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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3 / 162 · ce22995352c0cdc1

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 > 5.2 Level of detail

…sociating rule of behavior with a specific hierarchy is possible once we have sufficient familiarity with the biology and predictions of basic models. This can help to bridge hierarchies and avoid the fog of complexity. Mathematical methods such as dynamical systems, PDEs, and bifurcation analysis can help to find and account for emergent properties and universal principles in such basic models [ 48 ] . This is one of the strengths of the mathematical tools. A weakness is that these methods currently work well for small systems of differential equations, but not for large and complex systems.

…perimental observations of small cell groups provide good opportunities for understanding how to bridge from single to collective behavior. Modeling can then also explore the advantages of group migration, chemotaxis [ 18 , 103 ] , durotaxis [ 165 ] , etc., in larger groups. For a large enough number of cells, condensing the details into simpler rules becomes expedient. For example, while polarity is represented by PDEs and patterns inside a single cell, it can then be simplified, depicted by a direction vector [ 25 , 15 ] in place of a full internal gradient of Rho or Rac for multiple cells.

↓ 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 level of detail required in a model therefore depends on the question being asked rather than on the size of the system alone. A model that resolves intracellular polarity dynamics may be necessary to explain how a single cell reorients, whereas a direction-vector representation may suffice to capture the collective behavior of a large group.

提供给 Evidence 版本的文献片段

Spontaneous Spatiotemporal Ordering of Shape Oscillations Enhances Cell Migration

Here we demonstrate, using numerical simulations, that the interplay of directed motion, shape oscillations, and excluded volume enables cells to locally “synchronize” their motion and thus enhance collective migration. Our model captures elongation and contraction of crawling ameboid cells controll…

展开完整文献摘录

Here we demonstrate, using numerical simulations, that the interplay of directed motion, shape oscillations, and excluded volume enables cells to locally “synchronize” their motion and thus enhance collective migration. Our model captures elongation and contraction of crawling ameboid cells controlled by an internal clock with a fixed period, mimicking the internal cycle of biological cells. We show that shape oscillations are crucial for local rearrangements that induce ordering of internal clocks between neighboring cells even in the absence of signaling and regularization. Our findings reveal a novel, purely physical mechanism through which the internal dynamics of cells influences their collective behavior, which is distinct from well known mechanisms like chemotaxis, cell division, and cell-cell adhesion.

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

A related limitation concerns the level of detail at which cell-cell interactions are represented. Detailed models of contact mechanics and receptor-mediated adhesion capture the behavior of small groups, but the computational cost of resolving these interactions grows rapidly with cell number, which motivates the simplified interaction rules used in larger-scale collective models.

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 builds coherently from the draft's contrast between detailed PDE-based single-cell models and simplified direction-vector representations for multiple cells, extending this to the general principle that model detail depends on the research question. This is grounded in the draft's explicit discussion of simplifying polarity from PDEs to direction vectors [25, 15] for multiple cells. Output B introduces an unsupported claim about 'contact mechanics and receptor-mediated adhesion' and their 'computational cost' growing with cell number — neither contact mechanics, receptor-mediated adhesion, nor computational cost scaling are mentioned in the draft or source. The source discusses shape oscillations and excluded volume as a physical mechanism for collective migration, not contact mechanics or adhesion costs. Output B's claim about 'simplified interaction rules used in larger-scale collective models' also goes beyond the draft's specific point about direction vectors replacing PDEs for polarity.

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。