3 papers in the archive (2025–2026), average empirical rigor 7.2/10, average math complexity 4.5/10. Quadrant mix: Holy Grail ×1, Street Traders ×2.
Works mostly in NLP & LLMs in Finance.
Frequent co-authors: Bhaskarjit Sarmah (3), Stefano Pasquali (3), Fabrizio Dimino (2), Jacopo Biggiogera (1), Nicolas Colombo (1).
Scores are our LLM-assisted heuristics (methodology), not peer review or citation counts. Author pages are generated from paper metadata; name variants may split one person across pages. arXiv author page →
Ordered by a blend of empirical rigor (60%) and math complexity (40%).
Large Language Models are increasingly adopted in financial applications to support investment workflows. However, prior studies have seldom examined how these models reflect biases related to firm size, sector, or financial characteristics, which can significantly impact decision-making. This paper
The growing adoption of large language models (LLMs) in finance exposes high-stakes decision-making to subtle, underexamined positional biases. The complexity and opacity of modern model architectures compound this risk. We present the first unified framework and benchmark that not only detects and
High-stakes tabular pipelines rely on scattered, static quality checks that are hard to evolve and audit against policy intent. Regulated domains particularly suffer as the LLM-era automation raises expectations for traceability and accountability. We propose a vision for an ontology-grounded, repos