AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work formalizes fork placement as locating the pivots of the chain's value curve, where the expected outcome turns, and proposes belief-shift branching, which read the model's answer belief at candidate boundaries and fork just before the step where consecutive beliefs diverge most.
Memory-augmented speculation yields a 19--39\% relative accuracy improvement on action prediction and up to a $2.5\times$ increase on observation prediction tasks with repetitive action spaces as memory accumulates and generalize across speculator models of varying cost.
This work introduces a controlled testbed for studying how goal-directed persuaders shift elicited stances in networks of LLM agents grounded in real-world ego-network topologies, and argues for evaluating multi-agent persuasion as a trajectory- and exposure-level process.
A comprehensive re-evaluation of two memory-based methods for self-improving agents is conducted, broadening the scope of evaluation along two axes and hypothesizing that task and environment underspecification contribute to this fragility.
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