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.
arXiv:2609.08248v1 Announce Type: new Abstract: Modern industrial ads ranking stacks are increasingly bottlenecked not by model capacity or training compute, but by the throughput of human ML iterati...
arXiv:2601.15322v3 Announce Type: replace-cross Abstract: Tool-using agents can repeat a final decision while changing their recorded execution. We introduce the Determinism-Faithfulness Assurance Ha...
arXiv:2608. 06108v1 Announce Type: new Abstract: Investment competence is inherently personalized: the same market evidence can justify different actions for investors with different goals, horizons, portfolios, and risk boundaries.
arXiv:2608.29372v1 Announce Type: new Abstract: Retrospective backtests provide a limited test of adaptive trading agents: they cannot rule out historical contamination, expose sensitivity to a singl...
arXiv:2605. 27887v2 Announce Type: replace Abstract: Large language models (LLMs) have shown strong performance across diverse financial tasks, yet portfolio management (PM), a critical financial decision-making task, remains poorly benchmarked.
arXiv:2606. 17459v1 Announce Type: new Abstract: Evaluating the decision-making capabilities of large language models (LLMs) is a growing research priority, yet existing benchmarks focus on isolated cognitive tasks such as reasoning, knowledge retrieval, and economic rationality in stylized settings.
arXiv:2607. 23124v1 Announce Type: new Abstract: Large language model agents have advanced rapidly, yet progress remains fragmented across domains, capabilities, task difficulty, and interaction settings.
arXiv:2607. 10286v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly used in trading systems, where model reasoning, tool use, and continual decisions incur costs that are expected to produce trading value.
arXiv:2607. 11141v1 Announce Type: new Abstract: Large language models (LLMs) based agents are beginning to participate in portfolio construction and market analysis, where decisions must be justified under evolving information and risk constraints.
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."
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.
arXiv:2605. 22664v2 Announce Type: replace Abstract: LLM agents are increasingly expected to carry out end-to-end workflows, producing complete artifacts from high-level user instructions.