AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Cross-domain experiments show that the learned scorer transfers across benchmarks, indicating that the method captures reusable joingraph traversal behavior.
Stashbird is presented, an agent memory system that links source episodes to derived memory state through explicit provenance and achieves higher accuracy than Hindsight on LongMemEval-S and GroupMemBench and comparable accuracy on EverMemBench.
This work suggests sharing typed federated artifacts, schema-validated objects with well-defined fields for per-field privacy, dispute resolution, and cross-model transfer, and instantiating them as SYNAPSE1, a common tool-routing knowledge.
CAST, a critique-aware training framework that converts sparse task outcomes into action-level supervision for critique learning and policy optimization, is presented, demonstrating that critique-aware training improves the robustness of LLM agents in realistic dynamic environments.
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