AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This paper proposes MSCE, a training-free Memory--Skill Co-Evolution framework that organizes agent experience into grounded step traces, reusable procedural policies, and declarative environmental cognition, and introduces reflection-weighted value backfilling.
To measure evolved-state generation, LongEvoRoleBench is introduced, which pairs four long-dialogue corpora for cross-episode evolution with four short-dialogue corpora as within-scene state-tracking checks, under a unified next-utterance protocol.
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