AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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PROVISO is presented, a controller that decides which action to commit after the agent proposes an action and before it is executed, and commits the action with the highest calibrated value learned from outcomes.
Clinical decisions depend on anticipating how a patient's condition will evolve as treatment is adjusted and new evidence becomes available. Existing medical world models capture patient-state evolution, but often define the evolving state around a particular modality or task, limiting reuse as inputs or prediction tar...
XKV is introduced, which lifts all three restrictions on multi-agent LLM answering: learned-query attention pools both caches; self-attention over receiver-aligned layer tokens, with a learned layer map reconciling different depths, mixes the pooled summaries into a compact joint memory.
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