AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Large language models equipped with development environments have moved code generation toward repository-scale construction, yet building complete repositories remains difficult because interacting modules, interfaces, configurations, tests, and dependencies must work together. We introduce Code Primitives, agent-nati...
Large language models (LLMs) are increasingly used as interactive agents and simulators, yet it remains unclear whether they can recover latent sequential structure beyond surface action frequencies. This distinction is critical for behavioral simulation, where actions are often shaped by prior context rather than marg...
Agent harnesses govern how large language models (LLMs) gather context, invoke tools, verify results, preserve state, and terminate, largely affecting agent performance. However, the value of each harness mechanism can differ across heterogeneous tasks: a mechanism that improves one task may impose overhead or context...
ANTMAN is introduced, an adaptive coordination framework that treats evolving unresolved information needs as the unit of runtime coordination and maintains a revisable Need Graph that tracks unresolved requirements, accumulated evidence, prior attempts, and search progress.
A common rank-displacement audit at three scopes is introduced at three scopes: facts supported by the edited parameter, other correct answers to the target query, and correct answers across queries with the same relation.
MANTA, a framework for Multi-Agent Network Topology Adaptation that enables communication structures to self-evolve at inference time, is introduced and shows that inference-time self-improvement can extend to the architecture of collaboration itself.
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