AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Large language model (LLM) agents repeatedly load reusable content, such as skills, documents, and memory entries, into the current context. Re-encoding this content for every request wastes computation. Position-independent caching (PIC) alleviates this by encoding each artifact independently and reusing its key-value...
LGM is presented, a novel neuro-symbolic framework that shifts long-term memory disentanglement into a continuous latent space and significantly outperforms state-of-the-art baselines in capturing both explicit and implicit preferences while enabling personalized responses.
InteraCtive Memory Learning is proposed, a multi-agent framework that transforms the memory mechanism from a passive archive into a learnable, interactive memory policy that significantly outperforms strong baselines.
ThinkFlow bypasses the text bottleneck by dynamically compressing conversational flows into probabilistic latent memory skills, autonomously consolidating complex user states into disentangled, continuous vectors without semantic interference.
GRAIN models reasoning as a semantic parsing and tool-execution pipeline, guided by a Structure Invariance Reward, which forces the LLM to learn robust text-to-structure mappings rather than memorizing linguistic artifacts.
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