arXiv:2601. 06401v2 Announce Type: replace Abstract: Large language models are becoming increasingly significant in financial applications.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2608. 08634v1 Announce Type: new Abstract: Open-weight language models from Chinese AI labs caught up on benchmarks relative to proprietary frontier models in recent months.
arXiv:2608. 12342v1 Announce Type: cross Abstract: Ensuring the accuracy of financial documents is critical for economic analysis, regulatory compliance, and corporate decision-making.
arXiv:2603. 19225v3 Announce Type: replace-cross Abstract: Real-world financial decision-making is a challenging problem that requires reasoning over heterogeneous signals, including company fundamentals derived from regulatory filings and trading signals computed from price dynamics.
arXiv:2607. 15414v1 Announce Type: cross Abstract: Large Language Models (LLMs) have emerged as powerful tools for processing the heterogeneous information environments of modern financial markets.
CIFQA is a deterministic, tool‑grounded multi‑agent framework that separates language understanding from numerical execution for financial question answering. It assigns specialized agents for interpretation, routing, parameter extraction, computation planning, and response generation, while deterministic Python tools perform the calculations. On a fixed‑deposit benchmark, CIFQA achieves 95.54% accuracy on calculation‑intensive queries and 90.87% overall, outperforming larger LLM baselines and showing that architecture, not scale, drives numerical reliability.
arXiv:2607. 27853v2 Announce Type: replace-cross Abstract: Powered by advances in LLMs and autonomous agents, deep research has become one of the most widely adopted agentic products.