL_IMAGE:figures/full_fig_p017_7.png]
Figure 8
Figure 8. Figure 8: Qualitative example of successful evidence-grounded reasoning. EG-Reasoner correctly [PITH_FULL_IMAGE:figures/full_fig_p022_8.png]
Figure 9
Figure 9. Figure 9: Qualitative example of incomplete procedural grounding. EG-Reasoner captures the overall [PITH_FULL_IMAGE:figures/full_fig_p022_9.png]
Figure 10
Figure 10. Figure 10: Prompt template for descriptive question generation. [PITH_FULL_IMAGE:figures/full_fig_p023_10.png]
Figure 11
Figure 11. Figure 11: Prompt template for temporal question generation. [PITH_FULL_IMAGE:figures/full_fig_p024_11.png]
Figure 12
Figure 12. Figure 12: Prompt template for causal question generation. [PITH_FULL_IMAGE:figures/full_fig_p026_12.png]
Figure 13
Figure 13. Figure 13: Prompt template for counterfactual question generation. [PITH_FULL_IMAGE:figures/full_fig_p027_13.png]
Figure 14
Figure 14. Figure 14: Prompt template for model cross-validation. [PITH_FULL_IMAGE:figures/full_fig_p028_14.png]
Figure 15
Figure 15. Figure 15: Prompt template for training and inference. [PITH_FULL_IMAGE:figures/full_fig_p029_15.png]
Figure 16
Figure 16. Figure 16: Prompt template for model evaluation. 29 [PITH_FULL_IMAGE:figures/full_fig_p029_16.png]

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Reference graph

Works this paper leans on

26 extracted references · 1 canonical work pages

  1. [1]

    VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement Fine-Tuning

    URLhttps://api.semanticscholar.org/CorpusID:258588306. Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping Luo, et al. Mvbench: A comprehensive multi-modal video understanding benchmark. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22195–22206, 2024. Linjie Li, Yen...

    work page Pith review arXiv 2024

  2. [2]

    questions

    Generate **1 to 3 open-ended questions**, adjusting the number based on the richness of video content available. Each question must be **descriptive** in nature, asking about **what** is observable, stated, or directly inferable from the descriptions. Focus on: events, actions, objects, people, states, locations, relationships, and summaries. If the video...

  3. [3]

    Questions may require integrating information from **one or more timestamp segments**, depending on the descriptive content being tested

  4. [6]

    what happened after/before/first/next/during

    The question should be **concise, and low in information density**. Do **not** ask temporal reasoning questions that rely on order/sequence (e.g., "what happened after/before/first/next/during", "what is the sequence of"). Questions should **not** test temporal order. **Output Format:** Return a valid JSON array. The array can contain **multiple items** (...

  5. [7]

    questions

    Generate **1 to 3 open-ended questions**, adjusting the number based on the richness of temporal rela- tionships available. If the video content does not support genuine temporal reasoning, output `{"questions": []}` with no additional text. Each question must be **temporal reasoning** in nature, testing one of the following: - **Order/Sequence**: Underst...

  6. [8]

    **Do not use any words that explicitly or implicitly reference the video’s timestamps or segment order.**

    Questions must be **self-contained** and clear. **Do not use any words that explicitly or implicitly reference the video’s timestamps or segment order.**

  7. [9]

    Do not introduce outside knowledge, assumptions, or speculative content (e.g., the person’s identity, emotions, intentions, or behind-the-scenes details)

    Base your questions **strictly on the provided descriptions**. Do not introduce outside knowledge, assumptions, or speculative content (e.g., the person’s identity, emotions, intentions, or behind-the-scenes details)

  8. [10]

    The question should be **concise, and low in information density**, shouldn’t contain information that allows guessing the answer **without watching the video**

Show all 26 references
  1. [11]

    question

    **Ensure diversity:** Do not generate duplicate questions that ask the same thing in different wording. Each question should explore a different temporal relationship in the video. **Output Format:** Return a valid JSON array. The array can contain **multiple items** (1 to 3 a...

