Hugging Face Trending Papers
FIDES: A Concordance Protocol for LLM-Generated Trading Strategies
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arXiv AI
Aug 25
FIDES: A Concordance Protocol for LLM-Generated Trading Strategies
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 AI
Oct 26
DealsFlow: An Evidence-Gated Agentic Harness for Venture Capital Deal Triage
arXiv:2610. 32380v1 Announce Type: new Abstract: Large language models (LLMs) can read startup pitch decks, but letting them emit investment scores directly invites score inflation, run-to-run variance and rationales that are hard to audit.
Hugging Face Trending Papers
Oct 27
FIDES-Live: Evaluating Behavioral Concordance in Sequential Trading Agents
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...
arXiv AI
Oct 28
FIDES-Live: Evaluating Behavioral Concordance in Sequential Trading Agents
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.
arXiv Machine Learning
Oct 5
MintEval: Do LLMs Implement the Trading Strategy You Asked For? A Behavioural-Equivalence Benchmark for Natural-Language-to-Strategy Code
arXiv:2610.03080v1 Announce Type: cross Abstract: Large language models are moving from producing trading signals to writing the code that executes them. The failure mode of the second role is silent...
arXiv AI
Sep 24
Propose, Don't Judge: An Anytime-Valid Referee for LLM Agents That Mine Investment Factors
arXiv:2609.27051v1 Announce Type: new Abstract: Language-model agents now run the whole of quantitative factor research: they propose investment factors, backtest them, select the survivors and retir...