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Development case · blind model judgment pending independent human review; not a formal Benchmark conclusion.
AutocompleteEvidence version preferred14 / 162 · 34589a6b8456e007

A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challenges

Quantitative biology · 2306.15890v1

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

A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challenges

4 Limitations and Challenges

(1) Non-autoregressive models enable parallel sampling, which result in much faster sampling speed compared to autoregressive ones. (2) Non-autoregressive models have already achieved the best class of top-1 accuracy, which demonstrates that the non-autoregressive decoder is very powerful for reaction modeling. (3) Non-autoregressive models do not need pre-defined generation order.

(1) The uncertainty modeling of non-autoregressive models is very tricky. The top-k sampling process is not as natural as beam search in autoregressive modeling. (2) Non-autoregressive models rely on the atom-mapping information while this also requires additional algorithms for alignment. The illustration of NERF learning mechanism is shown in Figure 3 .

↓ 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: 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

Preferred by blind review

These limitations suggest that the choice between autoregressive and non-autoregressive decoders is not a simple matter of accuracy, but involves trade-offs among sampling speed, uncertainty modeling, and dependence on atom-mapping information.

Passages supplied to the Evidence version

Hippocampus-Inspired Cognitive Architecture (HICA) for Operant Conditioning

We propose a Hippocampus-Inspired Cognitive Architecture (HICA) as a neural mechanism for operant conditioning. HICA explains a learning mechanism in which agents can learn a new behavior policy in a few trials, as mammals do in operant conditioning experiments.

No Evidence

Same model and position · no retrieved passages · first output

Not preferred in this comparison

Beyond these architectural trade-offs, the field also lacks a unified benchmark that evaluates autoregressive and non-autoregressive models under identical atom-mapping and evaluation conditions, which makes reported accuracy comparisons difficult to interpret. Establishing such shared protocols would clarify whether the advantages attributed to either paradigm stem from the decoder design or from differences in data preprocessing and alignment quality.

Blind model judgment

The blinded model review preferred the Evidence version. Its original rationale appears below.

Read the original model judgment

Output A synthesizes only what is in the draft: sampling speed, uncertainty modeling, and atom-mapping dependence are all explicitly mentioned limitations/trade-offs. It adds no new factual claims. Output B introduces two unsupported claims: (1) 'the field also lacks a unified benchmark' — no source or draft mentions benchmarks, unified or otherwise; (2) 'which makes reported accuracy comparisons difficult to interpret' — the draft states non-autoregressive models 'have already achieved the best class of top-1 accuracy' with no suggestion this is difficult to interpret or compare. Output B also introduces 'shared protocols' and claims about 'data preprocessing and alignment quality' as sources of advantage, none of which appear in the draft or source. The source provided is about hippocampus-inspired cognitive architecture for operant conditioning, completely unrelated to either output.

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.