CMU and NC State researchers propose using System-Theoretic Process Analysis (STPA) and a capability-enhanced Model Context Protocol to derive formal safety specifications for LLM agent tool use, with Alloy-based verification demonstrating absence of unsafe flows in a calendar scheduling case study.
Microsoft's GraphRAG builds a Leiden-partitioned entity graph over a text corpus and precomputes community summaries to answer global sensemaking questions that standard vector RAG cannot handle — but a 2025 bias audit shows its 72–83% win rates collapse after correcting for position and length artifacts in LLM-as-judge evaluation.
FinAuditing tests 13 LLMs zero-shot on 1,102 real SEC XBRL filing instances; top scores are 13.86% on financial math verification and 12.42% on concept retrieval—results that directly bound what AI accounting tools can be trusted to automate without external tooling.
StructRAG (ICLR 2025) routes each query to a task-appropriate structure type — table, graph, catalogue, algorithm, or chunk — before reasoning, scoring 28 points higher than GraphRAG on the Loong benchmark while running 22× faster, with the DPO-trained router alone accounting for a 15-point accuracy gain.
Atlas (JMLR 2023) achieves 42.4% accuracy on Natural Questions with only 64 training examples—beating PaLM 540B by 3 points using 11B parameters—by jointly pre-training a Contriever-based dense retriever with a T5 Fusion-in-Decoder reader. Analysis covers retrieval accuracy limits, 587GB index infrastructure costs, and implications for Beancount ledger QA systems.
Izacard and Grave's FiD architecture independently encodes retrieved passages then fuses them in the decoder, outperforming RAG-Sequence by 4–11 points on NQ and TriviaQA. This post examines the design and its implications for Beancount ledger QA, where multi-entry synthesis across transactions is the norm.
A close reading of Du et al.'s ICML 2024 multiagent debate paper — which reports 14.8-point accuracy gains on arithmetic — alongside 2025 rebuttals showing equal-budget single agents match debate performance, and an analysis of why Collective Delusion (65% of debate failures) poses specific risks for AI-assisted ledger commits.
A NeurIPS 2024 Spotlight paper ablates three LLM-based time series forecasting methods — OneFitsAll, Time-LLM, and CALF — and finds that removing the language model improves accuracy in most cases, with up to a 1,383× training speedup. For finance AI applications like Beancount balance prediction, lightweight purpose-built models consistently beat repurposed LLMs.
Empirical comparison of RAG vs. unsupervised fine-tuning across 7B-parameter LLMs shows RAG achieves 0.875+ accuracy on post-cutoff facts while fine-tuning plateaus at 0.504 — with direct implications for Beancount agent design and any system requiring frequent knowledge updates.
IRCoT interleaves BM25 retrieval with each step of a chain-of-thought reasoning loop, achieving +11.3 retrieval recall and +7.1 F1 on HotpotQA over one-step RAG — and shows a 3B model can beat GPT-3 175B when retrieval strategy is right.
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