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arxiv: 2604.10517 · v1 · submitted 2026-04-12 · 💻 cs.AI

Recognition: unknown

From Perception to Planning: Evolving Ego-Centric Task-Oriented Spatiotemporal Reasoning via Curriculum Learning

Xiaoda Yang , Yuxiang Liu , Shenzhou Gao , Can Wang , Jingyang Xue , Lixin Yang , Yao Mu , Tao Jin
show 3 more authors
Shuicheng Yan Zhimeng Zhang Zhou Zhao
Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:30 UTC · model grok-4.3

classification 💻 cs.AI
keywords egocentric reasoningspatiotemporal reasoningcurriculum learningvision-language modelslong-horizon planningembodied AItask-oriented reasoning
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The pith

EgoTSR curriculum evolves vision-language models from spatial perception to long-horizon planning, removing chronological biases.

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

Modern vision-language models perform well on static scenes yet struggle with the dynamic, sequential reasoning required for egocentric tasks in changing environments. The paper claims this limitation arises because models absorb fixed temporal patterns from passive video data, producing hallucinations and weak generalization when the setting demands active planning. EgoTSR counters the problem by training models in three explicit stages on a new 46-million-sample dataset: first building accurate spatial descriptions, then learning to tag current task states, and finally practicing long sequences of planning steps. A sympathetic reader would care because embodied systems such as robots need reliable spatiotemporal reasoning to act safely over time without inventing events based on learned time order. The reported outcome is 92.4 percent accuracy on long-horizon logical reasoning while preserving fine-grained perceptual detail.

Core claim

EgoTSR is a curriculum-based framework that teaches task-oriented spatiotemporal reasoning by progressing from explicit spatial understanding with Chain-of-Thought supervision, through weakly supervised task-state tagging, to long-horizon sequence planning on the EgoTSR-Data dataset of 46 million samples. This staged approach eliminates reliance on chronological priors acquired from passive video, yielding 92.4 percent accuracy on long-horizon logical reasoning tasks while retaining high perceptual precision and outperforming existing open- and closed-source models.

What carries the argument

The three-stage curriculum that advances egocentric reasoning from spatial perception to internalized task-state assessment to long-horizon planning.

If this is right

  • Models reach 92.4 percent accuracy on long-horizon logical reasoning tasks.
  • Performance remains high on fine-grained perceptual tasks while reasoning improves.
  • The approach outperforms both open-source and closed-source state-of-the-art models.
  • Generalization improves in dynamic, embodied environments by avoiding passive temporal priors.

Where Pith is reading between the lines

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

  • Similar staged curricula could reduce order biases in other multimodal models trained on internet-scale video.
  • Robotics applications may gain safer long-term planning by adopting this perception-to-planning progression.
  • The method suggests synthetic data pipelines can target specific reasoning deficits that real-world video alone cannot fix.

Load-bearing premise

The constructed EgoTSR-Data dataset and its three-stage organization accurately reflect the intended progression from spatial understanding to planning without introducing new biases during data creation.

What would settle it

Test the trained model on egocentric video inputs whose event order has been deliberately randomized or reversed and measure whether accuracy on long-horizon reasoning drops sharply.

Figures

Figures reproduced from arXiv: 2604.10517 by Can Wang, Jingyang Xue, Lixin Yang, Shenzhou Gao, Shuicheng Yan, Tao Jin, Xiaoda Yang, Yao Mu, Yuxiang Liu, Zhimeng Zhang, Zhou Zhao.

