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University of Sussex, University of Oxford
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The Bellman equation's structure isn't just a mathematical artifact; it emerges from deep interdependencies that can reshape our approach to decision-making in AI.
Evasive steganographic payloads in LLMs can retain high recoverability while eluding detection, but targeted data interventions can restore visibility to these hidden secrets.
Refining state-action abstractions based on learning error can lead to near-optimal performance even with substantial information loss.
