AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The Trinity is introduced, a recursive self-improvement framework coupling world generation with Harness evolution through three roles: Creator, Explorer, and Evolver, which introduces Capability-Driven Real2Sim2Real: for heterogeneous tasks at the same node, Creator abstracts their common requirements into simplified,...
Recursive self-improvement (RSI) describes a system that improves not only its behavior but also the process by which it improves. Recent digital agents test this idea by rewriting their own code and checking each version with executable tests. Bringing these mechanisms into the physical world requires learning from pa...
Tool-using agents make consequential changes to external state, yet correct outcomes do not guarantee that their actions were supported by evidence established beforehand. We study where this evidence-to-action chain breaks as agents move from deciding whether to act to executing single actions and dependent workflows....
This study proposes local Platt-scaling applied separately to three different fine-grained confidence scores, finding that fine-grained confidence scores consistently achieve lower calibration error across a broader range of probability intervals, and this effect is further amplified when global Platt-scaling is applie...
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