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Self-keyed in-parameter memorization (SKIM), a closed-form method in which the model generates its own questions about each dialogue turn and writes those questions into its parameters as the keys that later retrieve the turn's content, is proposed.
A long-context recall method that maintains near-constant GPU memory usage as context length increases, without additional training is proposed, which enables answering single-fact questions in two-million-token story contexts where previous methods fail.
P2Skill is proposed, a prompt-based skill distillation method in which a local small language model (SLM) autonomously performs decomposition, PII-aware routing, paraphrasing, and reconstruction by following the skill prompts.
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