Examines a novel architecture for predictive reinforcement learning, suggesting enhanced accountability.
P3LA discloses a typed architecture connecting joint-embedding predictive architectures, belief-conditioned partially observable reinforcement learning, large language models, and accountable human action through PPP-governed P-Gates. It includes component role separation, a reference packet contract, gate outcomes, non-promotion invariants, pseudocode, a non-monotonic worked example, evaluation targets, and a related-work boundary.
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Christopher D. Pang (2026) studied this question.
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