arXiv:2602. 07294v4 Announce Type: replace-cross Abstract: With the increasing deployment of Large Language Models (LLMs) in the finance domain, LLMs are increasingly expected to parse complex regulatory disclosures.
By Yidong Jiang, Junrong Chen, Eftychia Makri, Jialin Chen, Peiwen Li, Ali Maatouk, Leandros Tassiulas, Eliot Brenner, Bing Xiang, Rex Ying
FinMultiHopQA is a new benchmark comprising 15,600 financial questions that require multi‑hop reasoning across filing sections, fiscal years, and peer companies. It is grounded in FinReflectKG, a temporally indexed financial knowledge graph with over 17.5 million triples derived from SEC 10‑K filings. The benchmark uniquely supports controlled multi‑hop taxonomies, document topologies, motif‑level reproducibility, and provenance‑linked auditability, and it allows researchers to test how different evidence presentations affect large language model performance.
By Abhinav Arun, Reetu Raj Harsh, Bhaskarjit Sarmah, Stefano Pasquali
arXiv:2608. 04374v1 Announce Type: cross Abstract: Large language models can produce fluent financial analysis, but fluency alone does not establish whether a report is suitable for institutional delivery.
By Yinghao Tang, Tan Zhenwei, Yiyao Wang, Wanli Gu, Xiaolu Zhang, Jun Zhou, Wei Chen
arXiv:2606. 18192v1 Announce Type: new Abstract: As high-quality public web corpora become increasingly exhausted, clean long-context documents have become a scarce and expensive source of training data for large language models (LLMs).
By Nick Bettencourt, Xiaowei Ding, Kay Giesecke
arXiv:2609.35864v1 Announce Type: new
Abstract: SEC 10-K filings contain substantial financial information that is not consistently captured in structured datasets, creating a missing-data problem af...
By Prisha Nair, Roee Shraga
FIRME (Fusion and Identity Registry for Memory Evolution) is a registry‑anchored memory graph designed to integrate investor observations across time and sources while preserving identity, provenance, and context. It reconciles varying surface forms of recurring observations into canonical relationships, fuses investor‑call discussions with portfolio cash‑flow signals within periods, and uses persistent aliases to maintain consistent relationship histories across periods. Evaluations on proprietary investor‑call data, cash‑flow records, and S&P 500 earnings‑call transcripts show that FIRME improves Correct Match Rate by 7.8–16.5 percentage points and significantly enhances reattachment recall and merge precision compared to a baseline registry lacking alias memory.