Dreams, Reflections, and Inceptions

A Pokémon Red speedrunning agent repeatedly failed in Viridian Forest because it mistook the edge of its screenshot-based viewport for an impassable map boundary, illustrating that detailed execution traces alone do not create usable learning. The project proposes a layered approach built on recorded traces, session-level reflections, and cross-session “dreams,” where dreams are structured, evidence-linked interpretations that identify recurring patterns, preserve uncertainty, and remain traceable to the underlying actions and outcomes. Reflections condense each session into important observations, while dreams compare those observations across runs to identify reusable lessons, such as correcting the agent’s boundary perception or assigning routine navigation to less expensive models. To prevent unsupported advice, every inferred observation should have “receipts” linking it to specific sessions, turns, and tool results. The approach also introduces “inceptions,” which replay prior tasks under varying reasoning levels, models, or tools to test whether a dream-derived lesson improves outcomes efficiently and transfers across settings, turning recorded agent experience into validated workflow improvements.

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