LabourCrew is a multi‑agent Retrieval‑Augmented Generation (RAG) framework designed for trustworthy statutory question answering in labour law. It introduces three grounding mechanisms: StatuteGraph, an evidence‑exchange ledger, and a calibrated trust gate that controls false‑accept rates. Evaluated on a Bangla Labour Act QA set, LabourCrew achieves a false‑accept rate of 0.081 and higher answer relevancy than existing RAG methods, demonstrating that calibrated abstention is key to auditable legal QA.
By Fatema Tuj Johora Faria, Mukaffi Bin Moin, Jubayer Al Mahmud, M. F. Mridha, Md. Alam Hossain
arXiv:2609.10293v1 Announce Type: new
Abstract: In high-stakes domains such as legal practice, a language-model answer is only useful to the extent that a reader can verify each claim against the sou...
By Chen Qian, Yimeng Wang, Yu Chen, Lingfei Wu, Andreas Stathopoulos
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
The paper introduces Self-Improving Retrieval-Augmented Generation (RAG), a framework that splits document question answering into Retrieval, Reasoning, and Judge agents coordinated by an orchestrator. When the Judge scores an answer below a dynamic threshold, the system retries with broader retrieval, more careful prompting, and relaxed acceptance criteria, achieving 86% oracle-guided accuracy on FinanceBench with a 36.4% Lazarus Rate. The approach logs every decision with confidence scores, providing audit trails needed for regulated financial applications.
By Junjie Xiong, Shawheen Ghezavat, Aum Hirpara
arXiv:2607. 12650v1 Announce Type: cross Abstract: Tool access alone does not make LLM empirical reasoning governable: accepted outputs need not descend from attested evidence, and accepted deductions need not hold up under formal scrutiny.
By Junyu Ren
arXiv:2606. 11537v1 Announce Type: new Abstract: Financial and tabular question answering requires more than fluent reasoning: answers must be grounded in the exact facts, formulas, units, signs, and scales that support them.
By Abdelrahman Abdallah, AbdelRahim A. Elmadany, Sameh Al Natour, Hasan Cavusoglu, Adam Jatowt, Muhammad Abdul-Mageed
arXiv:2608.22483v1 Announce Type: new
Abstract: Large Language Models (LLMs) increasingly support decision-making in high-stakes domains, but they often hallucinate and express confidence that is mis...
By Toghrul Abbasli, Kentaroh Toyoda, Yuan Wang, Li Chen
The paper proposes a claim‑specific verification audit for modular agents that replaces aggregate task scores with evidence‑based evaluations. Each agent conclusion is recorded with supporting evidence and classified as supported, unsupported, unresolved, or not evaluated, along with the boundary of validity. The audit employs three tools—oracle policies, perfect component replacements, and verifier‑score tests—to trace value changes, locate lost value, and assess verifier effectiveness, demonstrated on a portfolio‑allocation agent in a synthetic market.
By Ali Atiah Alzahrani
arXiv:2607. 01223v1 Announce Type: new Abstract: When should an AI system's answer be trusted?
By Ben Slivinski, Michael Saldivar
arXiv:2509. 00761v4 Announce Type: replace Abstract: Large language models are increasingly deployed for legal question answering, where evaluations typically focus on multiple-choice accuracy.
By Boqin Yuan, Ziqi Wang
arXiv:2607. 26512v1 Announce Type: new Abstract: AI agents can draft claims faster than authors can check whether the cited or retrieved evidence supports them.
By Gengyu Chen, Yongjie Yu, Weiling Wang
arXiv:2606. 19782v1 Announce Type: new Abstract: Financial chart question answering in regulated settings demands more than accuracy: practitioners must know which answers to trust before acting on them, and many institutions cannot send client data to external model providers.
By Aravind Narayanan, Shaina Raza