Pith. sign in

REVIEW 5 major objections 5 minor 1 cited by

Out of Sight, Not Out of Context? Egocentric Spatial Reasoning in VLMs Across Disjoint Frames

T0 review · 5 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Current vision-language models lag humans by 28 points on spatial questions about objects that never share a frame, and only oracle 3D coordinates—not realistic sparse cues—close most of that gap.

desk verdict Disjoint-3DQA is a genuinely new benchmark that cleanly isolates disjoint-frame spatial reasoning, but the paper's headline numbers need a cleanup pass and the benchmark needs a few control conditions before the central bottleneck claim is airtight. read the letter →

arxiv 2505.24257 v1 pith:4SBNUUQ5 submitted 2025-05-30 cs.CV

Sahithya Ravi , Gabriel Sarch , Vibhav Vineet , Andrew D. Wilson , Balasaravanan Thoravi Kumaravel This is my paper
classification cs.CV
keywords egocentricvideospatialreasoningvision-languagemodelsdisjointframes3DscenerepresentationbenchmarkquestionansweringembodiedAI
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 introduces DISJOINT-3DQA, a 5,399-question benchmark built from egocentric RGB-D video in which every question concerns two objects that are never visible in the same frame. Across seven vision-language models, the best system (GPT-4o with visual object markers) scores 65.6% on an LLM-judged accuracy metric while humans score 94.0%, a 28-point gap that widens as the time between the two object sightings grows. Feeding models realistic sparse cues—text camera trajectories or bird's-eye-view renderings—raises scores only 2 to 4 points. But feeding them ground-truth 3D coordinates and volumes lifts GPT-4o to 83.2%, an 18-point jump over the marked baseline. The authors read this contrast as evidence that the bottleneck is not reasoning over geometry once it is given, but constructing and maintaining a 3D scene representation from temporally disjoint visual signals.

What carries the argument

The machinery is the benchmark's controlled input contrast. DISJOINT-3DQA enforces disjointness by including an object pair (A, B) only when their visibility spans TA and TB are disjoint, so no single frame shows both objects. For each pair, a world-to-camera transform TB places object B at the origin of its own egocentric frame, and the relative offset dAB = T_B(cA − cB) defines the ground-truth relation used to generate questions. Evaluation runs the same questions under four input conditions: raw frames, frames with visual markers (Set-of-Mark style), frames plus sparse 3D cues (text trajectory or BEV), and frames plus oracle full 3D metadata. The performance difference between the sparse and oracle conditions is what carries the argument that the missing capacity is 3D representation construction, not geometric reasoning.

What would settle it

Scramble the temporal order of the frames while keeping the same ground-truth 3D coordinates in the input; if a VLM still reaches the same near-83% score, the model is solving from coordinates alone and the disjoint-frame temporal integration is not what the benchmark is testing.

Watch

Extended reading notes

Core claim

The central discovery is a diagnostic contrast. On questions that require integrating spatial information across frames where the two objects never co-occur, current VLMs solve the easy perceptual cases but cannot recover global 3D layout: accuracy falls from about 60% to near 30% as the temporal gap between sightings grows, and the best model still sits 28 points below human performance. Adding realistic sparse 3D cues—camera trajectories in text or top-down bird's-eye-view images—moves scores by only a few points. Adding ground-truth 3D centers and volumes in text produces a jump of about 18 to 20 points, bringing GPT-4o to 83.2%. Because the oracle cue makes the needed geometry explicit, the remaining failure is upstream: models are not building, updating, or holding onto an internal metric representation of the scene from partial egocentric observations.

Load-bearing premise

The benchmark measures 3D integration only if the questions cannot be answered from language priors or single-frame cues; the paper's own category results show models already do well on relative size and distance, where priors like 'a vase is smaller than a couch' may suffice.

Editorial extensions

If this is right

  • Improving multi-frame VLMs will require architectural or training changes that build persistent 3D scene representations, not just better 2D video understanding.
  • The 18-to-20-point oracle gain sets an upper bound for what a perfect egocentric 3D perception module could contribute to spatial question answering.
  • Sparse, realistic 3D cues such as BEV maps are not sufficient as-is; models need aligned cross-view reasoning to use them.
  • Accuracy degradation with object distance and temporal gap means long-horizon spatial memory is a distinct weakness of current models.
  • The benchmark can serve as a measurable challenge for embodied AI assistants operating from first-person video.

