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Paper Citation Record · LEDGER

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision

As of 12 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 1 inbound Pith citation observation for arXiv:2412.05569.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.05569 v2

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:39:37.311685Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T16:17:32.245386Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-10T16:17:32.360164Z

Reference resolution

35 of 35 outbound references displayed

  • verified exact1
  • verified fuzzy15
  • unresolved19
  • parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ee604d48-225f-4c87-947a-04e4b82cd38b · outbound

This paper cites Molgpt: molecular generation using a transformer-decoder model.Journal of Chemical Information and Modeling, 62(9): 2064–2076,.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision Molgpt: molecular generation using a transformer-decoder model.Journal of Chemical Information and Modeling, 62(9): 2064–2076,

Reference 1

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Source-reported events for the cited work

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Observation 978ab281-01ec-4aa5-95f5-31c6047a3886 · outbound

This paper cites Table 4: Hyper-parameters for pre-train models with different scales.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision Table 4: Hyper-parameters for pre-train models with different scales

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 6650e5a4-b072-4b93-b200-e2fb9f850645 · outbound

This paper cites an unresolved cited work.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision Unresolved cited work

Reference 5

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Source-reported events for the cited work

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Observation 692603a1-4f74-4044-b050-5197839842ea · outbound

This paper cites Table 7: Summary information of the MoleculeNet benchmark datasets.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision Table 7: Summary information of the MoleculeNet benchmark datasets

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 14d35a38-3180-4bc4-b319-1678df76cf5d · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 59fd64af-95a1-4c7b-a432-74ba9646891b · outbound

This paper cites Encode, Tag, Realize: High-Precision Text Editing.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision Encode, Tag, Realize: High-Precision Text Editing

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:39:37.223947Z digest=sha256:df814e0826159cc04565947053b578ee85932b8c5ca0786c10fd80f37c93db80

Observation be0fe2af-77a6-42ce-be80-5c8b615f946a · outbound

This paper cites Compared to the performance of the MLM (Figure 9), the larger SMI-EDITORmodel (i.e., Big Model) consistently outperforms the smaller models (i.e., Small Model).

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision Compared to the performance of the MLM (Figure 9), the larger SMI-EDITORmodel (i.e., Big Model) consistently outperforms the smaller models (i.e., Small Model)

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 7a7e6bf6-2670-491a-87fe-40319577cf91 · outbound

This paper cites This indicates that SMI-EDITOR represents a more challenging training task.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision This indicates that SMI-EDITOR represents a more challenging training task

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation f39ee4c3-9b60-419c-8da2-b02008413551 · outbound

This paper cites Lexically Constrained Neural Machine Translation with Levenshtein Transformer.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision Lexically Constrained Neural Machine Translation with Levenshtein Transformer

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 102f2633-05a5-43f6-a217-2b69cd0f7a0a · outbound

This paper cites These results are shown in Table.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision These results are shown in Table

Reference 13

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation b1cabb3a-9f80-4b4e-8f56-01a6bbefc696 · outbound

This paper cites MoleculeNet: A Benchmark for Molecular Machine Learning.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision MoleculeNet: A Benchmark for Molecular Machine Learning

Reference 14

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Observation 720f2df5-f178-4a47-98c8-1533b7a69521 · outbound

This paper cites Poisoning medical knowledge using large language models.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision Poisoning medical knowledge using large language models

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 97c11e42-6340-4137-ac66-725edb3bacb4 · outbound

This paper cites Towards a unified training for levenshtein transformer.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision Towards a unified training for levenshtein transformer

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 25b87e03-e4ce-469a-9f16-965c01d53bc1 · outbound

This paper cites A large range of Pre-trained SMILES LMs (Wang et al., 2019a; Chithrananda et al., 2020; Ross et al.,.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision A large range of Pre-trained SMILES LMs (Wang et al., 2019a; Chithrananda et al., 2020; Ross et al.,

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 8e0a6afc-8f98-4655-a92a-5cbde6164599 · outbound

This paper cites These models typically leverage techniques like MLM and autoregressive pre-training to capture the complex syntax and semantics embedded in SMILES sequences.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision These models typically leverage techniques like MLM and autoregressive pre-training to capture the complex syntax and semantics embedded in SMILES sequences

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T20:39:37.253980Z digest=sha256:9aec1bdf729be65a8cec616ccb28a323ec0507a72e9053e8619d64437db4ac9a

Observation 2159914e-94b7-4e23-af5c-33d73155ed87 · outbound

This paper cites (2021) applied generative models to drug design tasks.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision (2021) applied generative models to drug design tasks

Reference 19

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation d67c9a32-391b-4a73-b12e-4c46cff98648 · outbound

This paper cites Recognizing that edit operations from an input sequence to a target output can be diverse and difficult to compute directly, Gu et al.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision Recognizing that edit operations from an input sequence to a target output can be diverse and difficult to compute directly, Gu et al

Reference 20

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source=pdf_text observed=2026-08-11T20:39:37.262040Z digest=sha256:08cc5571a5e5d338c866debe66c1efb1f61c0495eeefee524fb17ed70f714e1c

