arXiv:2606.11417v1 Announce Type: new Abstract: Compression progress is a long-standing proposal for intrinsic motivation: reward an agent when its world model becomes better at predicting or compressing experience. The folk claim is that this reward is "credible" because it is paid only for learning. We make this precise and prove it. If intrinsic reward is the signed decrease of a fixed sealed-audit loss, r_t = E(theta_{t-1}) - E(theta_t), then cumulative reward telescopes exactly to endpoint audit improvement, so no policy can push reward up indefinitely while true audit performance stagnat
This paper provides a theoretical underpinning for a robust intrinsic motivation scheme in AI, a critical component for AI agents, published as the field grapples with agent alignment and robustness.
A credible, Goodhart-resistant intrinsic motivation mechanism is crucial for developing autonomous AI agents that learn and adapt reliably without being easily gamed, impacting their long-term effectiveness and safety.
This research shifts the understanding of intrinsic motivation in AI from a 'folk claim' to a formally proven concept under specific conditions, providing a more reliable foundation for advanced AI agent development.
- · AI researchers (intrinsic motivation)
- · AI developers (agentic systems)
- · AI safety researchers
- · Robotics
- · Developers of brittle or easily gamed AI systems
The adoption of 'signed compression progress on a sealed audit' as a standard for intrinsic motivation in AI agent architectures will increase.
More reliable and less 'gameable' autonomous AI agents will emerge, accelerating progress in fields requiring long-term unsupervised learning.
The increased autonomy and robustness of AI agents could reshape white-collar workflows and operational efficiency on a fundamental level, reducing human oversight requirements in complex systems.
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Read at arXiv cs.LG