Voyager: skill libraries unlock 3.3× more item discovery
Voyager's persistent code skill library discovers 3.3× more Minecraft items than prior SOTA without fine-tuning—the reuse pattern ledger agents need.
#automation
Automation
Automation techniques and tools for financial data processing workflows
HippoRAG: 89.1% Recall@5 on 2Wiki vs 68.2% ColBERT
HippoRAG's OpenIE+PageRank memory hits 89.1% Recall@5 on 2WikiMultiHopQA versus 68.2% ColBERTv2—path-aware retrieval for multi-year ledgers.
AgentBench: Evaluating LLMs as Agents — Lessons for Financial AI Reliability
AgentBench scored GPT-4 4.01 versus 0.96 for the best open-source LLM. Those failures are exactly what breaks a Beancount write-back agent on a live ledger.
AutoGen: Multi-Agent Conversation Frameworks for Finance AI
AutoGen's two-agent conversation lifts MATH accuracy from 55% to 69%, and its SafeGuard agent adds up to 35 F1 points on unsafe-code detection.
Gorilla: How Retrieval-Aware Training Reduces LLM API Hallucinations from 78% to 11%
Gorilla's Retriever-Aware Training cuts LLM API hallucination rates from 78% to 11%, making tool calls reliable enough for finance agents that write entries.
MemGPT: 92.5% multi-session recall vs 32.1% baseline
MemGPT's OS-style memory tiers push GPT-4 multi-session chat accuracy to 92.5% versus 32.1% fixed-context—needed for multi-year ledger agents.
SWE-agent: How Interface Design Unlocks Automated Software Engineering
SWE-agent's Agent-Computer Interfaces lifted GPT-4 Turbo from raw shell to 12.47% on SWE-bench, a 10.7-point gain from interface design alone.
SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
SWE-bench tests language models on 2,294 real GitHub issues; at publication Claude 2 resolved only 1.96%. Retrieval and patch-length limits shape coding agents.
CodeAct: Why Executable Python Code Makes LLM Agents 20% More Accurate
CodeAct replaces JSON tool calls with executable Python, lifting GPT-4 agent success by ~20 points and cutting turns 30%.
Self-Correction: GPT-4 falls 95.5%→91.5% on GSM8K
Huang et al. (ICLR 2024): intrinsic self-correction drops GPT-4 from 95.5% to 91.5% on GSM8K—ledger agents need external validators, not self-review.
Tree of Thoughts: Deliberate Problem Solving with LLM Search
Tree of Thoughts hits 74% on Game of 24 where GPT-4 chain-of-thought gets 4%, by searching and backtracking over steps, a pattern finance agents can borrow.
CRITIC: Why LLM Self-Correction Requires External Tool Feedback
CRITIC lifts open-domain QA by 7.7 F1 and cuts toxicity 79.2% by verifying LLM revisions against external tools, not itself.