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Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoring

T0 review · 3 major / 2 minor · reviewed 2026-06-28 · grok-4.3

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Pith's one-line read Hide-and-Seek detects failures in Vision-Language-Action robot policies by inducing localized signals from whole-trajectory labels alone.

desk verdict Hide-and-Seek applies inter- and intra-trajectory contrastive losses to pull localized failure signals from coarse trajectory labels in VLA models, but the evidence that those signals are truly causal rather than correlational remains thin. read the letter →

arxiv 2605.30834 v1 pith:ZC5VGOD3 submitted 2026-05-29 cs.RO cs.AI

Seongheon Park , Wendi Li , Changdae Oh , Samuel Yeh , Zsolt Kira , Michael Hagenow , Sharon Li This is my paper
classification cs.ROcs.AI
keywords VLAfailuredetectionruntimemonitoringcontrastivelearningcoarsesupervisionconformalpredictionroboticsembodiedAItrajectoryanalysis
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

Vision-Language-Action models allow robots to follow language instructions across tasks but often fail during execution in ways that break reliability. Current detection approaches either resample actions at high cost or apply failure labels uniformly across every timestep, which masks where problems actually start. Hide-and-Seek reframes the task as coarsely supervised learning and applies inter-trajectory and intra-trajectory contrastive objectives to surface failure-indicative actions without needing per-step annotations. Tests across LIBERO, VLABench, and a physical robot with three different VLA policies show state-of-the-art multi-task detection that balances accuracy against detection delay under conformal prediction and works on both seen and unseen tasks.

What carries the argument

Hide-and-Seek, a framework that applies inter-trajectory and intra-trajectory contrastive objectives to localize failure signals in VLA trajectories from coarse supervision.

What would settle it

A test set of trajectories containing both successful and failing segments where the method's localized predictions perform no better than uniform label propagation on failure detection metrics.

Watch

Extended reading notes

Core claim

Hide-and-Seek formulates VLA failure detection as a coarsely supervised learning problem. By combining inter-trajectory and intra-trajectory contrastive objectives, it localizes failure-indicative actions and induces temporally structured failure signals from trajectory-level supervision alone, without any step-level annotation. On LIBERO, VLABench, and a real-world platform with OpenVLA, π0, and π0.5 policies, the method reaches state-of-the-art multi-task failure detection with a practical accuracy-timeliness trade-off under conformal prediction and generalizes to both seen and unseen tasks.

Load-bearing premise

Inter-trajectory and intra-trajectory contrastive objectives can reliably produce time-localized failure signals when trained only on whole-trajectory success or failure labels.

Editorial extensions

If this is right

  • Failure detection becomes possible without action resampling or external models at runtime.
  • Temporally structured signals arise directly from trajectory-level labels via the contrastive objectives.
  • Conformal prediction yields controllable accuracy-timeliness trade-offs across multiple VLA policies.
  • Detection performance holds for both seen tasks and tasks not encountered during training.

Reading between the lines

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

  • The same coarse-supervision pattern could apply to other embodied monitoring tasks that currently require dense labels.
  • Runtime monitors built this way might allow a single model to handle safety checks for expanding task sets without retraining.
  • Integration with existing VLA policies could be tested by measuring how early the localized signals allow corrective interventions.
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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

3 major / 2 minor

Summary. The paper proposes Hide-and-Seek, a framework that treats VLA failure detection as a coarsely supervised problem. It combines inter-trajectory and intra-trajectory contrastive objectives to localize failure-indicative actions and produce temporally structured signals using only trajectory-level success/failure labels, without step-level annotations or external models. The method is evaluated on LIBERO, VLABench, and a real robotic platform using OpenVLA, π₀, and π₀.₅ policies, claiming state-of-the-art multi-task failure detection performance with a practical accuracy-timeliness trade-off under conformal prediction and good generalization to seen and unseen tasks.

