{"paper":{"title":"Cost and Accuracy of Long-Term Memory in Distributed Multi-Agent Systems Based on Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Benedict Wolff, Jacopo Bennati","submitted_at":"2026-01-12T20:20:35Z","abstract_excerpt":"Long-term memory (LTM) is fundamental to large language model (LLM)-based agents in the emerging Internet of Agents (IoA), where distributed multi-agent systems (DMAS) span cloud and edge networks. Existing evaluations are typically published by framework providers and focus on token usage and latency, rarely accounting for system-level cost or deployment in DMAS. These gaps are addressed with an independent reproducible testbed that evaluates accuracy, latency, CPU time, peak RAM, disk I/O and network usage in a simulated cloud-edge environment. Three venture capital-funded frameworks spannin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.07978","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2601.07978/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}