Figure 1
Figure 1. Figure 1: Overview of the EgoTSR Framework. This figure illustrates the evolving ego-centric task-oriented spatiotemporal reasoning process via curriculum learning. Left: The EgoTSR-Data of 46 million samples and the three-stage curriculum learning paradigm, transitioning from knowledge guidance (CoT Data) to capacity internalization (Tag Data) and generalization consolidation (LongTag Data). Top-Middle: Reasoning-e… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the EgoTSR-Data composition and the three-stage curriculum learning paradigm. Here shows the data structure and the framework evolving from explicit spatial reasoning to internalized intuitive judgment, and finally to complex long-horizon task planning. Different colors correspond to the three types of EgoTSR-data and three stages in the Curriculum Learning Paradigm: CoT (Blue), Tag (Green) and… view at source ↗
Figure 3
Figure 3. Figure 3: The dual-axis plot quantitatively demonstrates the effi￾cacy of the training Stage 3: LongTag. The Long Task Accuracy (Red Line) exhibits a steep monotonic ascent, surging from an initial 74.3% to a peak of 92.4%. At the same time, the Short Task Accuracy (Green Bars) demonstrates remarkable stability, oscillating narrowly between 86.6% and 88.7%. This confirms that the model acquires complex planning capa… view at source ↗
Figure 6
Figure 6. Figure 6: Visualization of the Task Completion Progress Curve. The figure aligns visual execution keyframes with the model’s real￾time inference. As the agent progresses through critical sub-goals, the Task Completion Curve exhibits a steady, monotonic ascent, accurately reflecting the accumulation of completed sub-tasks. This demonstrates our model’s capability to perform fine-grained temporal monitoring across lon… view at source ↗
Figure 5
Figure 5. Figure 5: Ablation Trajectories. The plot visualizes the variation curve of Curriculum Learning Paradigm stages (Blue), Subtask Planner (Green), Mixed Stages (Red), and our EgoTSR models with different training steps (Purple) converge to the Full Model (Gold Star), maximizing Accuracy while minimizing the Gap and verifying the superiority of our structured evolutionary strategy. 4.5. Ablation Study To validate EgoTS… view at source ↗
Figure 7
Figure 7. Figure 7: Sample data. This figure shows the detailed structure and examples of three types of data corresponding to our Curriculum Learning Paradigm: CoT Data (Blue) to establish explicit reasoning chains, Tag Data (Green) to foster internalized perception, and LongTag Data (Red) to enable complex decision-making for long-horizon tasks. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Robot manipulation case studies. 17 [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
read the original abstract

Modern vision-language models achieve strong performance in static perception, but remain limited in the complex spatiotemporal reasoning required for embodied, egocentric tasks. A major source of failure is their reliance on temporal priors learned from passive video data, which often leads to spatiotemporal hallucinations and poor generalization in dynamic environments. To address this, we present EgoTSR, a curriculum-based framework for learning task-oriented spatiotemporal reasoning. EgoTSR is built on the premise that embodied reasoning should evolve from explicit spatial understanding to internalized task-state assessment and finally to long-horizon planning. To support this paradigm, we construct EgoTSR-Data, a large-scale dataset comprising 46 million samples organized into three stages: Chain-of-Thought (CoT) supervision, weakly supervised tagging, and long-horizon sequences. Extensive experiments demonstrate that EgoTSR effectively eliminates chronological biases, achieving 92.4% accuracy on long-horizon logical reasoning tasks while maintaining high fine-grained perceptual precision, significantly outperforming existing open-source and closed-source state-of-the-art models.

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

3 major / 0 minor

Summary. The manuscript introduces EgoTSR, a curriculum-based framework for egocentric spatiotemporal reasoning in vision-language models. It constructs a 46-million-sample dataset (EgoTSR-Data) organized into three stages—Chain-of-Thought supervision, weakly supervised tagging, and long-horizon sequences—with the central claim that this progression eliminates chronological biases from passive video data, yielding 92.4% accuracy on long-horizon logical reasoning tasks while preserving fine-grained perceptual precision and outperforming both open- and closed-source state-of-the-art models.

Significance. If the performance claims prove robust, the work would meaningfully advance embodied AI by offering a structured curriculum to move models from explicit spatial perception to internalized planning, directly targeting a documented failure mode of temporal hallucinations in VLMs. The scale of the proposed dataset and the explicit three-stage design provide a concrete, testable path for bias mitigation that could influence future training paradigms for task-oriented agents.

major comments (3)
  1. [Abstract] Abstract: the claim that EgoTSR 'effectively eliminates chronological biases' is presented without any description of the bias quantification metric, the procedure used to measure residual temporal priors, or the statistical test confirming elimination; this is load-bearing for the central contribution.
  2. [Dataset Construction] Dataset section: the construction details for the 46M-sample EgoTSR-Data (including the rule-based or model-based synthesis of long-horizon sequences and weak tags) are absent, creating a circularity risk that reported gains reflect synthetic artifacts (e.g., action-order statistics or object-persistence patterns) rather than genuine reasoning improvements.
  3. [Experiments] Experiments section: no information is supplied on evaluation protocols, baseline re-implementations, train/test splits that control for chronological leakage, or statistical significance testing of the 92.4% accuracy figure, rendering the quantitative superiority claim impossible to assess from the manuscript.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments correctly identify areas where the manuscript requires greater transparency and elaboration to support its central claims. We address each point below and have revised the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that EgoTSR 'effectively eliminates chronological biases' is presented without any description of the bias quantification metric, the procedure used to measure residual temporal priors, or the statistical test confirming elimination; this is load-bearing for the central contribution.

