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Junior Williams

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#reinforcement learning Open access Sep 2026

Jev at the Agent Authorization Boundary: Evaluating TypeSafe's Decision Model and Its Reported RLCD Approach

TypeSafe describes Jev as a typed decision model trained using Reinforcement Learning for Calibrated Decisions (RLCD). We evaluate its behavior at an agent authorization boundary, where software must allow, hold, or deny a proposed action. A historical audit of 150 proposals motivates two separate paired comparisons: J...

Junior Williams · 0 citations
#reinforcement learning Open access Sep 2026

Jev at the Agent Authorization Boundary: Evaluating TypeSafe's Decision Model and Its Reported RLCD Approach

TypeSafe describes Jev as a typed decision model trained using Reinforcement Learning for Calibrated Decisions (RLCD). We evaluate its behavior at an agent authorization boundary, where software must allow, hold, or deny a proposed action. A historical audit of 150 proposals motivates two separate paired comparisons: J...

Junior Williams · 0 citations

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