PAL (Program-Aided Language Models) achieves a +38pp accuracy gain over chain-of-thought on arithmetic-heavy tasks by delegating computation to a Python interpreter — a directly applicable architecture for reliable Beancount ledger queries and finance AI.
Four 2024–2025 benchmarks show GPT-4 scoring 42% on real-world table QA versus 86% for humans, with complex aggregations collapsing to 19.6%—and Beancount's native syntax sits at the worst-performing end of the serialization hierarchy for LLM input.
A close reading of Wei et al.'s 2022 Chain-of-Thought paper and what it means for finance AI — why CoT raises precision but may cut recall on rare-event detection, why the scale threshold matters for production agents, and what a finance team building on LLMs should watch out for.
PHANTOM (NeurIPS 2025) is the first benchmark to measure LLM hallucination detection on real SEC filings across context lengths up to 30,000 tokens. Qwen3-30B-A3B-Thinking leads with F1=0.882; 7B models score near random guessing — with direct implications for autonomous accounting agents.
FinBen evaluates 15 LLMs across 36 financial datasets at NeurIPS 2024, finding GPT-4 reaches 0.63 Exact Match on numerical QA and 0.54 on stock movement forecasting — near chance. Here is what those numbers mean for building a reliable accounting agent on a Beancount ledger.
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