    Authors: We agree that the abstract claim requires explicit supporting methodology to be fully substantiated. The manuscript's experimental section demonstrates reduced reliance on temporal order through controlled comparisons, but we acknowledge the absence of a dedicated quantification procedure and statistical test in the original text. In the revised manuscript we have added a new subsection (4.3) that defines the chronological bias score as the relative accuracy drop on temporally shuffled test sequences, describes the shuffling procedure, and reports bootstrap-based significance testing (1000 resamples). The abstract has been updated to reference this evaluation protocol. revision: yes

  2. Referee: [Dataset Construction] Dataset section: the construction details for the 46M-sample EgoTSR-Data (including the rule-based or model-based synthesis of long-horizon sequences and weak tags) are absent, creating a circularity risk that reported gains reflect synthetic artifacts (e.g., action-order statistics or object-persistence patterns) rather than genuine reasoning improvements.

    Authors: We accept that the original dataset section provided only an overview and omitted the precise synthesis rules, thereby leaving open the possibility of artifact-driven results. The revised manuscript expands Section 3 with full construction details: long-horizon sequences are generated via a rule-based task-graph engine that samples from a library of egocentric action templates with enforced diversity in ordering and object co-occurrence; weak tags are produced by a frozen VLM followed by automated consistency filtering and manual verification on a 5% subset. Pseudocode, prompt templates, and dataset statistics are now included to permit reproduction and to demonstrate that performance gains exceed what could be explained by simple order or persistence statistics alone. revision: yes

  3. Referee: [Experiments] Experiments section: no information is supplied on evaluation protocols, baseline re-implementations, train/test splits that control for chronological leakage, or statistical significance testing of the 92.4% accuracy figure, rendering the quantitative superiority claim impossible to assess from the manuscript.

    Authors: We agree that the experimental reporting was incomplete and prevents independent verification. The revised version adds a dedicated evaluation subsection (4.1) that specifies: (i) train/test splits performed at the video-ID level to eliminate chronological leakage, (ii) re-implementation details and hyperparameter search for all baselines, (iii) the exact prompting and decoding settings used for closed-source models, and (iv) statistical testing of the 92.4% result via five independent runs with reported mean, standard deviation, and two-tailed t-test p-values against the strongest baseline. These additions make the superiority claim fully assessable. revision: yes

Circularity Check

0 steps flagged

No circularity in the derivation chain.

full rationale

The paper describes an empirical curriculum-learning framework (three stages on a constructed 46M-sample dataset) whose central claims are performance numbers on long-horizon tasks. No equations, self-definitional reductions, fitted-parameter-as-prediction steps, or load-bearing self-citations appear in the provided text that would make any claimed result equivalent to its inputs by construction. The progression from spatial understanding to planning is presented as a training paradigm supported by data construction, not as a mathematical derivation that collapses tautologically. This is the normal case of an applied ML paper whose results remain falsifiable on external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

The central claim depends on the effectiveness of author-defined curriculum stages and the quality of a large synthetic dataset introduced in this work; no explicit free parameters, standard axioms, or externally validated invented entities are stated in the abstract.

invented entities (1)
  • EgoTSR three-stage curriculum no independent evidence
    purpose: To structure learning progression from spatial perception to task assessment to long-horizon planning
    Defined by the authors as the core training paradigm for this paper.

pith-pipeline@v0.9.0 · 5513 in / 1274 out tokens · 60494 ms · 2026-05-10T16:30:45.064564+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

23 extracted references · 22 canonical work pages · 13 internal anchors

  1. [1]

    Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

    Ahn, M., Brohan, A., Brown, N., Chebotar, Y ., Cortes, O., David, B., Finn, C., Fu, C., Gopalakrishnan, K., Hausman, K., et al. Do as i can, not as i say: Grounding language in robotic affordances.arXiv preprint arXiv:2204.01691,

    work page internal anchor Pith review arXiv

  2. [2]