Significance. If the central claim holds, the work would advance runtime monitoring for embodied VLAs by reducing reliance on expensive resampling or external models while using only coarse labels. The multi-policy evaluation (OpenVLA, π₀, π₀.₅), inclusion of real-robot experiments, and use of conformal prediction for calibrated detection are strengths that increase practical relevance. The result would be significant for reliable deployment if the contrastive objectives demonstrably extract temporally localized causal signals rather than spurious correlations.

major comments (3)
  1. [Abstract and Method (contrastive objectives)] The central claim that inter-trajectory and intra-trajectory contrastive objectives induce temporally structured failure signals from trajectory-level labels alone (Abstract) rests on an unverified assumption: that the learned representations localize failure actions rather than exploit action-distribution shifts between success and failure trajectories. No step-level ground truth is available during training, so it is unclear whether the induced signals are causal or merely correlational; this directly affects whether the reported SOTA detection performance under conformal prediction reflects genuine failure localization.
  2. [Abstract and Experiments] Evaluation claims of SOTA multi-task performance and generalization to unseen tasks (Abstract) are presented without quantitative numbers, ablation studies on the two contrastive terms, or error analysis of false-positive timing. This makes it impossible to verify the accuracy-timeliness trade-off or to rule out that performance gains arise from dataset biases rather than the proposed localization mechanism.
  3. [Conformal prediction integration] The conformal-prediction wrapper is presented as providing calibrated, practical detection, yet the manuscript does not report how the nonconformity scores are constructed from the contrastive embeddings or whether the temporal structure of the signals is preserved after calibration. This is load-bearing for the claimed timeliness property.
minor comments (2)
  1. [Abstract] The abstract contains a missing space after the period in 'π₀.₅.Our method'.
  2. [Method] Notation for the two contrastive losses should be introduced with explicit equations and variable definitions in the method section to improve readability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive feedback, which highlights important aspects of our claims regarding the contrastive objectives, evaluation details, and conformal prediction integration. We address each major comment below.

read point-by-point responses

  1. Referee: [Abstract and Method (contrastive objectives)] The central claim that inter-trajectory and intra-trajectory contrastive objectives induce temporally structured failure signals from trajectory-level labels alone (Abstract) rests on an unverified assumption: that the learned representations localize failure actions rather than exploit action-distribution shifts between success and failure trajectories. No step-level ground truth is available during training, so it is unclear whether the induced signals are causal or merely correlational; this directly affects whether the reported SOTA detection performance under conformal prediction reflects genuine failure localization.

    Authors: We recognize this as a valid point. Without step-level supervision, proving strict causality is inherently limited. The intra-trajectory contrastive objective is specifically intended to promote temporal localization by contrasting segments within trajectories, while the inter-trajectory term separates success and failure distributions. In revision, we will add a limitations subsection discussing the correlational nature and potential for distribution shifts, along with more extensive qualitative results showing signal alignment with failure events. revision: partial

  2. Referee: [Abstract and Experiments] Evaluation claims of SOTA multi-task performance and generalization to unseen tasks (Abstract) are presented without quantitative numbers, ablation studies on the two contrastive terms, or error analysis of false-positive timing. This makes it impossible to verify the accuracy-timeliness trade-off or to rule out that performance gains arise from dataset biases rather than the proposed localization mechanism.

    Authors: The full paper contains quantitative results in tables for multi-task performance on LIBERO, VLABench, and real robot, including comparisons to baselines. However, to better support the abstract claims, we will incorporate key quantitative figures into the abstract where appropriate. We will also add explicit ablation studies isolating the contribution of each contrastive term and include an error analysis section focusing on false positive timing and its impact on the trade-off. revision: yes

  3. Referee: [Conformal prediction integration] The conformal-prediction wrapper is presented as providing calibrated, practical detection, yet the manuscript does not report how the nonconformity scores are constructed from the contrastive embeddings or whether the temporal structure of the signals is preserved after calibration. This is load-bearing for the claimed timeliness property.

    Authors: We will expand the conformal prediction section to explicitly describe the nonconformity score construction, which uses the per-timestep failure signal derived from the contrastive embeddings as the score. Additionally, we will include analysis showing that the temporal ordering and structure of the signals remain intact post-calibration, supported by before-and-after timeliness metrics. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity detected

full rationale

The provided abstract and description introduce Hide-and-Seek as a new framework using inter-trajectory and intra-trajectory contrastive objectives to induce temporally structured signals from coarse trajectory labels alone. No equations, parameter-fitting steps, self-citations, or uniqueness theorems are quoted that would reduce any claimed prediction or localization result to an input by construction. The central claim is presented as an independent methodological contribution evaluated on external benchmarks (LIBERO, VLABench, real-robot data), satisfying the default expectation that the derivation chain remains self-contained without circular reduction.

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

Only abstract available; no explicit free parameters, axioms, or invented entities can be extracted or verified.

