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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Evidence 更优20 / 162 · 525faa866ad5b626

Generative AI for Controllable Protein Sequence Design: A Survey

定量生物学 · 2402.10516v1

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

Generative AI for Controllable Protein Sequence Design: A Survey

1 Introduction

…t of the protein design space, lay the groundwork for sequence-based protein design. Deep generative models offer a promising tool for capturing the distribution underlying natural protein sequences to generate novel and diverse proteins. Moreover, making protein design controllable is crucial for the transition from theory to practice. Ideally, a practical method ought to generate proteins that adhere to task-specific structural and functional constraints. To this end, researchers have utilized conditional generative models and optimization algorithms to realize such tailored protein design.

…cts of the literature in terms of methodologies and tasks. For instance, compared to de novo design, the equally important theme of mutation-based optimization tends to be overlooked. In contrast, our survey endeavors to bridge this gap. We seek to demystify protein sequence design for machine learning researchers by systematically categorizing various controllable design tasks according to the constraints involved. We hope this survey reaches both the machine learning and biochemistry fields and inspires further collaboration towards developing more sophisticated and controllable algorithms.

↓ 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：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

盲评更优

Controllability in protein sequence design is typically pursued through two complementary strategies: conditional generative models that steer sampling toward a specified property, and optimization procedures that search the latent or sequence space for high-fitness candidates. Both strategies rely on a learned representation of the sequence distribution, which determines how readily the design space can be explored under task-specific constraints.

提供给 Evidence 版本的文献片段

Importance Weighted Expectation-Maximization for Protein Sequence Design

In this paper, we propose IsEM-Pro, an approach to generate protein sequences towards a given fitness criterion. At its core, IsEM-Pro is a latent generative model, augmented by combinatorial structure features from a separately learned Markov random fields (MRFs). We develop an Monte Carlo Expectat…

展开完整文献摘录

In this paper, we propose IsEM-Pro, an approach to generate protein sequences towards a given fitness criterion. At its core, IsEM-Pro is a latent generative model, augmented by combinatorial structure features from a separately learned Markov random fields (MRFs). We develop an Monte Carlo Expectation-Maximization method (MCEM) to learn the model. During inference, sampling from its latent space enhances diversity while its MRFs features guide the exploration in high fitness regions. Experiments on eight protein sequence design tasks show that our IsEM-Pro outperforms the previous best methods by at least 55% on average fitness score and generates more diverse and novel protein sequences.

无 Evidence

同一模型与写作位置 · 不提供检索文献片段 · 首次输出

本次未获偏好

未输出续写正文。

模型动作：complete

模型盲评结论

模型盲评判定“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）

Output A provides a coherent academic paragraph that continues the manuscript's discussion of controllable protein design, bridging from the general introduction to the survey's scope. It is grounded in the draft's themes of conditional generative models and optimization algorithms for task-specific constraints, without introducing unsupported claims or citation markers. Output B is empty despite the task clearly requiring content for the blank paragraph, making it unusable.

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。