    Rearrangement: A challenge for embodied ai.arXiv preprint arXiv:2011.01975, 2020

    Batra, D., Chang, A. X., Chernova, S., Davison, A. J., Deng, J., Koltun, V ., Levine, S., Malik, J., Mordatch, I., Mot- taghi, R., et al. Rearrangement: A challenge for embodied ai.arXiv preprint arXiv:2011.01975,

    work page arXiv 2011

  3. [4]

    $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

    doi: 10.48550. arXiv preprint ARXIV .2410.24164. Floridi, L. and Chiriatti, M. Gpt-3: Its nature, scope, limits, and consequences.Minds and machines, 30(4):681–694,

    work page internal anchor Pith review Pith/arXiv arXiv

  4. [5]

    Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation

    Fu, Z., Zhao, T. Z., and Finn, C. Mobile aloha: Learning bimanual mobile manipulation with low-cost whole-body teleoperation.arXiv preprint arXiv:2401.02117,

    work page internal anchor Pith review arXiv

  5. [6]

    Sycara, Matthew Johnson-Roberson, Dhruv Batra, Xiaolong Wang, Sebastian Scherer, Zsolt Kira, Fei Xia, and Yonatan Bisk

    Hu, Y ., Xie, Q., Jain, V ., Francis, J., Patrikar, J., Keetha, N., Kim, S., Xie, Y ., Zhang, T., Fang, H.-S., et al. Toward general-purpose robots via foundation models: A survey and meta-analysis.arXiv preprint arXiv:2312.08782,

    work page arXiv

  6. [7]

    Inner Monologue: Embodied Reasoning through Planning with Language Models

    Huang, W., Xia, F., Xiao, T., Chan, H., Liang, J., Florence, P., Zeng, A., Tompson, J., Mordatch, I., Chebotar, Y ., et al. Inner monologue: Embodied reasoning through planning with language models.arXiv preprint arXiv:2207.05608,

    work page internal anchor Pith review arXiv

  7. [8]

    What changed and what could have changed? state-change counterfactuals for procedure- aware video representation learning.arXiv preprint arXiv:2503.21055,

    Kung, C.-H., Ramirez, F., Ha, J., Chen, Y .-T., Crandall, D., and Tsai, Y .-H. What changed and what could have changed? state-change counterfactuals for procedure- aware video representation learning.arXiv preprint arXiv:2503.21055,

    work page arXiv

  8. [9]

    Reward design with language models.arXiv preprint arXiv:2303.00001, 2023

    Kwon, M., Xie, S. M., Bullard, K., and Sadigh, D. Re- ward design with language models.arXiv preprint arXiv:2303.00001,

    work page arXiv

  9. [10]

    SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

    Li, B., Wang, R., Wang, G., Ge, Y ., Ge, Y ., and Shan, Y . Seed-bench: Benchmarking multimodal llms with gener- ative comprehension.arXiv preprint arXiv:2307.16125, 2023a. Li, H., Yang, S., Chen, Y ., Tian, Y ., Yang, X., Chen, X., Wang, H., Wang, T., Zhao, F., Lin, D., et al. Cronusvla: Transferring latent motion across time for multi-frame prediction i...

    work page internal anchor Pith review arXiv

  10. [11]

    Evaluating Object Hallucination in Large Vision-Language Models

    Li, Y ., Du, Y ., Zhou, K., Wang, J., Zhao, W. X., and Wen, J.-R. Evaluating object hallucination in large vision- language models.arXiv preprint arXiv:2305.10355, 2023b. Li, Z. and Hoiem, D. Learning without forgetting.IEEE transactions on pattern analysis and machine intelligence, 40(12):2935–2947,

    work page internal anchor Pith review arXiv

  11. [12]

    Tempcompass: Do video llms really understand videos?,

    Liu, B., Zhu, Y ., Gao, C., Feng, Y ., Liu, Q., Zhu, Y ., and Stone, P. Libero: Benchmarking knowledge transfer for lifelong robot learning.Advances in Neural Information Processing Systems, 36:44776–44791, 2023a. Liu, H., Li, C., Wu, Q., and Lee, Y . J. Visual instruction tun- ing.Advances in neural information processing systems, 36:34892–34916, 2023b. ...

    work page arXiv

  12. [13]

    Is- bench: Evaluating interactive safety of vlm-driven embodied agents in daily household tasks.arXiv preprint arXiv:2506.16402, 2025

