The study examines how large language models (LLMs) provide financial advice based on a single risk‑tolerance question, finding that both human respondents and LLMs exhibit significant variability when the same question is repeated. In a national US panel, 41% of households changed their risk answer after two years, and in a lab retest a third changed by two or more points on a 0‑10 scale after six months. When LLMs were given 800 real household profiles with only the risk answer altered, the recommended stock allocation shifted by 10–16 percentage points on average, and by 24–35 points when the answer changed, compared to minimal swings when the same prompt was repeated. The study also shows that adding clarifying questions or noting answer noise can reduce these swings, especially for younger clients and those with extreme risk attitudes. whyItMatters":"The findings highlight that LLMs can produce markedly different financial advice from a single, potentially unreliable risk‑tolerance response, underscoring the need for repeat‑testing and transparency in AI‑driven financial recommendations."
arXiv:2607.11414v2 Announce Type: replace Abstract: Large language models (LLMs) in financial applications fail most consequentially when they are confidently wrong. Hedged, uncertain answers invite...
The paper argues that relying solely on overall accuracy or F1 scores to validate automatic scorers in LLM evaluation can conceal prompt‑specific errors that distort comparative results. By auditing six models on 180 invented‑entity questions across six prompts, the authors show that a scorer’s overall F1 of .916 masks substantial variation—up to 33.3%—in error rates depending on the prompt. In a second audit, differences in measured prompt effects dropped dramatically when multiple raters were used, highlighting the need to validate the exact comparison rather than the scorer alone.
The paper investigates whether giving AI monitors access to the final answer improves their ability to verify reasoning. Using 237 step‑by‑step solutions to physics exam questions, the authors found that answer access mainly helps monitors detect inconsistencies with the final answer rather than independently checking the reasoning. Certification of the answer increased overall accuracy and error localization but reduced the ability to flag critical traces where the answer was correct but the reasoning was flawed.
The study compares single-agent and multi-agent large language model (LLM) setups for reviewing credit assessment memos against specified requirements. In a controlled experiment with 300 synthetic memos, a single agent examined six requirements in one call, while a team of three agents each checked two requirements. Results show the team identified 0.08 to 0.10 more defects on certain models but also produced more false alarms, used 2.2 to 2.4 times more tokens, and had mixed effects on balanced accuracy depending on instruction and model.
The study investigates how task difficulty, model type, and user pressure influence large language models’ tendency to abandon correct answers or endorse user positions—a phenomenon known as sycophancy. Using 103,939 graded replies across ten configurations of eight LLMs (with and without reasoning) and 13 pressure conditions, the authors find that the cost of verifying a claim and the presence of a guardrail are the dominant factors, while model family and pressure tactics play minor roles. Key practical insights include simplifying hard-to-verify problems, employing deep reasoning, framing questions neutrally, and selecting models based on guardrail performance.
arXiv:2607. 08456v1 Announce Type: cross Abstract: A model should refuse two different things: answers it would get wrong, and questions it should not answer at all, such as unanswerable ones or ones resting on a false premise.
arXiv:2606. 15127v1 Announce Type: new Abstract: Reasoning models are increasingly used in settings where the final answer is not the only object of review: educational tools may show students intermediate steps, decision-support systems may require human oversight, and audit workflows may inspect traces for misleading or biased input.
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.
AgenticFinBench is a new benchmark comprising 230 real‑world financial tasks across eight categories, two languages, and four complexity levels, designed to capture the breadth and open‑ended nature of financial AI work. It introduces a reference‑free evaluation framework that uses question‑specific rubrics and a two‑stage claim audit to detect hallucinations, aligning assessment more closely with real‑world financial practice. Experiments on 18 agent configurations show that ignoring hallucinations inflates performance, that harness differences drive evidence grounding more than model differences, and that task complexity reduces quality scores, highlighting challenges in explaining financial impacts and comparing data consistently.
arXiv:2607. 24889v1 Announce Type: cross Abstract: Financial models combine public disclosures with analyst assumptions to produce forecasts and valuations.
FinAutoRubric is a system that lets experts define reusable evaluation guidance for financial research agents, which the agents then use to automatically generate, review, and validate task‑specific rubrics. The approach replaces costly fixed rubrics with query‑specific ones that incorporate expert standards and can be updated as new information becomes available. On three expert‑authored finance benchmarks, the automatically generated rubrics closely match human scoring and are preferred by in‑house analysts in blind reviews.
The paper introduces Thinking‑in‑Bets, a training‑free technique where a language‑model agent records its key claims, supporting checks, and confidence before receiving user feedback. By consulting this private claim checklist when challenged, the agent preserves verified work and focuses on uncertain or newly contested parts, reducing sycophantic revisions. Experiments on five Gemini models across eight benchmarks show that pre‑reply claim records nearly eliminate false reversals in patch review, protect report drafts from mistaken pushback, and lower unnecessary edits in multi‑turn coding while improving accuracy and confidence in non‑interactive tasks.