FM-Bench Unveils Benchmark for Long-Horizon Management with Competing Agents
agents benchmarks
| Source: HF Papers | Original article
Researchers introduce FM‑Bench, a new benchmark that evaluates language‑model agents' ability to manage long‑horizon tasks with competing agents in a football‑management simulation.
A new benchmark called FM‑Bench (Football Management Benchmark) has been released to test large‑language‑model (LLM) agents on ultra‑long‑horizon tasks. The environment asks an LLM‑driven agent to run a virtual football club for 20 in‑game years, navigating roughly 340‑400 decision points and accessing a suite of 26 tools. At each stop the agent may invoke any number of tool calls, shaping transfers, tactics, finances and other club operations. The first public results cover 15 frontier models, each evaluated on the same solo benchmark and scored automatically by the provided run‑benchmark script.
The launch matters because most existing evaluations focus on bounded, single‑step problems where success is easy to verify. FM‑Bench shifts the focus to sustained strategic planning, where actions accumulate and the simulated environment reacts to every choice. By quantifying how well agents manage cumulative consequences, the benchmark fills a gap in measuring “partial‑credit” performance and long‑term reasoning—capabilities that are critical for real‑world deployments such as autonomous trading, project management or complex game AI.
Researchers will now watch how the community adopts FM‑Bench, whether new architectures or prompting techniques can close the performance gap revealed by the initial model sweep, and how the benchmark evolves to include competing agents or multi‑team scenarios. Follow‑up work may also integrate FM‑Bench with other long‑horizon evaluations, offering a richer picture of agent intelligence as the field moves beyond short‑term task completion toward truly strategic AI assistants.
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