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Development case · blind model judgment pending independent human review; not a formal Benchmark conclusion.
AutocompleteJudgment incomplete16 / 162 · 17c04c10cafd0d5e

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

5 Conclusion and Future Directions > 5.3 Non-autoregressive Modeling

…xplored. 3D position vectors provide important complementary distance information for each pair of atoms. The relative pairwise distance can be very different in 3D Euclidean geometry compared to 2D molecular graphs. For example, atom v 1 v_{1} and atom v 2 v_{2} may be distant from each other in a non-euclidean molecular graph while they might be close to each other in 3D Euclidean space. This is particularly useful for reaction center ranking. Therefore, effectively incorporating 3D molecular information into modeling can facilitate the more accurate reaction and retrosynthesis predictions.

…as scaffold split and time split, should be included for cross-validation. For retrosynthesis prediction, the USPTO-50K dataset is at small-scale. A new benchmark dataset should be a large-scale dataset containing at least 100K samples. Furthermore, future benchmark datasets should contain more target molecules with multiple edits. In addition, a new evaluation metric for retrosynthesis is necessary and urgent. FusionRetro Liu et al. 2023 attempts to evaluate single-step retrosynthesis models in the context of multi-step planning. More diverse evaluation metrics can be designed in the future.

↓ 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: Judgment incomplete

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

Non-autoregressive modeling offers a promising alternative to the sequential decoding paradigm that dominates reaction and retrosynthesis prediction. By generating the reaction outcome or precursor set in a single pass rather than token by token, such approaches can avoid the exposure bias and error accumulation inherent to autoregressive decoders, while permitting parallel prediction of multiple reaction centers. Realizing these benefits, however, requires architectures that can capture the joint dependencies among simultaneously predicted outputs, which remains an open challenge for the field.

Passages supplied to the Evidence version

Metro: Memory-Enhanced Transformer for Retrosynthetic Planning via Reaction Tree

These approaches have the following limitations: 1) the training dataset of single-step reactions limits the understanding of the transformation of organic molecules as a sequence of chaining chemical reactions. 2) past works use single-step retrosynthesis models, which neglect the context informati…

Read full passage excerpt

These approaches have the following limitations: 1) the training dataset of single-step reactions limits the understanding of the transformation of organic molecules as a sequence of chaining chemical reactions. 2) past works use single-step retrosynthesis models, which neglect the context information in the reaction tree. 3) the test set is too small to comprehensively evaluate the performance. 4) the evaluation unit of existing benchmark is the reaction route which is one path from the root node to the leaf node in the reaction tree.

No Evidence

Same model and position · no retrieved passages · first output

Non-autoregressive modeling offers a promising alternative to the sequential decoding paradigm that dominates current reaction and retrosynthesis prediction. By generating the complete set of reaction edits in a single pass rather than one token at a time, such approaches can avoid the error accumulation and inference latency inherent to autoregressive decoding, and they align naturally with the graph-structured nature of chemical reactions.

Blind model judgment

The judgment was incomplete, so this case has no winner but remains in the denominator above.

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.