AnoLLM: Fine-Tuning LLMs for Tabular Anomaly Detection in Financial Data
AnoLLM beats classical baselines on mixed-type fraud data by scoring rows with LLM negative log-likelihood, but adds no edge on purely numerical tables.
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Data Science
Data science methods applied to financial datasets and accounting workflows
TableMaster: Adaptive Reasoning for Table Understanding with LLMs
TableMaster hits 78.13% on WikiTQ with GPT-4o-mini, 13 points over Chain-of-Table, via table-of-focus plus adaptive reasoning for agents over Beancount ledgers.
Zero-Shot Anomaly Detection with LLMs: How GPT-4 Performs on Tabular Data
GPT-4 reaches 74.1 mean AUROC on ODDS zero-shot, near the 75.5 ECOD baseline, but fails on high-variance data. Ledger auditing is not solved yet.
DocFinQA: Long-Context Financial Reasoning on Full SEC Filings
DocFinQA swaps FinQA's 700-word passages for full SEC filings, a 175× longer context that nearly halves GPT-4 accuracy on long documents.
GAIA Benchmark: Measuring What Frontier AI Agents Can Actually Do
GAIA's 466 tasks show frontier AI agents at 74.55% versus 92% for humans, with Level 3 coordination still the hardest gap to close.
OSWorld: Desktop AI Agents Succeed on 12% of Tasks Where Humans Succeed on 72%
OSWorld finds desktop AI agents succeed on 12.24% of real tasks versus 72.36% for humans, with 75% of failures from visuomotor grounding, not reasoning.
Chain-of-Table: Evolving Tables in the LLM Reasoning Chain
Chain-of-Table hits 67.31% on WikiTQ versus 61.48% for text-only chain-of-thought, and leads by 10.25 points on tables over 4,000 tokens.
TAPAS: Weakly Supervised Table QA Without SQL, and What It Means for Beancount
TAPAS answers table questions by selecting cells, never generating SQL. It fits small Beancount ledger queries but breaks down at scale.
GraphRAG: 72–83% wins vanish after LLM-judge bias fix
Microsoft's GraphRAG posts 72–83% sensemaking wins over vector RAG; a 2025 audit collapses those after correcting judge bias—caution for multi-doc ledger QA.
InvestorBench: Qwen2.5-72B tops stocks at 46.15% CR
InvestorBench: Qwen2.5-72B leads stock trading at 46.15% CR; finance-tuned Palmyra-Fin backfires on equities—size beats domain fine-tuning.
M3MAD-Bench: Are Multi-Agent Debates Really Effective Across Domains and Modalities?
M3MAD-Bench finds Collective Delusion drives 65% of multi-agent debate failures, and adversarial debate cuts accuracy by up to 12.8%.
Atlas: Joint Retriever-Reader Pre-Training Beats 540B-Parameter LLMs with 11B Parameters
Atlas hits 42.4% accuracy on Natural Questions with 64 examples, beating PaLM 540B by 3 points at 11B parameters via joint retriever-reader pre-training.