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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11 / 162 · 27f2ba81533c87f2

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

…ic plasticity and other basic functions ( Turrigiano (1999) ; Watt & Desai (2010) ) , or else could be implementing some kind of layer-wise normalization – which should theoretically be describable as predictive coding – but in general the exact function of this connectivity at a computational and algorithmic level is unknown both within and outside of predictive coding, as is the interaction between top-down and lateral connectivity. As such, it may be that predictive coding might be ‘right’ in some sense, but still missing core aspects of the computation that actually goes on in the cortex.

…ke architecture for action. However, it is also possible that the highest and most abstract levels of the brain may be primarily discrete, necessitating discrete generative models for their mathematical description which would entail a departure from predictive coding. Similarly, adaptive action in biological organisms requires long-term planning and maximization of future reward signals, which cannot readily be implemented within the predictive coding paradigm but instead likely relies on a complex set of machinery specialised for performing reinforcement learning ( Sutton & Barto (2018) ) .

↓ 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 relationship between predictive coding and the exploratory behaviour that supports adaptive action. If exploration is driven by minimizing a divergence between desired and predicted states rather than by maximizing an evidence bound, then the objective functional underlying predictive coding may not by itself account for information-seeking behaviour, and the two frameworks would need to be reconciled explicitly.

提供给 Evidence 版本的文献片段

Applications of the Free Energy Principle to Machine Learning and Neuroscience

We saw that to obtain information-seeking exploration as a core part of the objective functional, in addition to reward maximization crucially entails minimizing a divergence objective instead of an evidence objective. We then related this new dichotomy between divergence and evidence objectives to…

展开完整文献摘录

We saw that to obtain information-seeking exploration as a core part of the objective functional, in addition to reward maximization crucially entails minimizing a divergence objective instead of an evidence objective. We then related this new dichotomy between divergence and evidence objectives to a wide range of currently used objectives within the reinforcement learning and theoretical neuroscience communities. The importance of this result, really, lies not in the relationship to existing methods, but what it tells us about the deep foundation of exploration. Put simply, we see that extrinsic exploratory drives emerge from trying to match rather than maximize. Matching tries to maintain the complexity of the inputs, so that given a complex desire distribution, agents are driven to stabilize a similarly complex future.

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 further open question concerns the relationship between predictive coding and the brain's reward and value systems. While predictive coding accounts for perceptual inference and learning, it does not by itself specify how value is represented or how actions are selected to maximise future reward, and bridging this gap remains an active area of theoretical work.

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）

Both outputs are coherent continuations, but A is more tightly grounded in the supplied source and better extends the draft's specific thread. The draft ends with predictive coding's limitations regarding 'adaptive action,' 'long-term planning,' and 'maximization of future reward signals' in reinforcement learning. Output A directly engages with the source's core claim about 'divergence objective instead of an evidence objective' and 'information-seeking exploration,' connecting this explicitly to predictive coding's potential inadequacy for exploration. This matches the source's specific mechanism (divergence vs. evidence) and the draft's concern about reinforcement learning machinery. Output B is more generic—it states predictive coding doesn't 'specify how value is represented or how actions are selected,' which is true to the draft but adds no new source-grounded content. The source specifically discusses divergence objectives and exploration, not generic value representation. Output A uses this specific source contribution to advance the argument, making it more useful for a researcher who could insert it with minimal editing while preserving and extending the draft's meaning.

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。