#reconciliation
Automated ledger reconciliation using language model agents
Синтетично предизвикателство с три реда Beancount с независимо изведен отговор — проверката приключва на 2958.50 USD, септемврийската печалба на 1958.50 USD — изпълнено от един записан агентски прогон, като неуспехите му са запазени в транскрипта.
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.
EnterpriseArena runs 11 LLMs through a 132-month CFO simulation tracking survival, terminal valuation, and book-closing rates. Only Qwen3.5-9B survives 80% of runs; GPT-5.4 and DeepSeek-V3.1 hit 0%. Human experts achieve 100% survival at 5× the terminal value. The critical bottleneck: LLMs skip ledger reconciliation 80% of the time, acting on stale financial state.
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.
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.
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.
Voyager's persistent code skill library discovers 3.3× more Minecraft items than prior SOTA without fine-tuning—the reuse pattern ledger agents need.
AutoGen (Wu et al., 2023) introduces a multi-agent conversation framework where LLM-backed agents pass messages to complete tasks; a two-agent setup lifts MATH benchmark accuracy from 55% to 69%, and a dedicated SafeGuard agent improves unsafe-code detection by up to 35 F1 points — findings directly applicable to building safe, modular Beancount automation pipelines.
CodeAct (ICML 2024) replaces JSON tool-calling with executable Python code, improving GPT-4 agent success rates by ~20 percentage points on multi-tool tasks and reducing interaction turns by 30% — with direct implications for building reliable Beancount reconciliation agents.
CRITIC (ICLR 2024) achieves 7.7 F1 gains on open-domain QA and a 79.2% toxicity reduction by grounding LLM revision in external tool signals — a verify-then-correct loop that maps directly onto write-back safety for Beancount finance agents.
ReAct (Yao et al., ICLR 2023) редува разсъждения по верига от мисли с действия с инструменти в една траектория, превъзхождайки чистия CoT при проверка на факти и имитационното обучение при въплътени задачи с 34 процентни пункта. Този анализ разглежда режимите на отказ на статията — разсейване, предизвикано от търсене, и натрупващи се грешки — и какво означават те за автономни агенти, записващи обратно в Beancount счетоводни книги.
