AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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UniOPSD (Unified On-Policy Self-Distillation) is introduced, which unifies these feedback sources through adaptive local credit arbitration and constructs comparable credit estimates from environmental returns and successful-peer hindsight at shared interaction anchors.
These results support accounting for selection history when constructing and evaluating search-agent rollouts and rank first on seven QA benchmarks using Qwen3-4B, Qwen3-8B, and Qwen2.5-7B.
The results show that evolving skill memory is not merely an inference-time plug-in: it changes policy learning and the future training distribution while retaining value as optional external memory.
CoEvoKG is introduced, a framework that turns a knowledge graph into both a source of verifiable training tasks and a persistent evidence memory for agent evolution, closing the loop between model self evolution and knowledge accumulation.
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