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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。
自动补全两组均不可用19 / 162 · b5f36b40ee124f25

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

…ns, designing novel amino acid sequences that encode proteins with desired functions has been a central challenge in bioengineering. Nevertheless, due to the vast exploration space of possible proteins, conventional experimental methods, such as directed evolution Arnold 1998 , are prohibitively time-intensive and money-consuming. To overcome these challenges, recent years have witnessed the trend of leveraging generative AI technologies to explore the biochemical space intelligently. Fueled by advances in generative AI, the field of protein design is experiencing an unprecedented revolution.

…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.

↓ 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：两组均不可用

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

盲评判定不可用

未输出续写正文。

模型表示需要更多证据，未生成续写。

提供给 Evidence 版本的文献片段

Benchmarking deep generative models for diverse antibody sequence design

Computational protein design, i.e. inferring novel and diverse protein sequences consistent with a given structure, remains a major unsolved challenge. Recently, deep generative models that learn from sequences alone or from sequences and structures jointly have shown impressive performance on this…

展开完整文献摘录

Computational protein design, i.e. inferring novel and diverse protein sequences consistent with a given structure, remains a major unsolved challenge. Recently, deep generative models that learn from sequences alone or from sequences and structures jointly have shown impressive performance on this task. However, those models appear limited in terms of modeling structural constraints, capturing enough sequence diversity, or both. Here we consider three recently proposed deep generative frameworks for protein design: (AR) the sequence-based autoregressive generative model, (GVP) the precise structure-based graph neural network, and Fold2Seq that leverages a fuzzy and scale-free representation of a three-dimensional fold, while enforcing structure-to-sequence (and vice versa) 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

盲评判定不可用

Despite this progress, the survey landscape remains fragmented: existing reviews tend to organize generative protein design either by model architecture or by application domain, and rarely by the control mechanisms that determine whether a generated sequence satisfies a specified constraint. This gap motivates the taxonomy adopted in this survey, which categorizes controllable protein sequence design methods according to the conditioning signals they accept and the design objectives they target.

模型盲评结论

模型盲评判定两边都不可用；这与“持平”分开统计。

查看模型评审原始理由（英文）

Output A is deliberately empty with action 'needs_evidence', which is unusable for an autocomplete task that clearly needs content. Output B provides a coherent academic paragraph that continues the manuscript's flow, transitioning from controllable protein design to a survey/taxonomy framing. However, Output B contains unsupported claims: it states that 'existing reviews tend to organize generative protein design either by model architecture or by application domain, and rarely by the control mechanisms' and introduces 'this survey' with a specific taxonomy — neither the draft nor the single supplied source mentions any survey, review organization patterns, or a taxonomy of control mechanisms. The source only benchmarks three specific deep generative models for antibody sequence design and discusses their limitations in modeling structural constraints and sequence diversity. The claim about how reviews are organized and the existence of 'this survey' is an invented framing with no source support. Despite this unsupported claim, Output B is the only output that provides usable content; Output A fails the basic requirement of providing completion text.

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。