LLM-Based Trading Research Exposes Reproducibility Gaps

The arXiv paper titled "Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems" was submitted on June 6, 2026 by Junyi Yao and Zihao Zheng, per the arXiv record. The paper presents a reproducibility audit using a coded evidence matrix covering 30 trade-relevant primary studies, the authors report. The abstract states that the audit assesses point-in-time controls, split transparency, held-out evaluation, cost and turnover treatment, execution semantics, universe definition, and artifact release. The paper also supplies a 10-equity worked example that the authors use to illustrate how explicit friction and timing choices affect reported strategy performance.

Studies of algorithmic trading that reuse generic LLM components often depend critically on evaluation assumptions rather than model architecture alone, an industry-pattern observation. For practitioners, differences in temporal splits, transaction-cost modeling, and execution timing typically change backtest realism and can introduce look-ahead or overfitting biases unless strictly controlled. Reproducibility audits that codify those execution assumptions help expose where performance claims are sensitive to implementation details rather than model capabilities.

Industry context: The paper places agentic and LLM-driven trading claims into the broader reproducibility debate in computational finance by foregrounding execution realism. For ML researchers and quant practitioners, clearer shared conventions for temporal splitting, turnover accounting, and artifact release improve comparability and reduce the likelihood of spurious conclusions when models interact with market frictions.

Observers should look for follow-up work that proposes standardized reporting checklists or benchmark protocols for execution realism, and for any public artifact releases from audited studies that enable independent re-runs.