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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26 / 162 · d0b94997da8dc006

A Review of Link Prediction Applications in Network Biology

定量生物学 · 2312.01275v1

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 Review of Link Prediction Applications in Network Biology

6. Challenges and Future Directions

…tent predictive capability over time. Weighted Collapsed Tensor (WCT) models exhibit increased AUC with higher weights, particularly notable in the DPPIN-Babu network, suggesting a stronger alignment with the network’s temporal dynamics. In contrast, the Jaccard and Non-negative Matrix Factorization (NMF) models consistently show lower AUC scores across all networks, implying limited effectiveness in temporal link prediction. Overall, WCT models emerge as the most effective, balancing robustness and adaptability to temporal changes. We note that different weights show no impact on the metric.

The time analysis of link prediction models (see Figs. 11 b, 11 d, 11 f) on temporal networks indicates that the Collapsed Tensor (CT) method maintains consistent and low computational time across all networks, highlighting its efficiency. The Jaccard and Non-negative Matrix Factorization (NMF) methods exhibit sporadic spikes in computational time, suggesting variability in processing depending on the network’s state at different time points. Overall, the CT method stands out for its temporal efficiency in link prediction tasks across the examined networks.

↓ 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。

模型动作：complete

提供给 Evidence 版本的文献片段

Rank Selection for Non-negative Matrix Factorization

Non-Negative Matrix Factorization (NMF) is a widely used dimension reduction method that factorizes a non-negative data matrix into two lower dimensional non-negative matrices: One is the basis or feature matrix which consists of the variables and the other is the coefficients matrix which is the pr…

展开完整文献摘录

Non-Negative Matrix Factorization (NMF) is a widely used dimension reduction method that factorizes a non-negative data matrix into two lower dimensional non-negative matrices: One is the basis or feature matrix which consists of the variables and the other is the coefficients matrix which is the projections of data points to the new basis.

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

未输出续写正文。

模型动作：complete

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）

Both outputs A and B are empty strings with action='complete'. The task requires continuing the manuscript with one coherent academic paragraph. The draft discusses computational time analysis of link prediction models (CT, Jaccard, NMF) and ends with CT's temporal efficiency. A valid continuation would need to groundedly extend this discussion—perhaps comparing CT's efficiency to WCT models mentioned earlier, discussing implications for scalable temporal network analysis, or noting trade-offs between predictive performance (where WCT excelled) and computational efficiency (where CT excels). The empty outputs fail to provide any continuation content, making them unusable by the explicit rule: 'Mark an output unusable if it is empty when the task clearly needs content.' The single source provided only gives generic NMF background and does not directly support specific claims about temporal efficiency or computational complexity, but a grounded continuation could still work from the draft's established claims without needing additional source support.

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。