Predictive Objectives Discard Exogenous Control-Relevant Features: A Controlled Mechanistic Study
Joint-embedding predictive objectives like JEPA learn representations by forecasting future latent states, but a new mechanistic study reveals they systematically discard features that agents cannot control, even when those features matter for downstream tasks and are easy to encode. Using a controlled experimental framework that independently varies controllability and relevance, researchers compared six learning objectives and found that temporal predictability optimization conflicts with control-relevance capture. This finding challenges a core assumption in self-supervised representation learning for embodied AI and suggests that prediction-based pretraining alone may leave agents blind to critical but exogenous environmental dynamics.
Modelwire context
ExplainerThe key finding isn't just that JEPA misses some features, it's that the failure is structural: optimizing for temporal predictability actively selects against encoding things the agent didn't cause, which means the problem can't be patched by adding more data or training longer.
This connects directly to the concurrent work on 'Automating the Design of Embodied Agent Architectures,' which treats perception, memory, and planning as modular choices subject to empirical search. That framing assumes the underlying representation layer is a reliable substrate to build on. This paper complicates that assumption: if the pretraining objective systematically blinds an agent to exogenous dynamics, then architectural search over downstream modules is optimizing on top of a flawed foundation. The two papers together suggest that embodied AI development faces a two-level problem, not one.
Watch whether robotics teams using JEPA-style pretraining report degraded performance specifically on tasks involving uncontrolled environmental dynamics (wind, moving obstacles, third-party agents) in the next 12 months. That would be the real-world confirmation this mechanistic finding needs.
Coverage we drew on
- Automating the Design of Embodied AgentArchitectures · arXiv cs.LG
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MentionsJEPA · joint-embedding predictive objectives · inverse dynamics · reward-grounded learning
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