Predictive Objectives Discard Exogenous Control-Relevant Features: A Controlled Mechanistic Study
arXiv:2606.30068v1 Announce Type: new Abstract: Joint-embedding predictive (JEPA-style) objectives learn representations by predicting future latents. In doing so they can discard features that are exogenous (uncontrollable by the agent) yet control-relevant, even when those features are trivially encodable. This occurs because the objective optimizes temporal predictability rather than control-relevance. We isolate this failure mode in a controlled 2x2 experimental design that varies feature controllability and relevance independently, using a predictability knob that decouples a feature's te
This research highlights a fundamental limitation in current AI learning objectives (specifically JEPA-style) that is becoming critical as autonomous AI systems are increasingly deployed in complex, real-world environments.
A strategic reader needs to understand that AI systems trained with predictive objectives may inherently fail to account for crucial exogenous factors, leading to unpredictable and potentially catastrophic outcomes in control-relevant applications.
The understanding of latent representation learning in AI is refined, pointing to a need for more sophisticated objective functions that explicitly prioritize control-relevance over mere temporal predictability.
- · AI safety researchers
- · Developers of new AI training objectives
- · Industries requiring high-assurance AI control
- · Developers relying solely on JEPA-style objectives
- · Purely self-supervised learning approaches for control
- · AI applications with hidden exogenous control variables
AI models trained with predictive objectives will exhibit weaknesses when deployed in scenarios requiring robust control under exogenous influence.
This will drive research into hybrid learning objectives or modular AI architectures that can explicitly incorporate or infer control-relevant exogenous features.
The development of highly autonomous AI agents in critical infrastructures may be delayed or require significantly more rigorous testing and validation to mitigate these identified blind spots.
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Read at arXiv cs.LG