English(EN) EPSBench: Benchmarking LLM Agents for Earnings Forecasting

新基准评估LLM代理的盈利预测准确性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-31 04:00

研究人员推出了EPSBench，这是一个旨在评估大型语言模型（LLM）代理在公司盈利预测方面能力的新基准。该基准要求代理分析财务文件、财报电话会议记录和新闻，以估算损益表驱动因素，然后将其转换为GAAP稀释每股收益（EPS）。测试了六个模型，其中三个在点预测误差方面与分析师共识没有显著差异，而另外三个表现更差。此外，大多数模型的预测区间未能达到其声明的覆盖率，通常预测过低。 AI

影响该基准有望推动LLM代理在金融分析能力方面的改进，可能影响投资策略和财务报告。

排序理由该集群包含一篇介绍用于评估LLM代理的新基准的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇介绍用于评估LLM代理的新基准的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, product; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 10 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Yanbin Yin, Chuyu Xu, Liqin Ye, Jing Peng, Chao Zhang · 2026-10-31 04:00

EPSBench：为收益预测进行大语言模型智能体基准测试

arXiv:2610.38293v1 Announce Type: cross Abstract: Quarterly earnings forecasting requires an agent to connect financial evidence to the income-statement line items that produce a company's reported earnings. We introduce EPSBench, a benchmark for studying this ability across 149 …

本文如何被排名

报道来源 [1]

EPSBench：为收益预测进行大语言模型智能体基准测试

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