arXiv:2605.31064v1 Announce Type: cross Abstract: Large Language Models (LLMs) have significantly advanced online data services, particularly in the domain of financial question answering (FinQA). However, such systems remain susceptible to numerical reasoning hallucinations, which critically undermine reliability in high-stakes financial applications. Although retrieval-augmented generation (RAG) has been widely adopted to ground responses in external knowledge, it introduces three persistent challenges: noise sensitivity, calculation fragility, and an auditability crisis. Existing model-cent
The proliferation of LLMs in high-stakes applications like finance is exposing critical reliability issues around numerical reasoning and hallucinations, necessitating immediate technical solutions.
Reliable and auditable AI systems are critical for trust and adoption in regulated industries, and addressing numerical hallucinations directly impacts their utility and safety.
The focus for improving AI in critical sectors shifts towards data-centric compilation and better auditing mechanisms, rather than solely model-centric improvements.
- · AI safety researchers
- · Financial institutions adopting AI
- · Data-centric AI platforms
- · Untrustworthy AI models
- · Companies relying solely on RAG without further safeguards
Increased trust and adoption of AI in financial services due to improved numerical accuracy and auditability.
Development of specialized hardware or software architectures optimized for verifiable numerical reasoning in AI.
New regulatory frameworks specifically addressing numerical integrity and audit trails for AI in financial and other high-stakes domains.
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Read at arXiv cs.AI