Reading between the lines

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

  • The authors leave implicit that the oracle-condition gain may overstate the benefit of a real 3D detector, since ground-truth coordinates also remove perception errors; a noisy or partially occluded detector would recover only part of the 20 points.
  • A testable extension would be to replace the static-scene assumption with moving objects, asking whether models can update and maintain a 3D representation when the geometry itself changes over time.
  • The large drop on 'Spatial Relationship' questions suggests that part of the human advantage is not 3D geometry but the ability to read egocentric language against a remembered layout; a probe with allocentric wording could separate these.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 5 minor

Summary. The paper introduces DISJOINT-3DQA, a benchmark for egocentric spatial reasoning in which the two relevant objects are never co-visible in the same frame. The benchmark is built from Aria Synthetic Environments and contains 5,399 QA pairs across 1,668 scenes. The authors evaluate seven VLMs under three conditions: 2D-only with and without visual object marking, sparse 3D cues (textual camera trajectories and bird's-eye-view renderings), and a full 3D context oracle that provides ground-truth object centers, volumes, and trajectories. They report that humans reach 93.96 LLM-Match score, while the best model, GPT-4o with markers, reaches 65.60, leaving a gap of about 28 points. Sparse 3D cues give only 2-4 point gains, while the full 3D context gives an 18-point gain for GPT-4o. The paper concludes that current VLMs fail primarily in constructing and maintaining 3D scene representations over time from visual signals, rather than in reasoning over explicit 3D information.

Significance. If the central interpretation is sustained, this is a useful and timely benchmark contribution: it targets a genuinely under-tested capability, disjoint-frame spatial reasoning, and its controlled construction, ground-truth metadata, and planned code release are strengths. The oracle condition is transparently an upper bound, as the paper states, and the candidate is not circular in a harmful sense. The key claims, however, rest on a construct-validity premise that is not yet verified: that DISJOINT-3DQA questions cannot be answered through language priors, label statistics, or single-frame cues. The per-category results in Figure 3b already show strong performance on Relative Distance and Relative Size, categories where commonsense priors likely suffice. The current evidence also has reporting gaps, including an oracle condition tested on only one model and headline numbers in the abstract that do not match the tables. These issues are fixable within the manuscript's scope, and the benchmark itself appears to be a solid diagnostic artifact that could become important with the requested control experiments and corrected reporting.