Observation c895f56b-b1bd-4c5c-8b2b-4aa5091bd848 · outbound

This paper cites an unresolved cited work.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision Unresolved cited work

Reference 26

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation bc1c4ecf-c542-4d67-b26d-6e6ff9ce483c · outbound

This paper cites Table 13: Performance of SMI-EDITORand SMI-MLMwith different scales on downstream tasks.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision Table 13: Performance of SMI-EDITORand SMI-MLMwith different scales on downstream tasks

Reference 29

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 7975d800-134f-466e-85a8-33b7276081ed · outbound

This paper cites A clear negative correlation with a coefficient of R=−0.69 is observed.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision A clear negative correlation with a coefficient of R=−0.69 is observed

Reference 30

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source=pdf_text observed=2026-08-11T20:39:37.295513Z digest=sha256:03f724bf43ca7390cb7bb5d42aa8edaf316b9db1b3a10357d496132f22f9cca0

Observation 975ce3ff-44bd-42ca-9dde-d0016df2dd72 · outbound

This paper cites This further highlights the superiority of the SMI-EDITORin modeling the semantics of molecular substructures.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision This further highlights the superiority of the SMI-EDITORin modeling the semantics of molecular substructures

Reference 31

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Observation 750f30eb-6c98-471c-bf2f-2afcc01a8d9c · outbound

This paper cites an unresolved cited work.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision Unresolved cited work

Reference 32

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Observation 9657d8a3-6da4-47df-9318-bbd62fd5ad45 · outbound

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SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision Unresolved cited work

Reference 33

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Observation c0883700-9fd8-40ed-8da5-92a58497bb8d · outbound

This paper cites an unresolved cited work.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision Unresolved cited work

Reference 34

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 29c0b950-3d25-428b-9837-e96fa7c4df85 · outbound

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SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision Unresolved cited work

Reference 35

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source=pdf_text observed=2026-08-11T20:39:37.311685Z digest=sha256:c999d27e12cfa377459fecf36bdabf0ddd835fe7fd100e810d8f1c8c87ca095f

Observation 3de96dd6-2269-4191-97cf-30d7ba372929 · outbound

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SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision Unresolved cited work

Reference 255

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation d50cf05f-3681-48a6-826d-1340d31eec1f · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2008

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Observation 7355b599-4a94-4512-b680-22e291de0fb2 · outbound

This paper cites Regularized molecular conformation fields.Advances in Neural Information Processing Systems, 35:18929– 18941, 2022a.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision Regularized molecular conformation fields.Advances in Neural Information Processing Systems, 35:18929– 18941, 2022a

Reference 2017

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Observation 9726e053-c4b2-4ddb-b92f-7f70880e3dc5 · outbound

This paper cites Translation between Molecules and Natural Language.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision Translation between Molecules and Natural Language

Reference 2018

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source=pdf_text observed=2026-08-11T20:39:37.206808Z digest=sha256:84c410fc308f2bfa8f0b338a0014d3a5d61477402da4fc93166a1729a5939db4

Observation adfa8d98-0256-4a1b-bb03-553469fe7be2 · outbound

This paper cites BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations

Reference 2019

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Observation 53984c6f-08d0-4223-b32f-a8d13722f676 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision Adam: A Method for Stochastic Optimization

Reference 2020

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Observation 7f89474f-5e7c-483b-87e4-e8aadcfc6dc0 · outbound

This paper cites ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 2021

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-11T20:39:37.199443Z digest=sha256:08fdf6d2b5f7a7ffb0f6860a4a7472d3fb9e44e656bbc5b42c9f3a43430dd3db

Observation 4fac8878-26e7-410c-a2e5-077fbbef7045 · outbound

This paper cites InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization

Reference 2022

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Source-reported events for the cited work

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Observation be45c2c9-b502-43c6-bdbe-68d91dd1bf8a · outbound

This paper cites Pre-training Molecular Graph Representation with 3D Geometry.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision Pre-training Molecular Graph Representation with 3D Geometry

Reference 2023

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:39:37.216958Z digest=sha256:048362d9a4baa8d017cd0bc4cb803f71781fa872bda63b144485834f9ac896c9

Observation d7fa7a16-7750-484c-b164-8528d11b147b · outbound

This paper cites Contextual Representation Learning beyond Masked Language Modeling.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision Contextual Representation Learning beyond Masked Language Modeling

Reference 2024

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unresolved
no resolver link, observed 2026-08-11T20:39:37.210088Z

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source=pdf_text observed=2026-08-11T20:39:37.210088Z digest=sha256:20e76ce4ebbfaebd5bb36050dd6382d6b64c86b0cebe3eef381388b72f04975d

Pith citing papers

Observation a1a1894e-8875-4902-b2e6-66393a9de2c7 · inbound

ExLM: Rethinking the Impact of [MASK] Tokens in Masked Language Models cites this paper.

ExLM: Rethinking the Impact of [MASK] Tokens in Masked Language Models SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision

Reference 85

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local_arxiv, observed 2026-08-10T16:17:32.364579Z

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source=arxiv_source observed=2026-08-10T16:17:32.245386Z digest=sha256:3d29c383c5e7818e2b111f16caff6abdb18de6d9e63e42cc4c48cd9f2121351e