FlowingFlowing
← 返回全部开发案例
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96 / 162 · 2cf381c55ec9e312

Human Stress Assessment: A Comprehensive Review of Methods Using Wearable Sensors and Non-wearable Techniques

心理学与认知神经科学 · 2202.03033v2

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。原始盲评有 51 个 Evidence 胜出；任务校验将其中一条空白续写改归“两组均不可用”，因此公开统计为 50 个。普通组另有一条双方空白，已从“未分出胜负”改归“两组均不可用”。这些原始判定仍可在案例页查看。

50Evidence 版本更优
÷
81该层全部点位
=
62%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 21 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

Human Stress Assessment: A Comprehensive Review of Methods Using Wearable Sensors and Non-wearable Techniques

6 Future Directions

…the lab environment or real-life scenario, we don’t have the ground truth and the majority of the studies in the literature have to rely on subjective scoring which can vary from person to person. It has been observed that physiological data from two participants may have the same pattern but one of the participant’s labeled as ”stressed” whereas the other is labeled as ”non-stressed” in subjective labeling ( Liapis et al., 2015 ) . This type of labeling ambiguity degrades the performance of the system and thus needs to be rectified by developing a more robust mechanism for labeling the data.

…all the devices whether they are EEG headsets, skin conductance, and heart rate measurement modules or smartwatches all have limited battery life and can last approximately around 4-5 hours. But to record the activities of a user for a complete day, the power consumption of the devices needs to be kept at a minimum level so that the battery can work for more time. Existing literature has not examined this power consumption factor and needs to be addressed to be able to manufacture efficient and long-lasting data acquisition devices for monitoring stress, especially for real-life environments.

↓ 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

Battery life remains a critical constraint for continuous stress monitoring in real-life settings, as the wearable devices used for physiological data acquisition typically operate for only a few hours on a single charge. Addressing power consumption is therefore essential for enabling all-day monitoring outside controlled laboratory conditions.

提供给 Evidence 版本的文献片段

Optimised Convolutional Neural Networks for Heart Rate Estimation and Human Activity Recognition in Wrist Worn Sensing Applications ↗

The battery life of smartwatches and fitness trackers vary greatly depending on the features and functionality available on-board the wearable. The Apple Watch Series 5, which is more of a lifestyle and fitness tracker, can run for a period of up to 18 hours whereas the Fitbit Charge 3 fitness track…

展开完整文献摘录

The battery life of smartwatches and fitness trackers vary greatly depending on the features and functionality available on-board the wearable. The Apple Watch Series 5, which is more of a lifestyle and fitness tracker, can run for a period of up to 18 hours whereas the Fitbit Charge 3 fitness tracker can go for up to 7 days on a single charge. Continuous activity and heart rate monitoring speed up the depletion of the battery of most wearables. Gathering and processing of simultaneous sensor data can further increase the power consumption of the devices.

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

Privacy and ethical considerations also require attention before wearable stress monitoring can be deployed at scale in real-life settings. Continuous collection of physiological signals such as EEG, skin conductance, and heart rate raises concerns about data ownership, consent, and the potential misuse of sensitive health information, and these concerns are compounded when data are streamed or stored on cloud platforms.

评审结论与任务校验

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）

原始理由中的 Output A = 无 Evidence，Output B = Evidence。

Output B is grounded in the draft and source. The draft explicitly discusses limited battery life (4-5 hours), the need for all-day recording, and that existing literature has not examined power consumption. The source supports that continuous activity and heart rate monitoring speeds up battery depletion and simultaneous sensor data increases power consumption. Output B directly continues this thread with no unsupported claims. Output A introduces an entirely new topic—privacy and ethical considerations about data ownership, consent, cloud platforms—that has zero support in the draft or supplied source. This is a material factual claim about concerns, risks, and deployment barriers that are not mentioned anywhere in the provided materials. The draft discusses labeling ambiguity and power consumption; the source discusses battery life and power consumption. Privacy/ethical issues are completely absent, making Output A an unsupported claim that changes the manuscript's scope and introduces unsourced material.

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。