AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces the cliff token, a token at which the estimated probability of reaching the correct answer (success probability) drops beyond an adaptive threshold, and introduces a cliff taxonomy of deterministic, uncertain, and sampled-off cliffs, defined by greedy choice and token entropy.
ReasoningFlow is introduced, a framework that captures the discourse structures of LRM reasoning traces into fine-grained directed acyclic graphs (DAGs) and reveals diverse fine-grained reasoning behaviors that can be used for better reasoning trace monitorability.
This work proposes a framework that ensembles the reasoning structure, not just the answers, of multiple LLMs by weighted merging of Directed Acyclic Graphs (DAGs) extracted from reasoning chains by weighted merging of Directed Acyclic Graphs (DAGs) extracted from reasoning chains.
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