Bean Labs Research Log
Open experiments and findings from Bean Labs — the Finance AI Agent research initiative by Beancount.io. Browse by tag.
OpenHands: Open Platform for AI Software Agents and What It Means for Finance Automation
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.
Fin-RATE: How LLMs Fail at Cross-Period and Cross-Entity Financial Analysis
Fin-RATE benchmarks 17 LLMs on 7,500 expert-curated QA pairs from 2,472 SEC filings, revealing an 18.60% accuracy collapse under longitudinal tracking and a 54-point drop for finance-specialized Fin-R1 on cross-entity tasks — with the retrieval pipeline, not the backbone model, as the binding bottleneck.
FinDER: Real Analyst Queries Expose a 74% Recall Gap in Financial RAG
FinDER benchmarks RAG on 5,703 real hedge fund analyst queries against S&P 500 10-K filings; E5-Mistral achieves only 25.95% context recall, and abbreviation-heavy queries cost 8.2 precision points — evidence that query normalization, not better embeddings, is the first fix for finance AI pipelines.
Lost in the Middle: Position Bias in LLMs and Its Impact on Finance AI
The TACL 2024 paper by Liu et al. shows LLMs perform up to 20 points worse on information buried in the middle of long contexts — a U-shaped degradation affecting every tested model including Claude-1.3-100K — with concrete implications for how RAG pipelines should order retrieved passages in finance and accounting applications.
AD-LLM Benchmark: GPT-4o Hits 0.93+ AUROC Zero-Shot for Text Anomaly Detection
AD-LLM benchmarks GPT-4o and Llama 3.1 8B across three anomaly detection roles — zero-shot detector, data augmenter, and model selector — on five NLP datasets; GPT-4o reaches AUROC 0.93–0.99 zero-shot, but LLM-based model selection remains unreliable, with direct implications for financial audit AI.
CausalTAD: Causal Column Ordering for LLM Tabular Anomaly Detection
CausalTAD improves LLM-based tabular anomaly detection by reordering table columns to respect causal dependencies before serialization, lifting average AUC-ROC from 0.803 to 0.834 over AnoLLM on mixed-type benchmarks — with direct implications for detecting anomalies in structured ledger data.
AnoLLM: Fine-Tuning LLMs for Tabular Anomaly Detection in Financial Data
AnoLLM (ICLR 2025) reformulates tabular anomaly detection as LLM density estimation — fine-tuning on normal rows and scoring by negative log-likelihood. It outperforms classical methods on mixed-type fraud datasets but offers no edge on purely numerical data, with real implications for detecting anomalies in Beancount ledger entries.
LLMs Score 2.3% on Beancount DSL Generation: The LLMFinLiteracy Benchmark
The LLMFinLiteracy benchmark finds that five open-weight ~7B models generate fully correct Beancount transactions only 2.3% of the time, with failures concentrated in accounting reasoning—not syntax—pointing to compiler-in-the-loop feedback as the critical missing ingredient for reliable write-back agents.
TableMaster: Adaptive Reasoning for Table Understanding with LLMs
TableMaster is a prompting-only pipeline that reaches 78.13% on WikiTQ with GPT-4o-mini—13 points above Chain-of-Table—by combining table-of-focus extraction, semantic verbalization, and adaptive switching between text and symbolic reasoning. Here is what the architecture means for AI agents over financial ledgers like Beancount.
Zero-Shot Anomaly Detection with LLMs: How GPT-4 Performs on Tabular Data
GPT-4 achieves 74.1 mean AUROC on the ODDS benchmark without fine-tuning — nearly matching the classical ECOD baseline at 75.5 — but fails on multi-dimensional anomalies and high-variance datasets; a critical review of zero-shot LLM anomaly detection and its implications for automated Beancount ledger auditing.
DocFinQA: Long-Context Financial Reasoning on Full SEC Filings
DocFinQA replaces FinQA's curated 700-word passages with full 123,000-word SEC filings, exposing a 175× context increase that nearly halves GPT-4 accuracy on long documents. Retrieval pipelines fail to surface the right chunk 45% of the time at HR@3 — and long-context models are not a substitute.
TheAgentCompany: Benchmarking LLM Agents on Real-World Enterprise Tasks
TheAgentCompany tests 175 real workplace tasks across a simulated intranet with GitLab, OwnCloud, and RocketChat. The best model (Gemini-2.5-Pro) completes only 30% of tasks at $4 each, revealing that autonomous agents remain far from viable for accounting and finance workflows.