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12 / 162 · 6f54e7abd741955a

Predictive Coding: a Theoretical and Experimental Review

定量生物学 · 2107.12979v4

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

Predictive Coding: a Theoretical and Experimental Review

5 Discussion and Future Directions

…a straightforward way to utilize such information, the brain will use it. As such it is not clear whether rate-coded theories can simply be straightforwardly ported over to more biophysically realistic spiking models or not. This is a very important area for future work since it is vital to understand the degree to which predictive coding models can function in a spiking environment, and to design bespoke predictive coding algorithms for spiking neural networks, which can be empirically investigated for their performance and robustness characteristics as well as their biological plausibility.

…ed to handle the case of colored, or correlated, noise (i.e., noise with a non-zero autocorrelation). However, generalized coordinates only provide a very short term dynamical model and memory of the system, which is potentially ideal for modelling local smooth temporal dependencies such as optical flow, but is perhaps not sufficient for modelling more abstract changes over time. Developing and scaling flexible and expressive generative models to handle these kinds of temporal dependencies in continuously varying inputs is an open challenge in both neuroscience as well as in machine learning.

↓ 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

A further open question concerns the role of memory in predictive coding. Content-addressable memories, in which stored patterns can be retrieved from partial or noisy cues, are a classic component of neural models, and recent machine learning systems have implemented differentiable addressable memory and gating by allowing weighted superpositions of memory registers to be queried. Whether the brain employs such mechanisms, and how they might be integrated with hierarchical predictive coding, remains unresolved.

提供给 Evidence 版本的文献片段

Towards an integration of deep learning and neuroscience

Content addressable memories 22 22 22 Attractor models of memory in neuroscience tend to have the property that only one memory can be accessed at a time. Yet recent machine learning systems have constructed differentiable addressable memory ( Graves et al., 2014 ) and gating ( Whitney et al., 2016…

展开完整文献摘录

Content addressable memories 22 22 22 Attractor models of memory in neuroscience tend to have the property that only one memory can be accessed at a time. Yet recent machine learning systems have constructed differentiable addressable memory ( Graves et al., 2014 ) and gating ( Whitney et al., 2016 ) systems by allowing weighted superpositions of memory registers or gates to be queried. It is unclear whether the brain uses such mechanisms. are classic models in neuroscience ( Hopfield, 1982 ) . Most simply, they allow us to recognize a situation similar to one that we have seen before, and to “fill in” stored patterns based on partial or noisy information, but they may also be put to use as sub-components of many other functions.

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, which is unusable for an autocomplete task that clearly requires content. The draft discusses open challenges in predictive coding (spiking models, temporal dependencies) and needs a coherent continuation. Output B provides a relevant, well-grounded paragraph that transitions to memory in predictive coding using the supplied source about content-addressable memories. The source explicitly mentions 'It is unclear whether the brain uses such mechanisms' regarding differentiable addressable memory, which Output B accurately paraphrases as 'Whether the brain employs such mechanisms... remains unresolved.' The paragraph maintains academic tone, connects to the draft's theme of open challenges, and does not add unsupported claims. No citation markers are generated.

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。