FIDES-Live extends the FIDES framework to audit sequential trading agents by separating decision validity, numerical commitment adherence, execution and ledger reconciliation, and receipt‑grounded reporting. In a retrospective audit of 7,299 cryptocurrency account‑rounds and a 310‑round equity pilot, decision validity reached 97.35% and structured reporting accuracy 90.81%, while commitment adherence overall was 94.36% but dropped to 62.63% for non‑empty order proposals. The study shows that all cash and position transitions reconcile with recorded fills, and highlights the need to report concordance, evidence coverage, activity, and financial outcomes separately.
By Arther Tian, Alex Ding, Simon Wu, Aaron Chan
AStockArena is a real‑time decision‑auditing benchmark for continuous trading in China’s A‑share market, evaluating 13 open‑ and closed‑source models over 3,198 sequential decisions. The benchmark reconstructs actual account actions by aligning reports, trade requests, execution responses, and account states, then audits them with eight checks covering execution traceability, evidence sufficiency, and financial meta‑awareness. Across 1,070 trading points, 72.99% show at least one audit gap, with evidence gaps most common, and historical replay demonstrates that flagged trades significantly affect portfolio outcomes.
By Zeyu Liu, Sing Kwong Lai, Xuanzhi Feng, HaoYang Shang, Yang Li, Zihao Fang, Xuan Liu
arXiv:2606. 08285v1 Announce Type: new Abstract: Large language models (LLMs) and agentic systems are increasingly proposed for financial trading, yet their reported performance remains difficult to compare because studies vary in data provenance, temporal split discipline, execution timing, turnover treatment, and transaction-cost modeling.
By Junyi Yao, Zihao Zheng
An LLM asked for a trading strategy returns three artifacts at once: a natural-language rationale, an executable implementation, and once run, a track record. Whether these are the same object is rare...
arXiv:2608.23308v1 Announce Type: cross
Abstract: An LLM asked for a trading strategy returns three artifacts at once: a natural-language rationale, an executable implementation, and once run, a trac...
By Arther Tian, Alex Ding, Simon Wu, Aaron Chan
The paper examines whether four AI trading agents adhered to their predefined strategies and risk rules while trading across various market conditions. Analyzing 2,627 closed positions from February to August 2026, the study found high rule adherence, with agents keeping 97.5% of gross losses within declared stops and maintaining stable risk levels even during a 29% market drawdown. The authors provide an evaluation toolkit that allows traders to distinguish rule-following behavior from luck before allocating capital to such agents.
By Saad Naja, Nasser Odtallah, Jake Van-Praag, Sarah May
arXiv:2605. 28850v2 Announce Type: replace Abstract: We study behavioral alignment and representation dynamics of large language model (LLM) agents in financial decision environments.
By Weicheng Xue
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.
By Changlun Li, Peixian Ma, Qiqi Duan, Zhenyu Lin, Peineng Wu
arXiv:2607. 19453v1 Announce Type: cross Abstract: We audit whether candle-based machine-learning models can turn predictions of cryptocurrency extrema or short-horizon outcomes into positive Binance Spot paper policies after assumed costs.
By Ayoub Jadouli
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...
By Raffi Khatchadourian
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...
By Xiangxin Luo, Chengtian Hong, Haohua Li, Yongyi Xie
arXiv:2608. 13706v1 Announce Type: cross Abstract: Existing defenses against hallucination in retrieval-augmented and multi-agent pipelines remain partial: evidence is trusted despite modality disagreement, debate verifies an aggregate report rather than individual claims, and such verification occurs only after drafting, leaving inter-agent errors undetected until the final text.
By Fatema Tuj Johora Faria, Mukaffi Bin Moin, Jubayer Al Mahmud, M. F. Mridha, Md. Alam Hossain