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An idealized reference model---an injection-invariant, utility-preserving oracle---can nevertheless be approximated, as its output distribution in training is interestingly accessible, which motivates a theoretically principled SecPO loss.
MindMemOS is presented, a portable and self-evolving memory operating layer that organizes open-world information using a unified entity property timestructure that supports scenario-adaptive memory modeling, higher-order pattern discovery, autonomous memory refinement, and continuous skill evolution.
This paper proposes Secure On-Policy Distillation (SecOPD) that provides token-level feedback to guide defensive fine-tuning, and generalizes to domains completely unseen in training.
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