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.
Sequential trading agents produce decisions, orders, execution receipts, account states, and reports that need not agree, even when the final portfolio value is correctly computed. We present FIDES-Li...
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.
arXiv:2609.14811v1 Announce Type: new Abstract: We introduce Crypto Accounting Bench (CAB), a benchmark for assessing whether frontier and open-weight language models can reconstruct the complete jou...
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...
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.
arXiv:2605. 28850v2 Announce Type: replace Abstract: We study behavioral alignment and representation dynamics of large language model (LLM) agents in financial decision environments.
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.
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.
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: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.
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...