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Such agents can \\emph{look up} what they have seen but cannot \\emph{learn from} it: their policy is unchanged by experience, and any information dropped from the context is permanently lost. We introduce \\texttt{TMEM}, a self-evolving parametric memory framework in which the agent not only compresses history into explicit memory but also absorbs distilled supervision into fast LoRA weights $\\Delta_t$ via lightweight online updates, genuinely altering its future behavior within a single episode. We formalize this as an agentic decision process with fast-weight rollout dynamics: actions are sampled from $\\pi_{\\theta_0+\\Delta_t}$, while extraction actions produce supervision that updates $\\Delta_t$ for subsequent decisions. 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