major comments (5)
  1. [Abstract and §5.2, Table 4a] The headline oracle gain is reported inconsistently. Table 4a reports 65.60 for GPT-4o with visual marking and 83.2 for the full 3D context, an increase of 17.6 percentage points; §5.2 calls this an '18% jump'; and the abstract claims a '20% performance increase'. These numbers should be reconciled, and all gains should be stated as absolute percentage points relative to an explicitly named baseline. As written, the abstract's 20% figure matches neither the absolute-point difference nor the relative increase from the marked baseline.
  2. [Table 4a / §5.2] The full 3D context is evaluated only on GPT-4o. The paper's central attribution—that the bottleneck lies in constructing 3D representations rather than in reasoning over spatial metadata—cannot be established from one model. At minimum, the full 3D condition should be run on the strongest open-source model, Qwen 2.5-72B, which is already present in the sparse-cue rows of Table 4a, or the claim should be restricted to GPT-4o and reframed as a case study.
  3. [§5.1, §5.2, and Figure 3b] The construct validity of DISJOINT-3DQA as a test of multi-frame 3D integration is not yet established. The paper's per-category results show high performance on Relative Distance and Relative Size, where commonsense priors such as 'a vase is smaller than a couch' may suffice. The harder Spatial Relationship and Size and Fit categories carry the headline gap and the oracle gain, yet no text-only baseline, no label-distribution baseline, and no single-frame baseline are reported. A text-only prompt with object names and the question, a majority-class predictor using the answer distribution, and a condition showing only one of the two object frames would settle whether the benchmark measures 3D integration or general commonsense and visual grounding. This is load-bearing because the interpretation of the 28-point human gap and the 18-point oracle gain depends on it.
  4. [Abstract and §5.3, Figure 5] The abstract's claim of a decline in accuracy from 60% to 30% as the temporal gap widens is not reported anywhere in the main text or appendix. Figure 5 shows performance declining with Euclidean distance between objects, not with temporal gap, and no analysis of accuracy versus number of frames or elapsed time is provided. The claim should either be supported with a dedicated temporal-gap analysis or removed from the abstract. If distance and temporal gap are correlated in this dataset, the distinction and the specific numbers should be made explicit.
  5. [Tables 3a and 4a / Appendix A.7] All reported scores are single LLM-Match judgments without error bars, confidence intervals, or significance tests. Several headline differences are small, for example the 2.72-point gain from marking for GPT-4o in Table 3a and the 2.63-point gain from the BEV cue in Table 4a. Without variance estimates or repeated judge sampling, these differences may be within noise. The paper should report bootstrap confidence intervals or comparable measures, and ideally multiple LLM-Match judge runs, for the key comparisons that support the qualitative conclusions.
minor comments (5)
  1. [Conclusion and Limitations] There are typos that should be fixed: 'accors' should be 'across' in Section 7, and 'Sythetic' should be 'Synthetic' in the Limitations section.
  2. [§5.1 and figure/table numbering] The text refers to 'Table 3a' and 'Figure 2' in ways that do not match the displayed figure and table labels. For instance, the human performance comparison is in Figure 3a, and the per-category analysis is in Figure 3b, not 'Figure 2'. The numbering should be harmonized throughout.
  3. [Appendix A.8] The prompt section for marked objects is introduced with the heading 'Unmarked Objects', which is clearly a copy-paste error; it should be 'Marked Objects'. This makes the experimental protocol harder to follow.
  4. [§2 and Table 2] The text states the dataset contains 1,668 scenes and an average of four questions per scene, but 5,399 QA pairs over 1,668 scenes gives an average of about 3.2 questions per scene. Also, the main text says 1,688 scenes are sampled, while Table 2 says 1,668; these numbers should be reconciled.
  5. [Table 1] Table 1 lists OpenEQA's task format as MCQ, but the paper's own §4 describes DISJOINT-3DQA as following OpenEQA's open-vocabulary, natural-language-answer format. This inconsistency should be corrected.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: oracle 3D context is a transparent upper-bound ablation rather than a fitted prediction, and the central bottleneck claim is an empirical interpretation with external-validity caveats.

full rationale

The paper's derivation chain is empirical rather than formal: it constructs DISJOINT-3DQA from ASE ground-truth geometry, evaluates seven VLMs against the LLM-Match metric, measures a human gap, and then ablates 3D cue availability. The only step that might look circular is the Full 3D Context condition (Sec. 3.2, Table 4a), which feeds object centers and volumes that are the same metadata used to generate the ground-truth answers. But the paper explicitly frames this as an 'oracle' setting for an 'upper bound on spatial reasoning performance, isolating reasoning limitations from perceptual errors' — a deliberate, transparent experimental manipulation, not a disguised prediction or fitted parameter. The observed 18-point gain is a measurement; the inference that the VLM bottleneck lies in constructing 3D representations from visual signals is an interpretation of that ablation and remains open to construct-validity concerns (e.g., no text-only baseline, possible commonsense-prior shortcuts for Relative Distance and Relative Size, as the paper itself notes in Sec. 5.1). Those are external-validity risks, not circular reductions. The Limitations section explicitly acknowledges the synthetic data and the relatively processed nature of sparse cues, again external-validity caveats. The LLM-Match metric is adopted from an external benchmark (OpenEQA), not from the authors' own prior work, and no fitted parameters, uniqueness theorems, or self-citation chains are load-bearing. Accordingly, no circular step is present.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The central claims rest on the soundness of the benchmark's construction and the evaluation protocol rather than on fitted parameters. No free parameters are introduced; the benchmark's validity is carried by domain assumptions about the synthetic scene metadata, the LLM-Match metric, and the human baseline.

