AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Qualitative findings show how reviewing the work and feedback helps authors develop requirements and guide subsequent delegation and NarrativeSteward supported easier formulation of revision requests and inspection of changes, than general-purpose agents.
DuoOPD is introduced, in which the student's outcome sets the direction of feedback and the joint teacher-student outcome decides how the teacher supports it: when only the teacher succeeds, its verified answer becomes context for scoring the student's failed response.
The Detection-Aware Attribution Framework is proposed, which learns the effects of valid attribute replacements and amortizes this intervention evidence into deployment-time diagnosis, and shows that intervention-grounded attribute attribution can connect failure localization to executable system repair.
State-Grounded Conditioning is introduced, a design principle for user-facing LLM agents that must condition on live user state (game state, session history, live inventory), and a distinct failure class the authors call direction drift: task-complete responses whose chosen direction misaligns with the current state.
Training capable coding agents via reinforcement learning (RL) requires diverse tasks with reliable verifiers. Open-source codebases offer a rich source of such tasks, while existing methods typically rely on development artifacts such as issues and commits, limiting the range of tasks that can be extracted. To better...
This work introduces PersonaForge, a user simulation framework for synthesizing realistic multi-turn user--agent interactions that combines a four-dimensional persona space, SOUL-driven behavioral control calibrated to real-user statistics, and Reverse Deep Construction grounded in authentic seed queries.
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