#machine-learning
Machine learning techniques for financial data analysis and automation
FinRAGBench-V (EMNLP 2025) is the first large-scale benchmark for multimodal RAG with visual citations in finance, covering 112K+ document pages and 1,394 human-annotated QA pairs. Top models achieve only 20–61% block-level citation recall, and multimodal retrieval outperforms text-only by nearly 50 percentage points.
WildToolBench (ICLR 2026) evaluates 57 LLMs on 1,024 tasks drawn from real user behavior — no model exceeds 15% session accuracy, with compositional orchestration, hidden intent, and instruction transitions as the three sharpest failure modes.
A systematic survey of LLM confidence estimation and calibration methods—white-box logit approaches, consistency-based SelfCheckGPT, and semantic entropy—reveals that verbalized confidence scores from GPT-4 achieve only ~62.7% AUROC, barely above chance, with direct implications for deploying uncertainty-aware agents in finance and accounting.
JSONSchemaBench tests 9,558 real-world JSON schemas against six constrained decoding frameworks and finds that schema complexity causes coverage to collapse from 86% on simple schemas to 3% on complex ones, with XGrammar silently emitting 38 non-compliant outputs and no framework covering all 45 JSON Schema feature categories.
FinMCP-Bench evaluates six LLM models on 613 real-world financial tool-use tasks backed by 65 MCP servers — the best model scores 3.08% exact match on multi-turn tasks, revealing a 20× performance collapse from single-tool to multi-turn scenarios.
FinTrace benchmarks 13 LLMs on 800 expert-annotated financial task trajectories across 9 metrics, finding that frontier models achieve strong tool selection (F1 ~0.9) but score only 3.23/5 on information utilization — the step where agents reason over what tools return.
FinToolBench съчетава 760 реални финансови API инструмента с 295 изпълними заявки, за да оцени LLM агенти върху реални финансови задачи — разкривайки, че консервативният процент на извикване от 22,7% на GPT-4o води до по-високо качество на отговорите (CSS 0,670) от агресивния TIR от 87,1% на Qwen3-8B, докато несъответствието на намеренията надхвърля 50% при всеки тестван модел.
OmniEval (EMNLP 2025) benchmarks RAG systems across 5 task types × 16 financial topics using 11.4k auto-generated test cases. The best systems achieve only 36% numerical accuracy — concrete evidence that RAG pipelines need validation layers before writing to structured financial ledgers.
A critical reading of Xu and Ding's NAACL 2025 survey on LLM-based anomaly and OOD detection: the detection-vs-generation taxonomy holds up, but near-total absence of tabular coverage means financial AI practitioners must synthesize insights from vision models themselves.
A training-free inference-time calibration subtracts positional bias from LLM attention weights, recovering up to 15 percentage points of RAG accuracy when retrieved documents are buried mid-context — and what it means for finance-specific agent pipelines.
ReDAct runs a small model by default and escalates to an expensive model only when token-level perplexity signals uncertainty, achieving 64% cost savings over GPT-5.2-only while matching or exceeding its accuracy — a directly applicable pattern for Beancount transaction-categorization agents.
OpenHands is an MIT-licensed, Docker-sandboxed agent platform where CodeAct achieves 26% on SWE-Bench Lite — a sobering benchmark that establishes what AI agents can reliably do today, and why the first productive finance deployments should be tightly scoped rather than autonomous.