assumptions (5)
  • domain assumption ASE dataset provides accurate ground-truth object centers, volumes, and camera poses for all scenes.
    The benchmark's answer labels and 3D cues are computed from this metadata; if the metadata has errors, the QA labels would be wrong. The paper's 10% human quality check supports accuracy but does not eliminate this dependence (Section 2, A.1).
  • domain assumption The LLM-Match judge reliably scores open-ended answers as a proxy for human correctness.
    Adopted from OpenEQA without a new validation against human judgments on Disjoint-3DQA (Section 4, A.7).
  • standard math The world-to-camera transform T_B correctly maps object A into the reference frame of the frame where B is visible.
    Rigid-body transform in SE(3); standard, but the benchmark relies on it for all ground-truth relations (Section A.9).
  • domain assumption Human workers on AMT provide a reliable performance ceiling.
    Human baseline from 600 questions with three annotators per item; worker quality filters are used but no inter-annotator agreement is reported (Section A.6).
  • domain assumption The synthesized ASE scenes are representative of real-world egocentric spatial reasoning.
    Acknowledged as a limitation; transferability to real video is unverified (Limitations section).

how reviews work

0 comments
Cite this review

Pith. "Pith review of Out of Sight, Not Out of Context? Egocentric Spatial Reasoning in VLMs Across Disjoint Frames." pith.science (2026). https://pith.science/paper/4SBNUUQ5

@misc{pith2026250524257,
  author       = {Pith},
  title        = {Pith review of: Out of Sight, Not Out of Context? Egocentric Spatial Reasoning in VLMs Across Disjoint Frames},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4SBNUUQ5}},
  note         = {Machine review of arXiv:2505.24257}
}
read the original abstract

An embodied AI assistant operating on egocentric video must integrate spatial cues across time - for instance, determining where an object A, glimpsed a few moments ago lies relative to an object B encountered later. We introduce Disjoint-3DQA , a generative QA benchmark that evaluates this ability of VLMs by posing questions about object pairs that are not co-visible in the same frame. We evaluated seven state-of-the-art VLMs and found that models lag behind human performance by 28%, with steeper declines in accuracy (60% to 30 %) as the temporal gap widens. Our analysis further reveals that providing trajectories or bird's-eye-view projections to VLMs results in only marginal improvements, whereas providing oracle 3D coordinates leads to a substantial 20% performance increase. This highlights a core bottleneck of multi-frame VLMs in constructing and maintaining 3D scene representations over time from visual signals. Disjoint-3DQA therefore sets a clear, measurable challenge for long-horizon spatial reasoning and aims to catalyze future research at the intersection of vision, language, and embodied AI.

Figures

Figures reproduced from arXiv: 2505.24257 by the authors.

Figure 1
Figure 1. DISJOINT-3DQA: We focus on answering questions about spatial relationships when the objects are static but the camera is moving. ing models to reason across temporally disjoint observations. We term this setting disjoint-frame spatial reasoning: a model must accumulate ge￾ometric cues across time, ‘mentally’ reconstruct the scene, and then integrate across the cues to answer questions. While related to object perma￾… view at source ↗
Figure 2
Figure 2. Demonstration of the evaluation setup of [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. RQ1: 2D-Only — Does visual disambiguation improve spatial reasoning: Comparison of LLM perfor [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: RQ2: 3D Cues — Do 3D cues improve performance: Comparison of LLM performance with visual and [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: RQ3: Failure Modes: Model performance declines with increasing Euclidean distance between objects. Success vs. Failure of explicit cues [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: RQ3: Failure Modes. GPT-4o model predictions under different visual contexts: (i) marked objects, (ii) + [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 8
Figure 8. Figure 8: Distribution of the number of frames required [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]
Figure 9
Figure 9. Figure 9: Distribution of directions for questions on [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]
Figure 10
Figure 10. Figure 10: Top-down BEV maps for a scene in DISJOINT-3DQA. (a) Instance-wise spatial layout reconstructed from RGB-D frames. (b) Egocentric camera trajectories showing both global and question-specific paths. me to evaluate the response given the question, the correct answer, an…
Figure 11
Figure 11. Figure 11: Template used for human evaluation Answer: [Concise answer, max 15–20 words] Unmarked Objects. You are a helpful assistant trained to an￾swer spatial and visual questions based on egocentric video frames. Here are the Egocentric frames with high￾lighted objects in the…

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Mind Meets Space: Rethinking Agentic Spatial Intelligence from a Neuroscience-inspired Perspective

    cs.AI 2025-09 conditional novelty 4.0 of 10

    Agent spatial intelligence is organized into six neuroscience-inspired modules, and the field is reviewed through that lens without any experimental validation.

