10 tagged with "Reconciliation"
Automated ledger reconciliation using language model agents
FinRAGBench-V: Multimodal RAG with Visual Citations in the Financial Domain
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.
Can LLM Agents Be CFOs? EnterpriseArena's 132-Month Simulation Reveals a Wide Gap
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: Benchmarking LLM Agents for Real-World Financial Tool Use under MCP
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.
Found in the Middle: Calibrating Positional Attention Bias Improves Long-Context RAG
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: 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.
Voyager: Skill Libraries as the Foundation for Lifelong AI Agent Learning
Voyager, a GPT-4-powered Minecraft agent from NVIDIA and Caltech, demonstrates that a persistent code skill library enables genuine lifelong learning without fine-tuning — discovering 3.3× more items than prior state-of-the-art. The pattern maps directly onto long-horizon Beancount ledger automation, though financial correctness demands staging layers that game sandboxes never require.
AutoGen: Multi-Agent Conversation Frameworks for Finance AI
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: Why Executable Python Code Makes LLM Agents 20% More Accurate
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: Why LLM Self-Correction Requires External Tool Feedback
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: Synergizing Reasoning and Acting in Language Models
ReAct (Yao et al., ICLR 2023) interleaves chain-of-thought reasoning with tool actions in a single trajectory, outperforming pure CoT on fact verification and imitation learning on embodied tasks by 34 percentage points. This analysis covers the paper's failure modes — search-induced distraction and compounding errors — and what they mean for autonomous agents writing back to Beancount ledgers.