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Development case · blind model judgment pending independent human review; not a formal Benchmark conclusion.
AutocompleteNo-Evidence version preferred1 / 162 · 2c19f4cf93463f45

Bridging from single to collective cell migration: A review of models and links to experiments

Quantitative biology · 2011.10873v1

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, 51 of 81 left-column positions preferred the Evidence version. Ties, pairs where both versions were unusable and incomplete reviews remain in the denominator.

51Evidence version preferred
÷
81All positions in this group
=
63%Evidence preference in this group

Source contribution is a separate review: 16 of 22 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

Bridging from single to collective cell migration: A review of models and links to experiments

1 Introduction

In this review paper, we summarize the literature on computational models for cell motility, from the biochemical networks that regulate it, to the behavior of 1 and many cells. We discuss the distinct approaches used at each level, and how models can build bridges between the different size scales. We find models at many different levels of biological detail, and discuss their relative contributions to our understanding of single and collective cell behavior. Finally, we indicate how models have been linked to biological experiments in this field.

Over several decades, there has been great progress in our understanding of cell motility. In the 1980s and 1990s, the basic machinery of eukaryotic cell motion and the role of the actin cytoskeleton were discovered and refined. Regulation of motility by intracellular signaling networks was then deciphered in the late 1990s and through the 2000s. We continue to discover links between cell signaling and cell shape and function, in both normal and diseased cells. Recent efforts aim to link single cell behavior to collective behavior of many cells and emergent dynamics of tissues.

↓ 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: No-Evidence version preferred

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

Not preferred in this comparison

No continuation text was produced.

The model requested more evidence and did not continue.

Passages supplied to the Evidence version

Collective motion of cells crawling on a substrate: roles of cell shape and contact inhibition

At the level of a single cell, it is well established that its motion is intricately linked to its shape. The shape of crawling cells is highly variable, depending on the type of cell, the substrate, as well as a result of the migration process itself [ 5 , 6 , 7 , 8 ] . When a cell starts moving, i…

Read full passage excerpt

At the level of a single cell, it is well established that its motion is intricately linked to its shape. The shape of crawling cells is highly variable, depending on the type of cell, the substrate, as well as a result of the migration process itself [ 5 , 6 , 7 , 8 ] . When a cell starts moving, its shape breaks symmetry [ 6 ] , whereas circular cells typically cannot move. While there is evidence that shape has a strong influence on scattering and can lead to clustering and collective directed motion of swimmers [ 9 , 10 ] , less is known about the role of cell shape in organizing collective crawling. It has been shown in simulations that inelastic collisions between crawling cells, e.g. due to deformation, can lead to coherent migration [ 11 , 12 , 13 , 14 ] , suggesting the importance of deformability for collective behavior.

No Evidence

Same model and position · no retrieved passages · first output

Preferred by blind review

This review focuses on the computational models that have been developed to describe cell motility at these different scales, and on how those models connect to experimental measurements. We organize the discussion from subcellular biochemistry through single-cell migration to collective and tissue-level behavior, highlighting where models bridge between scales and where gaps remain.

Blind model judgment

The blinded model review preferred the no-Evidence version. Its original rationale appears below.

Read the original model judgment

Output A provides a coherent, usable continuation paragraph that fits the manuscript's structure and scope. It naturally bridges from the historical overview in the draft to the review's organizational framework, maintaining the paper's focus on computational models across scales. The content is supported by the draft's stated purpose (summarizing computational models for cell motility at different levels) and does not introduce unsupported factual claims. Output B returns an empty result with 'needs_evidence' action, which is unusable for an autocomplete task that clearly requires content continuation. The blank paragraph needs to be filled, and B fails to do so.

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.