FinRCA-Bench: Benchmarking Evidence Retrieval and Reasoning for Financial AI Sys

arXiv:2603.22305v1 Announce Type: cross Abstract: Large Language Models (LLMs) are rapidly transitioning from static Natural Language Processing (NLP) tasks including sentiment

arXiv:2603.19715v1 Announce Type: new Abstract: Formal verification via interactive theorem proving is increasingly used to ensure the correctness of critical systems, yet

arXiv:2610.27256v1 Announce Type: new Abstract: Analyzing merchant transaction histories requires dynamic temporal filtering, entity resolution, and aggregation over irregular

arXiv:2605.12673v1 Announce Type: new Abstract: Agent benchmarks have become the de facto measure of frontier AI competence, guiding model selection, investment, and deployment.

arXiv:2605.05409v1 Announce Type: new Abstract: Financial document question answering (QA) demands complex multi-step numerical reasoning over heterogeneous evidence--structured

arXiv:2605.00257v1 Announce Type: cross Abstract: The inception of Large Language Models (LLMs) has catalyzed AI adoption in the finance sector, yet their reliability in complex,

arXiv:2604.19755v1 Announce Type: new Abstract: Anti-money laundering (AML) transaction monitoring generates large volumes of alerts that must be rapidly triaged by investigators

arXiv:2608.28402v1 Announce Type: new Abstract: Across audit applications, judgments must be supported by reasonable evidence. However, standard financial language models