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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27 / 162 · e21a23f73a3fce55

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 > 6.2. Interpretability

…wn interactions are used to train the models before reporting unknown associations ( muzio2021biological ) . Thus, the missing associations are likely to create bias in the networks and have an adverse effect on the subsequent predictions. The fact that the networks are not complete precludes us from deriving inferences from their known topological properties. For instance, the knowledge that signaling, transcriptional, and protein networks are scale-free ( barabasi2004network ; albert2005scale ) does not warrant that the sampled subnetworks are scale-free as well ( heath2009computational ) .

…ure symmetry and inversions. Along the same lines, the embedding strategy needs to be relevant in the context of a biological question. If nodes are embedded based on local network topology, then the prediction cannot be expected to capture global features. Biological information, such as the structure of proteins and protein complexes, the layout of signaling pathways, or the higher-order organization of organelles, etc. are likely to be lost ( ideker2017network ) . Moreover, the noise and sparseness in the biomedical datasets diminish the predictive capabilities of the embedding approaches.

↓ 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

Higher-order representations offer one route to this end: hypergraphlets applied to hypernetworks in which nodes are proteins and hyperedges encode protein–protein interactions, complexes, or signaling pathways can test whether such topology carries biological information beyond that recoverable from pairwise PPI networks.

提供给 Evidence 版本的文献片段

Higher order molecular organisation as a source of biological function

We investigate biological hypernetworks in which nodes are proteins and hyperedges capture PPIs, protein complexes, or signaling pathways. The main aim is to check if the topology of these hypernetwork representations of the data carries biological information that goes beyond the information that c…

展开完整文献摘录

We investigate biological hypernetworks in which nodes are proteins and hyperedges capture PPIs, protein complexes, or signaling pathways. The main aim is to check if the topology of these hypernetwork representations of the data carries biological information that goes beyond the information that can be obtained from PPI networks. We use hypergraphlets in this investigation.

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 is empty when the task clearly requires content—a coherent academic paragraph to continue the manuscript. This makes it unusable. Output B provides a single coherent academic paragraph that directly draws on the supplied source about hypergraphlets and hypernetworks, connecting to the draft's themes of network embedding limitations and biological information loss. The paragraph is source-grounded: it uses the exact entities (hypergraphlets, hypernetworks, proteins, hyperedges, PPIs, protein complexes, signaling pathways) and purpose (testing whether topology carries biological information beyond pairwise PPI networks) from the source snippet. It naturally extends the draft's argument that local topology-based embeddings lose biological information, by proposing higher-order representations as a potential solution. No unsupported claims are introduced; the source's investigational framing ('can test whether') is preserved rather than overstated as a finding.

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。