    Lu, X., Chen, Z., Hu, X., Zhou, Y ., Zhang, W., Liu, D., Sheng, L., and Shao, J. Is-bench: Evaluating interactive safety of vlm-driven embodied agents in daily household tasks.arXiv preprint arXiv:2506.16402,

    work page arXiv

  13. [14]

    Eureka: Human-Level Reward Design via Coding Large Language Models

    Ma, Y . J., Liang, W., Wang, G., Huang, D.-A., Bastani, O., Jayaraman, D., Zhu, Y ., Fan, L., and Anandkumar, A. Eureka: Human-level reward design via coding large lan- guage models.arXiv preprint arXiv:2310.12931,

    work page internal anchor Pith review arXiv

  14. [15]

    ALFWorld: Aligning Text and Embodied Environments for Interactive Learning

    Shridhar, M., Yuan, X., C ˆot´e, M.-A., Bisk, Y ., Trischler, A., and Hausknecht, M. Alfworld: Aligning text and embodied environments for interactive learning.arXiv preprint arXiv:2010.03768,

    work page internal anchor Pith review arXiv 2010

  15. [16]

    Gemini: A Family of Highly Capable Multimodal Models

    Team, G., Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Sori- cut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. Gemini: a family of highly capable multimodal models.arXiv preprint arXiv:2312.11805,

    work page internal anchor Pith review Pith/arXiv arXiv

  16. [17]

    Time Blindness: Why Video-Language Models Can't See What Humans Can?

    10 Evolving Ego-Centric Spatiotemporal Reasoning via Curriculum Learning Upadhyay, U., Ranjan, M., Shen, Z., and Elhoseiny, M. Time blindness: Why video-language models can’t see what humans can?arXiv preprint arXiv:2505.24867,

    work page internal anchor Pith review Pith/arXiv arXiv

  17. [18]

    Large language models still can’t plan (a bench- mark for llms on planning and reasoning about change)

    Valmeekam, K., Olmo, A., Sreedharan, S., and Kambham- pati, S. Large language models still can’t plan (a bench- mark for llms on planning and reasoning about change). InNeurIPS 2022 Foundation Models for Decision Mak- ing Workshop,

    2022

  18. [19]

    Voyager: An Open-Ended Embodied Agent with Large Language Models

    Wang, G., Xie, Y ., Jiang, Y ., Mandlekar, A., Xiao, C., Zhu, Y ., Fan, L., and Anandkumar, A. V oyager: An open- ended embodied agent with large language models.arXiv preprint arXiv:2305.16291,

    work page internal anchor Pith review Pith/arXiv arXiv

  19. [20]

    Multimodal chain-of-thought reasoning: A comprehensive survey.arXiv preprint arXiv:2503.12605, 2025

    Wang, Y ., Wu, S., Zhang, Y ., Yan, S., Liu, Z., Luo, J., and Fei, H. Multimodal chain-of-thought reasoning: A comprehensive survey.arXiv preprint arXiv:2503.12605,

    work page arXiv

  20. [21]

    Do egocentric video-language models truly understand hand- object interactions?arXiv preprint arXiv:2405.17719,

    Xu, B., Wang, Z., Du, Y ., Song, Z., Zheng, S., and Jin, Q. Do egocentric video-language models truly understand hand- object interactions?arXiv preprint arXiv:2405.17719,

    work page arXiv

  21. [22]

    Instructvla: Vision-language-action instruction tuning from understanding to manipulation.arXiv preprint arXiv:2507.17520, 2025

    Yang, S., Li, H., Chen, Y ., Wang, B., Tian, Y ., Wang, T., Wang, H., Zhao, F., Liao, Y ., and Pang, J. In- structvla: Vision-language-action instruction tuning from understanding to manipulation.arXiv preprint arXiv:2507.17520,

    work page arXiv

  22. [23]

    Multimodal Chain-of-Thought Reasoning in Language Models

    Zhang, Z., Zhang, A., Li, M., Zhao, H., Karypis, G., and Smola, A. Multimodal chain-of-thought reasoning in lan- guage models.arXiv preprint arXiv:2302.00923,

    work page internal anchor Pith review arXiv

  23. [24]

    Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

    Zhou, D., Sch ¨arli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al. Least-to-most prompting enables complex reasoning in large language models.arXiv preprint arXiv:2205.10625,

    work page internal anchor Pith review arXiv