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Development case · blind model judgment pending independent human review; not a formal Benchmark conclusion.
AutocompleteBoth unusable72 / 162 · 59f81061d4498311

Introduction to Generalized Fiducial Inference

Statistics · 2302.14598v1

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

Introduction to Generalized Fiducial Inference

1 Introduction

… component U U with a known distribution F 0 F_{0} , e.g. a vector of independent and identically distributed (iid) standard uniforms or standard Gaussians. This is often expressed as the relation Y = A ⁡ ( U , θ ) \textbf{Y}=A(U,\theta) . Since the DGA is a function of a random variable with a known distribution, it immediately determines the likelihood f ⁡ ( 𝐲 | θ ) f(\mathbf{y}|\theta) . By solving the DGA for θ \theta , we get the distribution of our parameters, called the generalized fiducial distribution (GFD), that is entirely data-driven and does not require the use of Bayes’ Theorem.

…2013 ; Wang and Iyer, 2006a ; Wang and Iyer, 2006b ; Hannig et al., 2007 ; Wang et al., 2012 ) , inter-laboratory experiments and international key comparison experiments ( Iyer et al., 2004 ) . It has also been used to tackle statistical problems at the forefront of modern topics in statistical research, such as in wavelet regression ( Hannig and Lee, 2009 ) and extreme value estimation ( Wandler and Hannig, 2012a ) , and has recently led to a creative new perspective on linear model selection ( Williams and Hannig, 2019 ) and vector autoregressive graph selection ( Williams et al., 2019 ) .

↓ 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

Statistical Estimation of High-Dimensional Vector Autoregressive Models ↗

High-dimensional vector autoregressive (VAR) models are important tools for the analysis of multivariate time series. This paper focuses on high-dimensional time series and on the different regularized estimation procedures proposed for fitting sparse VAR models to such time series. Attention is pai…

Read full passage excerpt

High-dimensional vector autoregressive (VAR) models are important tools for the analysis of multivariate time series. This paper focuses on high-dimensional time series and on the different regularized estimation procedures proposed for fitting sparse VAR models to such time series. Attention is paid to the different sparsity assumptions imposed on the VAR parameters and how these sparsity assumptions are related to the particular consistency properties of the estimators established. A sparsity scheme for high-dimensional VAR models is proposed which is found to be more appropriate for the time series setting considered.

No Evidence

Same model and position · no retrieved passages · first output

Judged unusable

The product produced no insertable continuation text.

The model explicitly returned a no-completion marker.

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' despite the task requiring a coherent academic paragraph to continue the manuscript. The blank paragraph occurs between two substantive sections: the first defines DGA (data generating algorithm), GFD (generalized fiducial distribution), and fiducial inference fundamentals; the second lists applications and citations of fiducial methods. The empty outputs fail to bridge these sections with any transition, explanation, or continuation. The task explicitly asks to 'continue the manuscript in the blank paragraph with one coherent academic paragraph.' An empty response cannot satisfy this requirement. The source provided discusses high-dimensional VAR models and sparsity, which has thematic connection to the 'vector autoregressive graph selection' mentioned in the draft's second paragraph, but neither output attempts to use this or any other grounded material to create the required paragraph. Both outputs are empty when content is clearly needed, making both 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.