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arxiv: 2607.00547 · v1 · pith:X2DTPIDSnew · submitted 2026-07-01 · 💻 cs.CV · cs.AI

EgoGapBench: Benchmarking Egocentric Action Selection in Multi-Agent Scenes

Jihyeok Jung (1) , Jeewu Lee (2) , Sanghyeop Kim (2) , Chanhee Han (3) , Seong Joon Oh (1) ((1) KAIST AI , (2) Sogang University , (3) Ministry of Science , ICT) This is my paper

Pith reviewed 2026-07-02 15:01 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords egocentric action selectionmulti-agent scenesmultimodal large language modelsbenchmarkperspective understandingegocentric visionaction selection
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The pith

MLLMs fail to select actions from their own viewpoint amid other agents.

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

The paper separates first-person video understanding from the distinct skill of adopting an egocentric perspective when other agents are visible. It presents EgoGapBench as a test that requires choosing the right action for the camera wearer rather than for anyone else in the scene. Humans solve these cases reliably. Current multimodal large language models, both open and closed, perform far worse and default to actions taken by visible others. Standard egocentric training data does not fix the problem and can make it worse, while training directly on the new benchmark narrows but does not close the gap to human performance.

Core claim

EgoGapBench shows that both open-source and proprietary MLLMs perform substantially worse than humans on Egocentric Action Selection and systematically choose actions performed by other visible agents. Fine-tuning on existing egocentric datasets fails to close this gap and can even reduce accuracy. Fine-tuning on EgoGapBench training data raises performance but still falls short of human reliability. The results indicate that the ability to select actions from the agent's own perspective is not acquired from first-person-view data alone.

What carries the argument

EgoGapBench, a diagnostic benchmark that isolates Egocentric Action Selection by forcing choice of an action appropriate to the camera wearer's perspective in multi-agent scenes without body cues.

If this is right

  • MLLMs require separate evaluation and training for egocentric perspective taking beyond scene understanding.
  • Existing egocentric datasets are insufficient to teach models to distinguish their own actions from those of others.
  • Targeted fine-tuning on multi-agent egocentric data improves but does not solve the deficit.
  • Models will continue to misattribute actions in crowded scenes unless the gap is addressed.

Where Pith is reading between the lines

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

  • The limitation could hinder reliable use of MLLMs in collaborative settings where distinguishing self from others matters.
  • New data collection focused on explicit perspective conflicts may be needed rather than relying on standard egocentric video.
  • The benchmark could serve as a template for testing other perspective-sensitive skills such as predicting what another agent will do.

Load-bearing premise

The benchmark tasks isolate egocentric perspective understanding from ordinary first-person scene understanding even when body cues are missing.

What would settle it

An MLLM trained solely on existing egocentric datasets that reaches human accuracy on EgoGapBench would falsify the claim that egocentric action selection cannot be learned from first-person-view data alone.

Figures

Figures reproduced from arXiv: 2607.00547 by (2) Sogang University, (3) Ministry of Science, Chanhee Han (3), ICT), Jeewu Lee (2), Jihyeok Jung (1), Sanghyeop Kim (2), Seong Joon Oh (1) ((1) KAIST AI.

Figure 1
Figure 1. Figure 1: An example item from EgoGapBench. The item asks the model to adopt a viewpoint [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Left: many egocentric datasets and benchmarks include visible camera-wearer body cues, allowing models to answer from body evidence rather than perspective. Middle: vertically flipping an EgoThink image yields a viewpoint that could not arise from a first-person position, yet models return nearly the same answer, indicating reliance on visible body cues. Right: on EgoNormia, replacing one option with an ac… view at source ↗
read the original abstract

Existing egocentric benchmarks have primarily constructed the egocentric setting from first-person-view data, which makes it difficult to evaluate egocentric perspective itself in isolation. However, understanding first-person-view input and taking an egocentric perspective are separable abilities, especially when first-person body cues are absent or when other agents are present. To isolate egocentric perspective understanding, we introduce EgoGapBench, a diagnostic benchmark for measuring action selection in multi-agent egocentric scenes. We define the ability measured by this benchmark as Egocentric Action Selection (EAS): selecting an appropriate action from the agent's perspective in the presence of other agents. On EgoGapBench, humans answer reliably, whereas both open-source and proprietary MLLMs perform substantially worse and systematically select actions performed by other visible agents. Fine-tuning on existing egocentric data fails to close this gap and can even be detrimental. In contrast, fine-tuning on EgoGapBench training data improves accuracy but does not reach human performance. These results show that EAS is difficult to acquire from first-person-view data alone, and that MLLMs should be evaluated and trained not only for scene understanding but also for egocentric action selection.

