arXiv:2606. 21875v2 Announce Type: replace-cross Abstract: Modern data analysis usually gives a prediction without showing whether the evidence behind it is clear, conflicting, or stable.
By Jeffery Opoku, David Banahene
arXiv:2606. 03031v1 Announce Type: new Abstract: Structured financial audit verification is difficult for language-model agents because correctness depends on structured evidence rather than text alone.
By Yan Wang, Xuguang Ai, Jaisal Patel, Xueqing Peng, Fengran Mo, Yupeng Cao, Haohang Li, Mingyu Cao, Lingfei Qian, V\'ictor Guti\'errez-Basulto
arXiv:2609.15319v1 Announce Type: cross
Abstract: Frontier models score well on shallow document/chart reading tasks. In a controlled data-room audit, moving evidence into buried conditions reduced a...
By Luis M. S\'anchez
arXiv:2609.05905v1 Announce Type: cross
Abstract: LLM agents are increasingly used for live forecasting, where they retrieve up-to-date information and produce estimates for unresolved future events....
By Yuanpu Cao, Yongkang Du, Yurui Chang, Lu Lin, Jinghui Chen
arXiv:2608. 13706v1 Announce Type: cross Abstract: Existing defenses against hallucination in retrieval-augmented and multi-agent pipelines remain partial: evidence is trusted despite modality disagreement, debate verifies an aggregate report rather than individual claims, and such verification occurs only after drafting, leaving inter-agent errors undetected until the final text.
By Fatema Tuj Johora Faria, Mukaffi Bin Moin, Jubayer Al Mahmud, M. F. Mridha, Md. Alam Hossain
arXiv:2606. 19501v1 Announce Type: new Abstract: Decentralized finance exposes supervisors to fast-moving, networked credit risks.
By Aijie Shu, Bowei Chen, Wenbin Wu, Cathy Yi-Hsuan Chen, Fengxiang He
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.
By Changlun Li, Peixian Ma, Qiqi Duan, Zhenyu Lin, Peineng Wu
Large language models (LLMs) can synthesize financial narratives but may express high confidence when evidence is sparse, stale, or contradictory. This failure is especially consequential in forecasting, where filings, news, prices, volume, and technical signals can disagree.
arXiv:2607. 17797v1 Announce Type: new Abstract: Financial statements (FS) such as Balance Sheet (BS), Income Statement (IS) and Cash-flow Statement (CS) summarize the annual financial performance of a company.
By Kshitij Madhav Jadhav, Sushodhan Vaishampayan, Manoj Apte, Sachin Pawar, Nitin Ramrakhiyani, Girish Keshav Palshikar
arXiv:2606. 19887v1 Announce Type: cross Abstract: Existing safety benchmarks target general adversarial scenarios but miss finance-specific risks.
By Chaeyun Kim, Daeyoung Park, Junghwan Kim, Jinyoung Jeong, Eunji Song, Yongtaek Lim, Minwoo Kim
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
The paper proposes an independence‑graded audit protocol for agentic AI systems, arguing that independence should be evaluated along three orthogonal axes: principal independence, substrate independence, and evidence independence. It introduces a seven‑step protocol based on the beta‑factor model from reliability engineering, demonstrates its application through a structural detectability analysis and a Monte Carlo study, and maps the framework to relevant regulatory standards such as the EU AI Act, ISO/IEC 42006, and UK public‑sector guidance.
By Mohamed Chahine Ghanem