The paper evaluates an agentic harness that combines a 27B language model, financial calculations, narrative templates, and validation checks to produce corporate and investment banking presentation decks. Using a panel of five judges, the system consistently scores higher than a baseline model that generates directly from a short prompt, with scores ranging from 20.4 to 33.6 out of 95. However, judge variability and changes in grading criteria make it challenging to discern small improvements in deck quality.
By Ludovic Gibert, Matis Despujols, Andre-Louis Rochet
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.
By Ming Ze Tang, David Cutting, Boyd Carson, Vasiliki Carson
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
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:2607. 24889v1 Announce Type: cross Abstract: Financial models combine public disclosures with analyst assumptions to produce forecasts and valuations.
By Jiacheng Lu, Sinuo Wang, Wentao Zhao, Rui Sun, Cheng Hua, Tao Song, Hui Cai, Beidi Luan, Zhengze Wu, Lingjing Teng, Yijia He, Jing Li, Daxin Jiang, Zuo Bai, Haibing Guan
VERITYGATE is a four‑gate framework that checks whether LLM‑generated narrations adhere to a fixed schema of declared evidence IDs, entities, numbers, and claim types, rather than verifying every fact in the prose. In experiments with GPT‑4o‑mini, Llama‑3.3‑70B, and Claude Sonnet 4.6 on 900 grounded‑ungrounded pairs, the framework identified high failure rates (up to 80.3% for mini claims) and demonstrated that a single repair pass can improve claim survival rates. The authors also provide code, data, and preliminary human studies to validate the rules and highlight gaps between schema compliance and correct prose.
"whyItMatters":"The framework offers a systematic way to evaluate and improve the faithfulness of LLM explanations to structured evidence, revealing significant failure rates and the impact of repair strategies."
By Sachin Gupta
The paper investigates how sentence‑specificity scores can guide the selection of revisions in collaborative technical documentation. It compares two predictors—SpeciTeller and a target‑adapted model by Ko et al.—across Wikipedia and three technical corpora, finding that the predictors rank sentences differently and that SpeciTeller can improve direction‑valid selection rates in certain datasets. The study also shows that filtering and token‑length adjustments alter but do not reconcile these ranking differences.
By Rocker D'Antonio, Thomas Benton Townsend, Dimitrios Michael Manias
arXiv:2608. 06108v1 Announce Type: new Abstract: Investment competence is inherently personalized: the same market evidence can justify different actions for investors with different goals, horizons, portfolios, and risk boundaries.
By Yuanhong Jiang, Jingjie Zou, Zhenghong Lin, Xusheng Yu, Qiqi Huang, Shuai Jia, Shijie Dai
arXiv:2607. 27189v2 Announce Type: cross Abstract: We introduce APEX-Accounting, a benchmark built by Mercor in partnership with Ramp, to assess whether frontier models can do the real work of accountants.
By Julien Benchek, Austin Bennett, Jasmin Kern, Ryan Stevens, Rene Sultan, Charis Ching, Hayley Popiel, Vaibhav Mittal, Felix Mercier, Brendan Foody, Bertie Vidgen
arXiv:2608. 11683v1 Announce Type: new Abstract: AI agents are increasingly deployed for professional investment research, yet no benchmark captures the complexity of the full investor workflow.
By Yuhao Zhang, O. Ozan Koyluoglu, Thejas Venkatesh, Richard Diehl Martinez, Vishank Bhatia, Arash Alidoust, Ashwin Paranjape
The study examines a two‑agent résumé screening process where both employer‑side and candidate‑side agents exchange evidence before deciding who advances, contrasting it with the traditional one‑call automated screening. Using GPT‑5.5 and Claude Opus 4.7 on 600 constructed résumé‑job pairs, the two‑agent method increased the proportion of applications advanced (up to 39.3% for GPT‑5.5) and raised pass rates for borderline cases from 4.5% to 26.2% (GPT‑5.5) and 6.5% to 16.1% (Opus 4.7). The results show that the screening procedure itself, rather than just the underlying model, determines which candidates reach human review and how consistently that access recurs.
By Jian Gao, Hang Jiang
arXiv:2608. 14639v1 Announce Type: cross Abstract: Per-field accept/review with selective risk at most alpha -- accept a field only if the error rate among accepted fields is controlled -- is the trust contract document-extraction systems need, and the natural procedure silently violates it on real documents.
By Bhaskar Gurram