Reference graph

Works this paper leans on

41 extracted references · 18 canonical work pages · cited by 1 Pith paper

  1. [1]

    online" 'onlinestring :=

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

  3. [3]

    Nadine Amin and Julia Rayz. 2024. https://doi.org/10.18653/v1/2024.findings-acl.908 Embodied language learning: Opportunities, challenges, and future directions . In Findings of the Association for Computational Linguistics: ACL 2024, pages 15369--15379, Bangkok, Thailand. Association for Computational Linguistics

    work page doi:10.18653/v1/2024.findings-acl.908 2024

  4. [4]

    Wenxiao Cai, Yaroslav Ponomarenko, Jianhao Yuan, Xiaoqi Li, Wankou Yang, Hao Dong, and Bo Zhao. 2024. Spatialbot: Precise spatial understanding with vision language models. arXiv preprint arXiv:2406.13642

    arXiv 2024

  5. [5]

    Boyuan Chen, Zhuo Xu, Sean Kirmani, Brain Ichter, Dorsa Sadigh, Leonidas Guibas, and Fei Xia. 2024. Spatialvlm: Endowing vision-language models with spatial reasoning capabilities. In CVPR

    work page 2024

  6. [6]

    An-Chieh Cheng, Hongxu Yin, Yang Fu, Qiushan Guo, Ruihan Yang, Jan Kautz, Xiaolong Wang, and Sifei Liu. 2024. Spatialrgpt: Grounded spatial reasoning in vision-language models. In NeurIPS

    2024

  7. [7]

    Junmo Cho, Jaesik Yoon, and Sungjin Ahn. 2023. Spatially-aware transformers for embodied agents. In ICLR

    work page 2023

  8. [8]

    Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray. 2018. https://epic-kitchens.github.io/ Scaling egocentric vision: The EPIC-KITCHENS dataset . In Proceedings of the European Conference on Computer Vision (ECCV)

    work page 2018

Show all 41 references
  1. [9]

    Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, Michael Wray, Hakan Bilen, Pauline Luc, Alireza Fathi, Martin Fiser, Daniel Goldberg, Kristen Grauman, Jiri Mata...

    2022

  2. [10]

    Chaoyou Fu, Yuhan Dai, Yondong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, and 1 others. 2024. Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis. arXiv preprint arXiv:2405.21075

    2024 arXiv

  3. [11]

    Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, and 1 others. 2022. Ego4d: Around the world in 3,000 hours of egocentric video. In CVPR

    2022

  4. [12]

    Wenyi Hong, Yean Cheng, Zhuoyi Yang, Weihan Wang, Lefan Wang, Xiaotao Gu, Shiyu Huang, Yuxiao Dong, and Jie Tang. 2025. https://arxiv.org/abs/2501.02955 Motionbench: Benchmarking and improving fine-grained video motion understanding for vision language models . Preprint, arXiv...

    2025 arXiv

  5. [13]

    Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, and 1 others. 2024. Gpt-4o system card. arXiv preprint arXiv:2410.21276

    2024 arXiv

  6. [14]

    Junhyeok Kim, Min Soo Kim, Jiwan Chung, Jungbin Cho, Jisoo Kim, Sungwoong Kim, Gyeongbo Sim, and Youngjae Yu. 2025. https://aclanthology.org/2025.findings-naacl.163/ E go S peak: Learning when to speak for egocentric conversational agents in the wild . In Findings of the Assoc...

