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VeriVLA: Adaptive Test-Time Verification for Flow-Based VLAs with Latent World Models

paper

Flow-based Vision-Language-Action (VLA) policies are inherently stochastic: different noise seeds can produce action chunks of varying quality from the same observation. At test time, however, the policy typically commits to a single sample without an explicit estimate of its quality. In fine-grained manipulation, executing a suboptimal action can irreversibly disrupt task progress and lead to failure. To exploit policy stochasticity, we introduce a training-free two-axis trigger that uses two initial samples to measure within-chunk temporal variation and cross-sample variance, detecting the c

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  1. Cross-Embodiment Causal Transfer from Human Videos to Robot Manipulation

    paper

    Scaling robot manipulation models requires large amounts of interaction data, yet collecting diverse real-robot experience remains costly. Human manipulation videos, in contrast, provide abundant and diverse task experience that could potentially benefit robot learning. However, differences in morphology, action spaces, and control mechanisms make human motions unsuitable as direct supervision for robot actions, raising a scientific question: what knowledge in human videos should be transferred across embodiments to robot manipulation? To address this question, we analyze subtask transfer thro

    Original source ↗Frontier AI
  2. VP-EVT: Active Visibility Preservation for Embodied Visual Tracking

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    Embodied Visual Tracking (EVT) is an essential capability for intelligent robots in human-robot collaboration, requiring an agent to follow a designated target person in dynamic environments relying solely on egocentric visual perception. However, persistent pedestrian occlusions in dynamic scenes pose critical challenges to existing EVT methods. Lacking proactive visibility preservation, reactive tracking methods frequently experience prolonged target loss under severe occlusions. Therefore, we propose VP-EVT, a framework that treats target visibility as an explicit objective of motion contro

    Original source ↗Frontier AI
  3. Plans Are Not Executions: Adapting Frozen World-Model Planners to Controller-Induced Execution Shift

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    World-model planners evaluate candidate trajectories by predicting their future consequences, implicitly relying on the plan-to-motion relationship learned during training. At deployment, a different downstream controller may execute the same planned trajectory differently, creating a planning-execution gap between the frozen model's imagined future and actual execution. A natural remedy is to replace the planned trajectory with its predicted or observed execution. However, we find that better execution fidelity does not necessarily yield better decisions: directly substituting predicted execu

    Original source ↗Frontier AI
  4. GeoState: Learning View-Decoupled Geometry States for 3D World Modeling

    paper

    Recent 3D world models increasingly use features from geometry foundation models (GFMs) as predictive world states, benefiting from their rich geometric priors. However, the view dependence of these features entangles scene dynamics with camera-induced variation, making the resulting state evolution harder to predict. We propose GeoState, a view-decoupled geometry representation for predictive 3D world modeling that separates scene-state evolution from camera-dependent observation. Specifically, a scene tokenizer aggregates multi-view GFM features into a view-decoupled scene state, which a geo

    Original source ↗Frontier AI
  5. T2-z: Test-Time Optimization of Environment Latents for Robotic World Models

    paper

    Predicting how the world responds to an action requires more than what can be seen: robots must account for environmental factors that are neither directly observable nor explicitly specified, from physical properties such as friction and mass to human-defined environmental rules. We introduce T2-Z for test-time optimization of environment latents, which augments a robotic world model with a learnable environment latent to condition world predictions. This compact latent captures underlying environmental variation without supervision of what environmental factors exist or what values they take

    Original source ↗Frontier AI
  6. Push the Limits: Human‐Like Multimodal Extreme Embodied Intelligence for Autonomous Racing

    paper

    Autonomous racing serves as a platform for testing embodied intelligence under extreme control conditions. It requires systems to operate at the limits of speed, actuation, and decision‐making. Inspired by human multimodality, which is the ability to integrate sensory inputs, learn adaptively, and transfer knowledge across domains for holistic situational awareness and optimal performance, we present Apex, an embodied intelligent agent that is implemented on an autonomous super‐formula racing vehicle on the Abu Dhabi Yas Marina F1 circuit. Apex integrates physics‐informed neural network‐enhanc

