Persistent Memory

TL;DR for operators A production voice agent has to decide more than what to say. It has to decide whether a pause means the user finished speaking, whether response pacing should change, which earlier constraints still matter, and how much historical audio is worth sending back to the model. Scalable Context Orchestration for Serving LLMs Over Voice1 treats those decisions as a separate systems layer. Its llmovoice middleware explicitly tracks semantic, speech-delivery, and environmental state; organizes completed interactions into persistent memory structures; and projects only a bounded, relevance-weighted portion of history into the serving model. ...

TL;DR for operators Production AI does not become reliable by remembering everything. It becomes reliable by preserving the information that defines the current state, explicitly representing what is allowed to change, and discarding history that would contaminate the next decision. Two papers arrive at this conclusion from remarkably different directions. One generates future amyloid-PET scans by anchoring the generative process to a patient’s baseline scan. The other builds persistent enterprise agents by retaining specifications, schemas, tools, and output contracts while deleting prior reasoning traces. ...