AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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It is argued that effective knowledge editing must account for the intricate nature of knowledge representation, and three promising research directions are proposed that respect the complexity of knowledge representation in a real-world setting.
To make CBM measurable, BeliefTrack is introduced, a closed-world benchmark spanning Rule Discovery and Circuit Diagnosis, where a finite belief space and symbolic verifiers enable exact turn-level evaluation.
This work identifies memory-induced cognitive traps: even faithfully recorded and semantically relevant memories can distort model reasoning or beliefs and degrade current task performance, and proposes AdaptiveMem, a simple yet effective inference-time method that instructs LLMs to avoid memory traps.
LongDS is introduced, a benchmark for long-horizon, multi-turn data analysis where agents must maintain, update, restore, and compose evolving analytical states, suggesting that the key bottleneck is maintaining a correct analytical state rather than increasing interaction budget.
Mechanist is an agentic system that uses AI as a scientific instrument for the autonomous discovery of mechanisms underlying AI intelligence, and develops a mechanism theory of belief, revealing how models represent world knowledge, form beliefs, infer the beliefs of others, and how these mechanisms emerge during pretraining.
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