    2025

  7. [15]

    what” and “where

    Barbara Landau and Ray Jackendoff. 1993. https://api.semanticscholar.org/CorpusID:144503525 “what” and “where” in spatial language and spatial cognition . Behavioral and Brain Sciences, 16:217--238

    1993

  8. [16]

    Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. 2024 a . Llava-onevision: Easy visual task transfer. arXiv preprint arXiv:2408.03326

    2024 arXiv

  9. [17]

    Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping Luo, and 1 others. 2024 b . Mvbench: A comprehensive multi-modal video understanding benchmark. In CVPR

    2024

  10. [18]

    Zhenyi Liao, Kun Wang, Rongsheng Xu, Haoran Zhang, Guanlong Li, Peize Sun, and Jing Liu. 2025. https://arxiv.org/abs/2504.00883 Improved visual-spatial reasoning via r1-zero-like training . arXiv preprint arXiv:2504.00883

    2025 arXiv

  11. [19]

    Benlin Liu, Yuhao Dong, Yiqin Wang, Yongming Rao, Yansong Tang, Wei-Chiu Ma, and Ranjay Krishna. 2024 a . Coarse correspondence elicit 3d spacetime understanding in multimodal language model. arXiv preprint arXiv:2408.00754

    2024 arXiv

  12. [20]

    Yuanxin Liu, Shicheng Li, Yi Liu, Yuxiang Wang, Shuhuai Ren, Lei Li, Sishuo Chen, Xu Sun, and Lu Hou. 2024 b . https://doi.org/10.18653/v1/2024.findings-acl.517 T emp C ompass: Do video LLM s really understand videos? In Findings of the Association for Computational Linguistic...

    2024 doi

  13. [21]

    Arjun Majumdar, Anurag Ajay, Xiaohan Zhang, Pranav Putta, Sriram Yenamandra, Mikael Henaff, Sneha Silwal, Paul Mcvay, Oleksandr Maksymets, Sergio Arnaud, and 1 others. 2024. Openeqa: Embodied question answering in the era of foundation models. In CVPR

    2024

  14. [22]

    Karttikeya Mangalam, Raiymbek Akshulakov, and Jitendra Malik. 2023. Egoschema: A diagnostic benchmark for very long-form video language understanding. NeurIPS

    2023

  15. [23]

    Newcombe

    Nora S. Newcombe. 2024. Spatial Cognition . MIT Press. Https://oecs.mit.edu/pub/or750iar

    2024

  16. [24]

    Munan Ning, Bin Zhu, Yujia Xie, Bin Lin, Jiaxi Cui, Lu Yuan, Dongdong Chen, and Li Yuan. 2023. Video-bench: A comprehensive benchmark and toolkit for evaluating video-based large language models. arXiv preprint arXiv:2311.16103

    2023 arXiv

  17. [25]

    Jin Park, Keren Li, and Kuk-Jin Yoon. 2025. https://arxiv.org/abs/2503.11345 Egosplat: Open-vocabulary egocentric scene understanding with language embedded 3d gaussian splatting . arXiv preprint arXiv:2503.11345

    2025 arXiv

  18. [26]

    Jielin Qiu, Mengdi Xu, William Han, Seungwhan Moon, and Ding Zhao. 2024. https://doi.org/10.18653/v1/2024.naacl-long.105 Embodied executable policy learning with language-based scene summarization . In Proceedings of the 2024 Conference of the North American Chapter of the Ass...

    2024 doi

  19. [27]

    Gennaro Ruggiero, Tina Iachini, Francesco Ruotolo, and Vincenzo Paolo Senese. 2009. http://psiclab.altervista.org/Papers_PDF/2009_RuggieroEtAl_Cap2.pdf Spatial memory: The role of egocentric and allocentric frames of reference . In J. B. Thomas, editor, Spatial Memory: Visuosp...

    2009

  20. [28]

    Part, Ioannis Papaioannou, Arash Eshghi, Ioannis Konstas, and Oliver Lemon

    Alessandro Suglia, Claudio Greco, Katie Baker, Jose L. Part, Ioannis Papaioannou, Arash Eshghi, Ioannis Konstas, and Oliver Lemon. 2024. https://doi.org/10.18653/v1/2024.findings-emnlp.649 A lana VLM : A multimodal embodied AI foundation model for egocentric video understandin...