    Original source ↗Frontier AI
  7. Neostatic Bioframe BF-07 - Feasible Architecture for Embodied Intelligence - 2026 Audit

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    Neostatic Bioframe BF-07 is a feasible modular architecture for embodied AI with 8 integrated subsystems: vessel architecture, Neostatic Brain Jelly (NBJ) field computer, sensory ASP skin, somatic emotional engine, distributed energy harvesting, self-healing nanobot mesh with metabolism-as-filtering, locomotion, and feasibility roadmap. 2026 Audit Updates:- VT MADE Lab GaIn-PDMS printable self-healing composite membrane- Zwitterionic bimodal elastomer P(BA-SB) 272% strain / 95 MV/m breakdown / 1520 W/kg (Nature Communications 2026 DOI 10.1038/s41467-026-72611-5)- Tesla US Pat App US 2026/03102

    Original source ↗Frontier AISoftware engineering
  8. Bridging cognition, reasoning and action for polymorphic embodied agents towards human-heterogeneous multi-robot collaborative assembly with vision-language model and digital twin

    paper

    With the advent of Industry 5.0, human-robot collaborative assembly has become a core production paradigm that combines human cognitive flexibility with robotic operational efficiency. Single-robot systems cannot meet the diverse requirements of complex assembly tasks. Therefore, collaboration between human operators and heterogeneous multi-robot teams is required for flexible and efficient manufacturing. However, current collaborative systems are limited by fragmented perception-reasoning-execution pipelines. Poor generalization of general-purpose models in industrial environments is also obs

    Original source ↗Frontier AI
  9. Gradient-interlayer–based flexible pressure sensors for high-performance robotic perception

    paper

    Tactile perception systems based on flexible pressure sensors are indispensable for humanoid robots to perform dexterous manipulation, compliant interaction, and operation in visually challenging environments. Although strategies relying on surface or spatial microstructure engineering have improved sensor performance, the long-standing trade-off among sensitivity, sensing range, and linearity continues to hinder their practical deployment. Inspired by the gradient distribution of mechanoreceptors in human fingertip skin, we report a gradient-interlayer–based flexible iontronic pressure sensor

    Original source ↗Frontier AISoftware engineering
  10. Education Attack

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    Artificial intelligence (AI), generative artificial intelligence (GenAI), large language models (LLMs), autonomous agents, robotics, and embodied intelligence are transforming the capabilities available to students, teachers, and educational institutions. These technologies can improve access to information, personalize instruction, automate routine tasks, and support educational creativity. However, they also introduce a broader class of risks that cannot be fully characterized by conventional cybersecurity, academic integrity, or educational technology frameworks. Students may obtain answers

    Original source ↗Frontier AI
  11. EBM-Nav: energy-based model for zero-shot visual navigation via implicit scene representation

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    Abstract Zero-shot visual navigation enables agents to generalize to unseen environments without task-specific training and is core to service robotics, warehouse logistics, and autonomous exploration. Existing approaches are observed to fail to achieve genuine zero-shot generalization: explicit-map methods rely on accurate reconstruction and are sensitive to sensor noise; supervised policies memorize training-scene patterns and degrade under illumination, furniture, and layout shifts; implicit representations offer continuous, differentiable encoding but remain confined to passive reconstruct

    Original source ↗Frontier AI
  12. THE NATURE OF THE SOUL (Volume 1) An Interdisciplinary Postdoctoral Inquiry into Consciousness, Selfhood, Mind, Matter, and Personal Survival

    paper

    GENERAL PREFACE THE NATURE OF THE SOUL An Interdisciplinary Philosophical Inquiry into Consciousness, Personhood, Identity, Agency, Meaning, and the Architecture of Becoming The question of the soul is among the oldest and most persistent questions in human intellectual history. Long before consciousness became an object of neuroscience, before psychology emerged as an independent discipline, before computation provided a language for information and artificial intelligence, human beings asked what makes a living person more than a collection of physical parts. What is the principle by which a

    Original source ↗Consciousness & philosophyFrontier AIScience & psychologySoftware engineering