57 tagged with "Automation"
Automation techniques and tools for financial data processing workflows
M3MAD-Bench: Are Multi-Agent Debates Really Effective Across Domains and Modalities?
M3MAD-Bench stress-tests Multi-Agent Debate across 9 models, 5 domains, and vision-language settings, finding that Collective Delusion causes 65% of failures, adversarial debate cuts accuracy by up to 12.8%, and Self-Consistency typically matches debate accuracy at lower token cost.
AGrail: Adaptive Safety Guardrails for LLM Agents That Learn Across Tasks
AGrail (ACL 2025) introduces a two-LLM cooperative guardrail that adapts safety checks at inference time via test-time adaptation, achieving 0% prompt injection attack success and 95.6% benign action preservation on Safe-OS — compared to GuardAgent and LLaMA-Guard blocking up to 49.2% of legitimate actions.
ShieldAgent: Verifiable Safety Policy Reasoning for LLM Agents
ShieldAgent (ICML 2025) replaces LLM-based guardrails with probabilistic rule circuits built on Markov Logic Networks, achieving 90.4% accuracy on agent attacks with 64.7% fewer API calls — and what it means for verifiable safety in financial AI systems.
Atlas: Joint Retriever-Reader Pre-Training Beats 540B-Parameter LLMs with 11B Parameters
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.
GuardAgent: Deterministic Safety Enforcement for LLM Agents via Code Execution
GuardAgent (ICML 2025) places a separate LLM agent between a target agent and its environment, verifying every proposed action by generating and running Python code — achieving 98.7% policy enforcement accuracy while preserving 100% task completion, versus 81% accuracy and 29–71% task failure for prompt-embedded safety rules.
Multiagent LLM Debate: Real Accuracy Gains, Uncontrolled Compute, and Collective Delusion
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.
TAT-LLM: Ge-fined-tunde LLaMA 2 voor discreet redeneren over financiële tabellen en tekst
TAT-LLM fine-tunt LLaMA 2 7B met LoRA op financiële tabel-tekst QA-benchmarks en behaalt 64,60% EM op FinQA — waarmee het de 63,91% van GPT-4 verslaat — door redenering te ontleden in deterministische Extraheer-Redeneer-Voer-uit stappen die rekenkundige fouten elimineren.
Fine-Tuning vs. RAG: Why Retrieval Wins for Injecting New Knowledge into 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: Interleaving Retrieval with Chain-of-Thought for Multi-Step QA
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.
FLARE: Active Retrieval Augmented Generation
FLARE (EMNLP 2023) improves on standard RAG by triggering retrieval mid-generation using token-probability confidence thresholds, reaching 51.0 EM on 2WikiMultihopQA versus 39.4 for single-retrieval — but calibration failures in instruction-tuned chat models limit its reliability for production finance agents.
DSPy: Replacing Brittle Prompt Engineering with Compiled LLM Pipelines
DSPy replaces hand-crafted prompt strings with declarative signatures and a metric-driven compiler—boosting Llama2-13b from 9.4% to 46.9% on GSM8K math reasoning and offering a more maintainable path for production finance AI pipelines.
LATS: Language Agent Tree Search — 추론, 행동, 계획을 하나의 프레임워크로 통합
LATS(Language Agent Tree Search, ICML 2024)는 ReAct, Tree of Thoughts, Reflexion을 단일 MCTS 프레임워크로 통합하여 GPT-4와 함께 HumanEval에서 92.7%의 pass@1을 달성했습니다. Git 기반의 Beancount 장부의 경우, 운영 환경에서 LATS를 제한하는 상태 복원 요구 사항을 아주 쉽게 충족할 수 있습니다.