Unverified50% confidenceBenchmarkExact time
在从50%到92%的准确率提升中,REPL贡献最大(直接从50%跳至74%),随后领域知识注入、模糊搜索、公式追踪、系统prompt优化和bug修复层层累积
1
Sources
50%
Confidence
Long-term
Relevance
7/14/2026
First Seen
Sources
从50%到92%:让AI智能体精通电子表格的实战方法论
Related Claims
VerifiedThrough experiments on multi-hop reasoning benchmarks such as HotpotQA and FEVER, the ReAct paper demonstrated that compared to pure Chain-of-Thought reasoning, ReAct improved accuracy by approximately 10%–30% on tasks requiring external information retrieval.73% similarUnverifiedMeta、NVIDIA等公司的研究表明RAG在知识密集型任务上的准确率比纯参数记忆提高20%-40%70% similarUnverifiedThe per-step accuracy of current top-tier models is approximately between 90% and 95%.68% similarUnverifiedMedIQ多智能体系统最终达到57%准确率,摘要智能体带来约5%提升,而拥有全部前置信息的Oracle上限为66%68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/510605API
curl https://kongchang.com/api/v1/knowledge/claims/510605MCP
get_claim(id=510605)