E3解决了论文评审中遗漏关键技术问题的痛点，做学术评审或论文质量控制的团队可以直接用这套开源工具提升效率，建议点开看看具体实现。
E3是一个自动化的论文评审助手，旨在帮助审稿人和工程团队识别论文中的技术问题。它能够报告问题的性质、位置、对贡献的影响以及解决所需的证据，涵盖未支持的声明、缺失的消融实验、弱基线、隐藏假设、有效性威胁和数据泄露风险。为了无污染地评估E3，研究采用了issue级回测协议：只使用训练截止日期后的论文，并由一个观察匿名评审的元法官标记每个问题来源对为“捕获”、“部分”或“遗漏”。在100篇ICLR 2026论文和4598个问题行的测试中，E3在所有聚合指标上实现了最高召回率，部分包含召回率达90.2%，比GPT高15.5个百分点，比Claude高17.1个百分点，比人类评审高29.2个百分点。E3还恢复了人类评审提出的89.6%的问题，并额外发现了1635个被人类遗漏的问题。
E3: Issue-Level Backtesting for Automated Research Critique
We present E3, an automated review assistant that augments reviewers and engineering teams by identifying decision-relevant technical concerns in research papers. For each concern, E3 reports its nature, its location, its bearing on the contribution, and the analysis or evidence that would resolve it, covering unsupported claims, missing ablations, weak baselines, hidden assumptions, threats to validity, and leakage risks. To evaluate E3 without contamination confounds we adopt an issue-level backtesting protocol: the corpus is restricted to papers postdating the training cutoff of every automated source, and for each paper a meta-judge that observes only anonymised reviews labels every issue-source pair as Caught, Partial, or Missed. Applied to 100 ICLR 2026 papers and 4598 judged issue rows, comparing E3 against the ICLR human reviews and two prompt-matched LLM baselines built on gpt-5.4 from OpenAI and claude-opus-4-6 from Anthropic, with meta-judge gpt-5.5, E3 attains the highest recall on every aggregate metric. Partial-inclusive recall reaches 90.2 percent, which is 15.5 points over GPT, 17.1 points over Claude, and 29.2 points over the human reviews, and strict recall preserves the ordering at 65.8 percent. On concerns raised by the human reviewers, E3 recovers 89.6 percent; on concerns the human reviewers missed it surfaces 1635 additional rows admitted into the judged union, 406 above the next-best source. Corpus, baseline prompts, judge prompt template, and evaluation code are released.