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arXiv:2609.36739v1 Announce Type: cross Abstract: Multi-agent LLM systems are increasingly structured like organizations, with roles, critics and shared memory, yet they are evaluated on tasks that l...
FM‑Bench is a new benchmark that tests large language model agents on long‑horizon decision‑making by having them run a football club for 20 in‑game years. The agent must manage a squad, trade players, negotiate contracts, invest in facilities, set lineups, and respond to a board that can fire it, all while a deterministic engine aggregates the outcomes into a final score without human judgment. The benchmark evaluates six behavioral capabilities and compares 15 frontier models in solo and arena tracks, revealing that managerial behavior—not computational scale—drives performance.
FM‑Bench is a new benchmark that tests large language model agents on long‑horizon decision making by having them run a football club for 20 in‑game years. The agent must manage a squad, trade players, negotiate contracts, invest in facilities and youth, set lineups, and respond to a board that can fire it, all using 26 tools and roughly 340–400 decision stops, with a deterministic engine producing a final score without human or LLM judges. The benchmark includes a solo track where each of 15 frontier models competes against a frozen scripted world, and an Arena track where the same models plus a scripted anchor share one 20‑year world, allowing the first head‑to‑head evaluation at this scale. whyItMatters":"FM‑Bench provides a rigorous, large‑scale test of sustained, cumulative decision‑making in language‑model agents, revealing that managerial strategy—not computational scale or vendor—drives performance over long horizons."
arXiv:2607. 02255v1 Announce Type: new Abstract: Memory for a long-horizon LLM agent is a contract about what each future decision is allowed to see.
Agent Zero Memory is a provenance‑aware long‑term memory system for large language model agents that distills user interactions into three parallel memory structures: an episodic timeline, an associative entity‑event knowledge graph, and a semantic, citation‑locked hierarchical documentary memory. Retrieval is performed via an intent gate, source router, and concurrent searches across the three systems, producing integrated, cited answers that exclude fabrication and require evidence the reader has opened. The system achieves state‑of‑the‑art performance on LongMemEval (95.60%) and LoCoMo (93.60%) while offering a favorable accuracy‑cost‑latency trade‑off across multiple backbone LLMs.
arXiv:2606. 29771v1 Announce Type: new Abstract: LLM agents are increasingly cast as autonomous portfolio managers, and benchmarks have moved from financial question-answering to sequential trading.