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Cite this review

Pith. "Pith review of Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoring." pith.science (2026). https://pith.science/paper/ZC5VGOD3

@misc{pith2026260530834,
  author       = {Pith},
  title        = {Pith review of: Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoring},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZC5VGOD3}},
  note         = {Machine review of arXiv:2605.30834}
}
abstract

Vision-Language-Action (VLA) models enable robots to follow natural language instructions and generalize across diverse tasks, but they remain vulnerable to execution failures that compromise reliability in real-world deployment. Detecting such failures during execution is therefore critical for the robust deployment of embodied systems. Existing failure detection methods either rely on expensive action resampling or external models, while alternatives propagate trajectory-level labels uniformly across every timestep, obscuring localized failure signals. In this paper, we propose \textbf{Hide-and-Seek}, a framework that formulates VLA failure detection as a coarsely supervised learning problem. By combining inter-trajectory and intra-trajectory contrastive objectives, Hide-and-Seek localizes failure-indicative actions and induces temporally structured failure signals from trajectory-level supervision alone, without any step-level annotation. We evaluate Hide-and-Seek on LIBERO, VLABench, and a real-world robotic platform across three representative VLA policies: OpenVLA, $\pi_0$, and $\pi_{0.5}$.Our method achieves state-of-the-art multi-task failure detection performance with a practical accuracy--timeliness trade-off under conformal prediction, and generalizes well to both seen and unseen tasks.

Figures

Figures reproduced from arXiv: 2605.30834 by the authors.

Figure 1
Figure 1. Hide-and-Seek Failure Detection. Failure trajectories contain a substantial amount of normal actions before failure onset, yet only a trajectory-level label is available during training, leaving the temporal structure entirely unknown (top). From this coarse supervision, Hide-and-Seek discovers the most failure-indicative actions (e.g., the failure onset and subsequent critical event) by contrasting scores across an… view at source ↗
Figure 2
Figure 2. Overall framework. Given a failure-success trajectory pair (τf , τs), the detector fϕ produces per-step failure scores st for both failure and successful trajectories. Linter (Eq. 2) enforces that the most failure-indicative action in the failure trajectory τf ranks higher than the hardest false￾positive in the successful trajectory τs. Lintra (Eq. 3) defines a proxy failure onset as tonset = arg maxt(st − st−1), an… view at source ↗
Figure 3
Figure 3. Detection accuracy–timeliness tradeoff on LIBERO-10 with OpenVLA and [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: (a) Comparison between uniform trajectory-level labeling and our approach; (b) effect of [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Average failure score trend across episodes for OpenVLA on LIBERO-10. The per￾timestep score is averaged over successful (blue) and failure (red) trajectories on (a) the training set, (b) the seen evaluation split, and (c) the unseen evaluation split. Shaded regions in…
Figure 6
Figure 6. Figure 6: Average failure score trend across episodes for π0 on LIBERO-10. We visualize the average per-timestep failure score st across episodes, computed after training the detector, for the training set and the seen/unseen evaluation splits. Results are shown for OpenVLA ( […
Figure 7
Figure 7. Figure 7: Visualization of failure scores for the OpenVLA policy across different tasks. A failure is declared at the earliest timestep t where the failure score st (red curve) exceeds the time-varying threshold ζt (green region) determined by conformal prediction. 17 [PITH_FUL…
Figure 8
Figure 8. Figure 8: Visualization of failure scores for the π0 policy across different tasks. A failure is declared at the earliest timestep t where the failure score st (red curve) exceeds the time-varying threshold ζt (green region) determined by conformal prediction. 18 [PITH_FULL_IMA…
Figure 9
Figure 9. Figure 9: Layer-wise ablation of OpenVLA internal representations for failure detection on LIBERO [PITH_FULL_IMAGE:figures/full_fig_p023_9.png]
Figure 10
Figure 10. Figure 10: Visualization of OpenVLA action embeddings on LIBERO-10. (a) Under uniform trajectory-level labeling, all timesteps in failure trajectories are assigned the failure label, mislabeling a substantial portion of normal actions as failures, resulting in significant overla…
Figure 11
Figure 11. Figure 11: Visualization of π0 action embeddings on LIBERO-10. These observations provide empirical support for our formulation: uniformly propagating trajectory￾level labels dilutes discriminative signals by assigning failure labels to predominantly correct actions, whereas iso…

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Forward citations

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Pith tools

Reviewed June 28, 2026 · model on record in the stance chip above.