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Счетоводство с обикновен текст
Изследвания, основани на счетоводни формати и работни процеси в обикновен текст
Uncertainty-Aware Deferral for LLM Agents: When to Escalate from Small to Large Models
ReDAct defers from a small model to a large one only when perplexity signals uncertainty, cutting cost 64% at matching accuracy for agent workflows.
OpenHands: Open Platform for AI Software Agents and What It Means for Finance Automation
OpenHands' CodeAct agent scores 26% on SWE-Bench Lite, showing what AI agents reliably do today. Finance automation should start tightly scoped, not autonomous.
LLMs Score 2.3% on Beancount DSL Generation: The LLMFinLiteracy Benchmark
LLMFinLiteracy finds five ~7B models write correct Beancount transactions just 2.3% of the time, failing on accounting reasoning rather than syntax.
TableMaster: Adaptive Reasoning for Table Understanding with LLMs
TableMaster hits 78.13% on WikiTQ with GPT-4o-mini, 13 points over Chain-of-Table, via table-of-focus plus adaptive reasoning for agents over Beancount ledgers.
τ²-bench: Measuring the Cost of Dual-Control in Conversational AI Agents
τ²-bench finds active users with their own tools cut conversational agent success by 18–25 points. Beancount agents sharing write access lose the same way.
GAIA Benchmark: Measuring What Frontier AI Agents Can Actually Do
GAIA's 466 tasks show frontier AI agents at 74.55% versus 92% for humans, with Level 3 coordination still the hardest gap to close.
WorkArena: How LLM Web Agents Perform on Real Enterprise Knowledge Work
WorkArena's 33 ServiceNow tasks show GPT-4o at 42.7% overall but 0% on list filters, a wall structured UI interaction must clear.
τ-bench: Measuring AI Agent Reliability in Real-World Tool-Use Domains
τ-bench finds top LLMs fall from pass@1 0.692 to pass@4 0.462 on retail tool-use tasks. Write-back agents on a ledger face the same consistency cliff.
Chain-of-Table: Evolving Tables in the LLM Reasoning Chain
Chain-of-Table hits 67.31% on WikiTQ versus 61.48% for text-only chain-of-thought, and leads by 10.25 points on tables over 4,000 tokens.
TableLlama: Can a 7B Open Model Match GPT-4 on Table Understanding?
TableLlama beats GPT-4 on column type annotation (F1 94 vs 32) but trails by 33 points on WikiTQ compositional reasoning.
TAPAS: Weakly Supervised Table QA Without SQL, and What It Means for Beancount
TAPAS answers table questions by selecting cells, never generating SQL. It fits small Beancount ledger queries but breaks down at scale.