AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The results show that group identity shapes how LLMs aggregate information across agents, independently of its correctness, and identify a manipulation surface for multi-agent AI systems.
An operation-based view that evaluates mathematical frameworks by the conceptual operations they support is proposed, identifying thirteen operations (including similarity, composition, generalization, and grounding) that recur across cognition, psychology, and AI.
This taxonomy reveals three broad patterns: inference-time approaches remain comparatively underexplored, related ideas have developed largely in isolation across pipeline stages, and externally grounded methods span the entire pipeline despite often being described under different terminology.
This work explores a self-supervised framework that encourages models to predict concepts, approximated as sets of semantically equivalent tokens, suggesting that concepts enhance semantic alignment while preserving language modeling quality.
It is found that two alerts a single agent judges almost identically on its own can drive collective behavior far apart, so auditing any one member need not reveal what the population will do, and that collective behavior can be predicted in advance.
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