AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Research for interactive LLMs from retaining more context toward preserving uncertainty is redirected from retaining more context toward preserving uncertainty, to motivate uncertainty-preserving state management.
ReAdapt is proposed, which augments the ReAct loop with an explicit structured social state z = (G, B, R, N, D) capturing goal, belief, relationship, norm, and disclosure, and which helps LLM agents turn retrieved social evidence into revised decisions.
This study contributes to psychological ownership theory by extending it to human–AI interaction and proposing that, in this setting, ownership depends on cognitive and authorial presence rather than solely on task completion.
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