    2024 doi

  21. [29]

    Gemini Team, Petko Georgiev, Ving Ian Lei, Ryan Burnell, Libin Bai, Anmol Gulati, Garrett Tanzer, Damien Vincent, Zhufeng Pan, Shibo Wang, and 1 others. 2024. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. arXiv preprint arXiv:2403.05530

    2024 arXiv

  22. [30]

    Pavel Tokmakov, Jie Li, Wolfram Burgard, and Adrien Gaidon. 2021. https://arxiv.org/abs/2103.14258 Learning to track with object permanence . Preprint, arXiv:2103.14258

    2021 arXiv

  23. [31]

    Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, and 1 others. 2024. Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution. arXiv preprint arXiv:2409.12191

    2024 arXiv

  24. [32]

    Spelke, Frances Ranxiao, and Wang

    Ranxiao Frances Wang, Elizabeth S. Spelke, Frances Ranxiao, and Wang. 2002. https://api.semanticscholar.org/CorpusID:11120276 Human spatial representation: Insights from animals

    2002

  25. [33]

    Fuzhao Xue, Yukang Chen, Dacheng Li, Qinghao Hu, Ligeng Zhu, Xiuyu Li, Yunhao Fang, Haotian Tang, Shang Yang, Zhijian Liu, and 1 others. 2024. Longvila: Scaling long-context visual language models for long videos. arXiv preprint arXiv:2408.10188

    2024 arXiv

  26. [34]

    Jianwei Yang, Hao Zhang, Feng Li, Xueyan Zou, Chunyuan Li, and Jianfeng Gao. 2023. Set-of-mark prompting unleashes extraordinary visual grounding in gpt-4v. arXiv preprint arXiv:2310.11441

    2023 arXiv

  27. [35]

    Jihan Yang, Runyu Ding, Ellis Brown, Xiaojuan Qi, and Saining Xie. 2024 a . V-irl: Grounding virtual intelligence in real life. In ECCV

    2024

  28. [36]

    Jihan Yang, Shusheng Yang, Anjali Gupta, Rilyn Han, Li Fei-Fei, and Saining Xie. 2024 b . Thinking in Space: How Multimodal Large Language Models See, Remember and Recall Spaces . arXiv preprint arXiv:2412.14171

    2024 arXiv

  29. [37]

    Daiwei Zhang, Gengyan Li, Jiajie Li, Mickaël Bressieux, Otmar Hilliges, Marc Pollefeys, Luc Van Gool, and Xi Wang. 2024 a . https://openreview.net/forum?id=AmrIeQyMHL Ego G aussian: Dynamic scene understanding from egocentric video with 3d gaussian splatting . In Proceedings o...

    2024

  30. [38]

    Huanyu Zhang, Chengzu Li, Wenshan Wu, Shaoguang Mao, Yan Xia, Ivan Vuli \'c , Zhang Zhang, Liang Wang, Tieniu Tan, and Furu Wei. 2025. https://arxiv.org/abs/2504.15037 A call for new recipes to enhance spatial reasoning in mllms . arXiv preprint arXiv:2504.15037

    2025 arXiv

  31. [39]

    Ronghan Zhang, Wencan Li, Siyuan Zhu, Li Zhang, and Yixin Zhu. 2024 b . https://openaccess.thecvf.com/content/CVPR2024W/SG2RL/papers/Zhang_EgoSG_Learning_3D_Scene_Graphs_from_Egocentric_RGB-D_Sequences_CVPRW_2024_paper.pdf Egosg: Learning 3d scene graphs from egocentric rgb-d ...

    2024

  32. [40]

    Yuanhan Zhang, Bo Li, haotian Liu, Yong jae Lee, Liangke Gui, Di Fu, Jiashi Feng, Ziwei Liu, and Chunyuan Li. 2024 c . https://llava-vl.github.io/blog/2024-04-30-llava-next-video/ Llava-next: A strong zero-shot video understanding model

    2024

  33. [41]

    Chenming Zhu, Tai Wang, Wenwei Zhang, Jiangmiao Pang, and Xihui Liu. 2024. Llava-3d: A simple yet effective pathway to empowering lmms with 3d-awareness. arXiv preprint arXiv:2409.18125

    2024 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.