mlsanityData to 7 Oct 2026

FORESIGHT-9: Prospective and Process-Aware Evaluation of Adaptive Trading Agents

arXiv 2608.29372 ↗First posted 29 Aug 2026cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Retrospective backtests provide a limited test of adaptive trading agents: they cannot rule out historical contamination, expose sensitivity to a single realized market path, or reveal internal degeneration during long-horizon adaptation. We introduce FORESIGHT-9, a prospective and process-aware benchmark built from nine auditable counterfactual stress worldlines branching from a common July 2026 information boundary. Each worldline specifies staged macro-financial events and joint multi-asset anchors; a deterministic generator realizes the trajectories, while observations are disclosed according to in-world time. A common contract standardizes observations and execution while preserving each agent's native adaptation loop. We evaluate two adaptive trading-agent frameworks with two foundation-model backbones across 36 long-horizon runs. Agent rankings vary substantially across worldlines and backbones, and a fixed equal-weight policy outperforms 31 of 36 runs. Process telemetry exposes failures that terminal returns conceal: in one high-return run, the live factor library collapsed while executed holdings converged to the equal-weight fallback, even though decision records continued to report an active factor ensemble. FORESIGHT-9 therefore evaluates not only portfolio outcomes, but whether adaptive agent state and execution remain coherent across alternative futures. We release the worldlines, trajectories, audit traces, and regeneration scripts.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 27 references that are in the map. First posted on arXiv 29 Aug 2026.

Builds on

15 of 27 references.

References · 15

  1. 2023Let's Verify Step by Stepcited by 3,598
  2. 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 1,908
  3. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,280
  4. 2023AgentBench: Evaluating LLMs as Agentscited by 1,211
  5. 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,134
  6. 2022Solving math word problems with process- and outcome-based feedbackcited by 871
  7. 2024AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agentscited by 319
  8. 2016Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learningcited by 285
  9. 2024ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilitiescited by 272
  10. 2023FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Designcited by 163
  11. 2024A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalistcited by 137
  12. 2019Quant GANs: Deep Generation of Financial Time Seriescited by 107
3 more
  1. 2023Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysiscited by 29
  2. 2026FactorMiner: A Self-Evolving Agent with Skills and Experience Memory for Financial Alpha Discoverycited by 13
  3. 2026From Knowing to Doing: A Memory-Controlled Benchmark for LLM Trading Agents on Stock Marketscited by 7

Built on by

Not cited yet.