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

1 major / 0 minor

Summary. The paper introduces EgoGapBench, a diagnostic benchmark for Egocentric Action Selection (EAS) in multi-agent scenes. It argues that existing egocentric benchmarks conflate first-person-view scene understanding with true egocentric perspective-taking, especially absent body cues. The central empirical claim is that humans answer reliably on EgoGapBench while both open-source and proprietary MLLMs perform substantially worse and systematically select actions performed by other visible agents; fine-tuning on existing egocentric datasets fails to close the gap (and can degrade performance), while fine-tuning on EgoGapBench training data improves accuracy but does not reach human levels. The conclusion is that EAS cannot be acquired from first-person-view data alone.

Significance. If the benchmark successfully isolates perspective-taking from generic scene understanding, the results identify a clear limitation in current MLLMs and demonstrate that standard egocentric training corpora are insufficient for this capability. The human-model performance gap and the differential effect of fine-tuning regimes provide concrete, falsifiable baselines for future work on perspective-aware models in robotics and AR.

major comments (1)
  1. [Abstract] Abstract (and benchmark construction sections): the task prompt template, scene-selection criteria, and input format used to identify the ego agent among visible others are not described. The central claim that observed model errors reflect a failure of egocentric perspective-taking (rather than saliency bias or prompt ambiguity) is load-bearing on this isolation being achieved; without explicit evidence that the prompt unambiguously designates the camera viewpoint, the systematic 'other-agent' error pattern could arise from generic visual saliency rather than an EAS deficit.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on clarifying the benchmark construction. We address the major comment below and will revise the manuscript to strengthen the presentation of how the egocentric viewpoint is isolated.

read point-by-point responses

  1. Referee: [Abstract] Abstract (and benchmark construction sections): the task prompt template, scene-selection criteria, and input format used to identify the ego agent among visible others are not described. The central claim that observed model errors reflect a failure of egocentric perspective-taking (rather than saliency bias or prompt ambiguity) is load-bearing on this isolation being achieved; without explicit evidence that the prompt unambiguously designates the camera viewpoint, the systematic 'other-agent' error pattern could arise from generic visual saliency rather than an EAS deficit.

    Authors: We agree that the abstract and benchmark construction sections would benefit from explicit details on the prompt template, scene-selection criteria, and input format. In the revised version we will expand the benchmark construction section to include the full task prompt template (which designates the camera wearer as the ego agent via explicit instructions such as "select the action the person holding the camera should take"), the scene-selection criteria (multi-agent scenes with no visible body cues for the camera wearer, balanced action distributions), and the input format (first-person video frames with the ego agent identified solely by viewpoint). We will also add example prompts, qualitative error analysis, and a short ablation showing that rephrasing the prompt to emphasize "your action" does not alter the model error pattern. The systematic "other-agent" bias (rather than random or most-salient selection) combined with near-ceiling human performance already suggests the gap is not reducible to generic saliency, but the added material will make this isolation explicit. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical benchmark construction and evaluation

full rationale

The paper introduces EgoGapBench as a new diagnostic dataset for Egocentric Action Selection and reports human vs. MLLM performance plus fine-tuning experiments. No derivations, equations, fitted parameters renamed as predictions, or self-citation chains appear in the provided text. All claims rest on direct empirical measurements (accuracy numbers, systematic error patterns) rather than any reduction of outputs to inputs by construction. The central claim that EAS is difficult to acquire from FPV data alone is supported by the reported fine-tuning results on existing egocentric datasets versus the new benchmark, which are independent observations. This is a standard empirical benchmark paper with no load-bearing self-referential steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Benchmark paper with no mathematical model, fitted parameters, or new postulated entities.

pith-pipeline@v0.9.1-grok · 5782 in / 992 out tokens · 32513 ms · 2026-07-02T15:01:25.996082+00:00 · methodology

discussion (0)

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Reference graph

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