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开发阶段案例 · 模型盲评尚待独立人工复核，不代表正式 Benchmark 结论。
自动补全无 Evidence 更优13 / 162 · dd158ce70abf9ca5

A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challenges

定量生物学 · 2306.15890v1

FLOWING EVIDENCE BENCHMARK

怎么判断 Evidence 真的有帮助？

核心问题是：在同一写作位置、使用同一模型和任务时，提供检索文献片段会怎样改变首次输出？我们成对比较两种条件，保留持平、不可用和评审未完成的结果。

同一段原稿 · 比较是否提供 Evidence

01 · 固定写作位置

论文原稿

同一处写作点位 ▌

同一原稿位置的自动补全成对比较

02 · 构造两组输入

两组共用

原稿上下文、模型、任务和提示词

A · 提供 Evidence

额外提供检索文献片段

B · 不提供 Evidence

不提供检索文献片段

LLM

同一模型、同一版本

A → 首次输出

B → 首次输出

盲评 Agent

匿名标记两份首次输出为 X、Y

按续写质量判断：准确性、任务贴合度和可用性
输出：X 更优 / 持平 / Y 更优 / 两组均不可用

换序复评：X / Y → Y / X

盲评具体怎么判？

① 匿名两份输出
评审看到同一原稿和两份首次输出，但不知道哪份用了 Evidence。

② 比较并换序复评
根据当前任务的标准，按 X/Y、再按 Y/X 的顺序各评一次。

③ 复核分歧
两次结论不一致时再做第三次判定；未完成的评审也留在分母。

自动补全点位怎样分层？

在查看生成结果前，先核查检索片段是否含有能直接支撑下一步续写的具体命题；有则放在左侧机会组，否则放在右侧普通组。每个学科从可准入论文中均衡选取两组点位。50/50 是实验设计，不代表真实写作中两类点位各占一半。

AUTOCOMPLETE · 110

定量生物学、统计学、天体物理学

左侧 55 个、右侧 55 个点位；统计学采用 10 篇论文的复测结果。

AUTOCOMPLETE · 52

心理学与气候科学

左侧 26 个、右侧 26 个点位；心理学计入 9 篇，气候科学计入 4 篇。

表格里的百分比怎么算？

五学科共有 81 个左侧点位，其中 51 个点位的 Evidence 版本获评更优。持平、两组均不可用和评审未完成的点位仍计入分母。

51Evidence 版本更优
÷
81该层全部点位
=
63%该层 Evidence 获评更优的比例

来源贡献是另一项复核：已进入复核的 22 个 Evidence 获胜点位中，16 个确认直接使用了检索论文；另有 29 个胜出点位尚待复核。

当前是开发阶段的模型评审结果，尚未完成独立人工复核；这些数字不代表正式 Benchmark 结论，也不能单独证明因果关系。

原稿写作位置

原文摘录 · 非 PDF 页面

研究论文 · 原文片段

A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challenges

1 Introduction

…ave achieved initial success. In this survey, we conduct a comprehensive investigation of advanced deep learning-based models for reaction and retrosynthesis prediction. We summarize the design mechanisms, strengths, and weaknesses of state-of-the-art approaches. Then, we discuss the limitations of current solutions and open challenges in the problem itself. Finally, we present promising directions to facilitate future research. To our knowledge, this paper is the first comprehensive and systematic survey that seeks to provide a unified understanding of reaction and retrosynthesis prediction.

…rocess is notoriously labor-intensive and costly. As Eroom’s law suggests Scannell et al. 2012 , the exploration of new drugs becomes increasingly slower and more expensive over time. Therefore, it is natural and significant to leverage machine learning techniques to accelerate the drug discovery process. In recent years, the use of deep learning approaches to enhance different stages of drug discovery has become prevalent due to the rise of deep learning. Among these stages, reaction prediction and retrosynthesis prediction are two fundamental steps that can benefit from deep learning tools.

↓ 此处生成下一段续写

黄色标出写作点位附近的原文；本页根据评测输入重排文本，不是原始 PDF 截图。

同一写作位置的成对对照盲评结果：无 Evidence 更优

两组使用同一模型、同一写作任务和原稿上下文，仅比较是否提供检索文献片段。下方展示评测记录中的首次输出。

Evidence

同一模型与写作位置 · 提供检索文献片段 · 首次输出

本次未获偏好

Recent studies have sought to combine expert experience with deep learning by introducing structural motifs into retrosynthesis, treating the task as a molecular editing problem. While such motifs can simplify the editing process, they also enlarge the vocabulary and can reduce predictive consistency.

提供给 Evidence 版本的文献片段

MotifRetro: Exploring the Combinability-Consistency Trade-offs in retrosynthesis via Dynamic Motif Editing

Combining expert experience with deep learning is a promising direction for retrosynthesis prediction, and some recent studies Gao et al. (2022) ; Liu et al. (2022) ; Dai et al. (2019) ; Somnath et al. (2021) have followed this approach by introducing structural motifs into retrosynthesis, which is…

展开完整文献摘录

Combining expert experience with deep learning is a promising direction for retrosynthesis prediction, and some recent studies Gao et al. (2022) ; Liu et al. (2022) ; Dai et al. (2019) ; Somnath et al. (2021) have followed this approach by introducing structural motifs into retrosynthesis, which is essentially a molecular editing problem. As shown in Figure 1 , introducing motifs can simplify the molecular editing process, but it can also increase the vocabulary size and decrease predictive consistency.

无 Evidence

同一模型与写作位置 · 不提供检索文献片段 · 首次输出

盲评更优

Reaction prediction aims to determine the products of a given set of reactants, whereas retrosynthesis prediction seeks to identify plausible reactants that could produce a target molecule. The two tasks are complementary: the former models the forward direction of chemical transformations, while the latter reasons backward from a target compound to its synthetic precursors.

模型盲评结论

模型盲评判定“无 Evidence 更优”。下方保留评审原始理由（英文），供核对判断依据。

查看模型评审原始理由（英文）

Both outputs are coherent academic paragraphs that could follow the draft. However, Output B is preferable for this autocomplete task because it directly introduces and defines the two core topics (reaction prediction and retrosynthesis prediction) that the survey promises to cover, as established in the first paragraph's thesis statement ('reaction and retrosynthesis prediction'). This creates logical continuity: the draft introduces the survey's scope, discusses drug discovery motivation, and then Output B defines the key terms before the survey proceeds to detailed analysis. Output A, while source-grounded, jumps ahead to a specific technical approach (motif-based retrosynthesis) that would more naturally appear in a later section about methods or recent advances, not in the introductory positioning paragraph. Output B requires less structural reorganization of the manuscript and better serves the survey's expository arc.

文献片段是输入材料；出现于此不代表输出使用了它，也不代表它能够支持全部主张。原文与检索片段经过截取；页面没有展示模拟分数或模拟 PDF。