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Development case · blind model judgment pending independent human review; not a formal Benchmark conclusion.
AutocompleteBoth unusable128 / 162 · fc83e161dbc148fb

Predictive Coding: a Theoretical and Experimental Review

Psychology and cognitive neuroscience · 2107.12979v4

FLOWING EVIDENCE BENCHMARK

How do we tell whether Evidence helps?

At the same writing position, with the same model and task, how does supplying retrieved paper passages change the first output? We compare matched versions and retain ties, unusable outputs and incomplete reviews.

SAME DRAFT · EVIDENCE ON OR OFF

01 · FIXED WRITING POSITION

MANUSCRIPT

Same writing position ▌

Matched autocomplete at the same draft position

02 · TWO MATCHED INPUTS

SHARED BY BOTH

Manuscript context, model, task and prompt

A · With Evidence

Retrieved paper passages supplied

B · Without Evidence

No retrieved passages supplied

LLM

Same model and version

A → first output

B → first output

Blind judge agent

First outputs are anonymized as X and Y

Continuation review: accuracy, fit to the writing task and usability
Returns: X preferred / tie / Y preferred / both unusable

Order check: X / Y → Y / X

HOW DOES BLIND JUDGING WORK?

① Anonymize both outputs
The judge sees the same draft and both first outputs without knowing which received Evidence.

② Compare and swap order
The judge applies task-specific criteria in X/Y and then Y/X order.

③ Review disagreements
A third pass resolves disagreements. Incomplete reviews remain in the denominator.

How are autocomplete positions stratified?

Before seeing generation outcomes, we check whether a retrieved passage contains a specific proposition that directly supports the next writing move. Those positions appear in the left opportunity group; the rest are ordinary positions on the right. We select a balanced sample from admitted papers in each field. The 50/50 split is experimental, not a measure of how often either type occurs in writing.

AUTOCOMPLETE · 110

Biology, statistics and astrophysics

55 positions on each side; statistics uses the ten-paper rerun.

AUTOCOMPLETE · 52

Psychology and climate science

26 positions on each side; psychology includes nine papers and climate science four.

How are the table percentages calculated?

Across five fields there are 81 source-opportunity positions. The original blind review preferred Evidence in 51; a task check moved one empty Evidence continuation to both unusable, leaving 50 in public counts. In the ordinary group, another pair of empty outputs moved from no winner to both unusable. Original verdicts remain visible on case pages.

50Evidence version preferred
÷
81All positions in this group
=
62%Evidence preference in this group

Source contribution is a separate review: 16 of 21 Evidence wins entered into source review directly used retrieved papers; another 29 wins await review.

These are development-stage model judgments pending independent human review. They are not formal Benchmark conclusions and do not, on their own, establish causality.

Manuscript writing position

Text excerpt · not a PDF page

Research manuscript · excerpt

Predictive Coding: a Theoretical and Experimental Review

5 Discussion and Future Directions

…ge-scale simulations of predictive coding networks with simultaneous learning has not yet been done. Finally, the implementation of precision in the brain remains largely mysterious. There are competing theories of precision being implemented through lateral connectivity ( K. Friston, (2005) ) , feedback superficial-to-superficial connectivity ( Shipp, (2016) ) , or primarily through subcortical processes and regions such as the pulvinar ( Kanai et al., (2015) ) . However, to our knowledge, there has been no real explicit experimental tests of the differing predictions made by these theories.

…orithm. Finally, while there has been considerable progress in predictive coding networks setup for the machine learning paradigm of hierarchical static image classification, there has been considerably little work experimenting with potentially more brain-like paradigm such as spatial predictive coding between pixels in an image, or temporal predictive coding over sequences. Indeed, there has not yet even been any large scale study of the performance characteristics of generalized coordinates on spatio-temporal video data, so precisely how effective they are in such settings remains unknown.

↓ Next paragraph was generated here

The highlighted text is near the writing position. This is a layout of the evaluation input, not a PDF screenshot.

A matched comparison at the same writing positionJudgment: Both unusable

Both arms use the same model, task and manuscript context. Only the retrieved passages differ. The first recorded output from each arm is shown below.

Evidence

Same model and position · retrieved passages supplied · first output

Judged unusable

The product produced no insertable continuation text.

The model explicitly returned a no-completion marker.

Passages supplied to the Evidence version

Predictive Coding Can Do Exact Backpropagation on Convolutional and Recurrent Neural Networks ↗

Predictive coding networks (PCNs) are an influential model for information processing in the brain. They have appealing theoretical interpretations and offer a single mechanism that accounts for diverse perceptual phenomena of the brain. On the other hand, backpropagation ( BP ) is commonly regarded…

Read full passage excerpt

Predictive coding networks (PCNs) are an influential model for information processing in the brain. They have appealing theoretical interpretations and offer a single mechanism that accounts for diverse perceptual phenomena of the brain. On the other hand, backpropagation ( BP ) is commonly regarded to be the most successful learning method in modern machine learning. Thus, it is exciting that recent work formulates inference learning ( IL ) that trains PCNs to approximate BP. However, there are several remaining critical issues: (i) IL is an approximation to BP with unrealistic/non-trivial requirements, (ii) IL approximates BP in single-step weight updates; whether it leads to the same point as BP after the weight updates are conducted for more steps is unknown, and (iii) IL is computationally significantly more costly than BP.

No Evidence

Same model and position · no retrieved passages · first output

Judged unusable

The product produced no insertable continuation text.

The model returned an empty text field.

Judgment and task check

Public counts mark both outputs unusable; an empty continuation cannot complete this task.

Read the original model judgment

In this rationale, Output A = No Evidence; Output B = Evidence.

Both outputs return empty text with action 'complete', failing to produce the requested autocomplete paragraph. The task explicitly requires continuing the manuscript with 'one coherent academic paragraph' in the blank paragraph. The draft ends with an open research gap about generalized coordinates on spatio-temporal video data, and the provided source discusses predictive coding networks, backpropagation, and inference learning with specific limitations (approximation issues, single-step weight updates, computational cost). A usable autocomplete should have leveraged this source to extend the discussion—perhaps connecting the computational challenges of inference learning to the need for large-scale spatio-temporal studies, or noting how the approximation limitations of IL relate to the unknown effectiveness of generalized coordinates. Both outputs completely abdicate the task by returning empty strings, making them equally unusable.

The passages are input material; their presence does not mean an output used them or that they support every claim. Draft and source passages are excerpted; no simulated scores or PDF appear here.