  2. [12]

    why"** or **

    Generate **1 to 3 open-ended questions**, adjusting the number based on the richness of causal connections available. Each question must be **causal** in nature, asking **"why"** or **"how"** something happened based on evidence from the video. If the video content does not su...

  3. [13]

    Why did X happen?

    Each question **must**: - Require integrating information from **at least two distinct timestamp segments** (non-adjacent segments are encouraged). - Be framed as either: - **Cause-seeking:** "Why did X happen?", "How did X happen?" or "What caused X?" (X is an outcome) - **Ef...

  4. [14]

    Do not introduce outside knowledge, assumptions, or speculative content (e.g., the person’s identity, emotions, intentions, or behind-the-scenes details)

    Base your questions **strictly on the provided descriptions**. Do not introduce outside knowledge, assumptions, or speculative content (e.g., the person’s identity, emotions, intentions, or behind-the-scenes details). Do not invent any fact, event, or detail not explicitly sta...

  5. [15]

    the person

    The question should be **concise, abstract and low in information density**: - Use generic terms (e.g., "the person", "the individual", "the object") rather than specific labels (e.g., "the gymnast", "the chef") that reveal professional or situational context. - Do **not** men...

  6. [16]

    question

    **Ensure diversity:** Do not generate duplicate questions that ask the same thing in different wording. Each question should explore a different causal relationship in the video. **Output Format:** Return a valid JSON array. The array can contain **multiple items** (1 to 3 as ...

  7. [17]

    questions

    **First, evaluate the video content:** Determine if it contains clear causal relationships, dependencies, or alternative possibilities that support meaningful counterfactual reasoning. - **Unsuitable scenarios (output {"questions": []}):** Static scenes, purely descriptive mon...

  8. [18]

    Each question should test a distinct counter- factual scenario or explore a different causal relationship

    **Ensure diversity:** Do not generate duplicate questions. Each question should test a distinct counter- factual scenario or explore a different causal relationship

  9. [19]

    **Ensure reasoning complexity:** Prioritize questions that require synthesizing information from **at least two distinct timestamped segments** to establish a causal chain

  10. [20]

    What if

    Each question must: - Begin with a counterfactual premise ("What if...", "If... not/had...") - Alter **one specific event or condition** from the actual video, while keeping all other events unchanged - Do **not** include in the question any description of the consequence, dow...

  11. [21]

    question

    Base your questions **strictly on the provided descriptions**. Do not invent any fact, event, or detail not explicitly stated or logically implied. **Output Format:** Return a valid JSON array. The array can contain **multiple items** (1 to 3 as appropriate). Each item follows...

  12. [22]

    **Question Reasonableness (0-10 points)** - Does the question require watching the video to answer? (Cannot be answered by common sense alone) - Is the question relevant to the video content described in metadata? - Is the question clear, specific, and answerable based on the ...

  13. [23]

    to remove moisture

    **Answer Correctness (0-10 points)** - Is the answer directly supported by the metadata? (No hallucination) - **REASONABLE INFERENCES ARE ENCOURAGED**: If the answer makes minimal, logical inferences that are directly implied by the metadata (e.g., "to remove moisture" when me...

  14. [24]

    {question}

    **Metadata-Answer Alignment (0-10 points)** - Can all claims in the answer be traced back to specific segments? - Are there any contradictions between answer and metadata? **Scoring Guide:** - Total Score≥25: PASS - Total Score<25: FAIL - **Automatic FAIL only if these critica...

  15. [25]

    If the response to be judged contains ALL key information of the golden answer or expresses the same meaning using other sentences or synonyms, it is considered a match, and the output is 1

  16. [26]

    If the response to be judged does NOT contain the key information from the golden answer, it is considered a mismatch, and the output is 0

  17. [27]

    If such content exists, it is considered a mismatch, and the output is 0

    The response to be judged should NOT contain any content that is contradictory, conflicting, or unreason- able when inferred from the video content description. If such content exists, it is considered a mismatch, and the output is 0

  18. [28]

    If the response to be judged contains MOST of the key information of the golden answer, and does NOT contain any information that is contradictory, conflicting, or unreasonable when inferred from the video content description, it is considered a partial match, and the output i...

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Reviewed June 26, 2026 · model on record in the stance chip above.