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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4 / 162 · cebee9387c3b261c

Bridging from single to collective cell migration: A review of models and links to experiments

定量生物学 · 2011.10873v1

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

Bridging from single to collective cell migration: A review of models and links to experiments

5 Discussion > 5.3 Model-experiment links

Biology is an experimental science at its core, and models that illuminate its mysteries must eventually meet and concur with biological evidence. Modern methods such as machine learning can help match a vast dataset with the best candidate model(s) as shown elegantly by [ 118 ] . At the same time, modelers, physicists, and biophysicists can make real contributions by using their craft and theories to unlock important facts that are not at all apparent otherwise [ 42 ] .

Several of our examples demonstrate that some experimental papers have explicitly addressed signaling pathways that mediate cell–cell communication in collective cell behavior. As pointed out eloquently by a reviewer of this paper, “experiments can only tell [us … what are] upstream and downstream regulators. We need mathematical models to incorporate this information with the intracellular networks to form a signaling network on a multicellular level to study how a group of cells processes signals collaboratively.”

↓ 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

The reviewer's point identifies a division of labor rather than a gap in either approach: experiments establish which regulators act upstream and downstream of a signal, while models are needed to embed that information in an intracellular network and ask how a group of cells processes signals collaboratively. Network control frameworks illustrate how such intracellular information can be used to predict intervention targets, as shown for a leukemia signaling network and for the network controlling helper T cell differentiation.

提供给 Evidence 版本的文献片段

Cell fate reprogramming by control of intracellular network dynamics

Here we develop a novel network control framework that integrates the structural and functional information available for intracellular networks to predict control targets. Formulated in a logical dynamic scheme, our approach drives any initial state to the target state with 100% effectiveness and n…

展开完整文献摘录

Here we develop a novel network control framework that integrates the structural and functional information available for intracellular networks to predict control targets. Formulated in a logical dynamic scheme, our approach drives any initial state to the target state with 100% effectiveness and needs to be applied only transiently for the network to reach and stay in the desired state. We illustrate our method’s potential to find intervention targets for cancer treatment and cell differentiation by applying it to a leukemia signaling network and to the network controlling the differentiation of helper T cells. We find that the predicted control targets are effective in a broad dynamic framework. Moreover, several of the predicted interventions are supported by experiments.

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, usable continuation that directly addresses the reviewer's quoted point about the complementary roles of experiments and models in studying collective cell signaling. It grounds its claims in the supplied source about network control frameworks for intracellular networks, specifically mentioning the leukemia signaling network and helper T cell differentiation examples from the source—matching entity, relation, and scope without overclaiming. The paragraph maintains academic tone and flows logically from the draft's discussion of experiments identifying upstream/downstream regulators to the need for models to integrate this information multicellularly. Output B is empty when the task clearly requires content continuation; a blank paragraph cannot be inserted as a coherent academic paragraph.

来源复核确认：这段续写直接采用了检索论文中可验证的具体信息。

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。