Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-11T03:42:21.307552Z
Paper Citation Record · LEDGER
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES
As of 7 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 0 inbound Pith citation observations for arXiv:2607.05691.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
59 of 59 outbound references displayed
- verified exact30
- verified fuzzy10
- unresolved1
- parse uncertain0
- malformed identifier5
- metadata mismatch13
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 1940799e-0e73-4639-8a0a-6917d9271468 · outbound
This paper cites Knowledge-Centric Hallucination Detection.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Knowledge-Centric Hallucination Detection
Reference 1
- Resolution
- metadata mismatch
- doi, observed 2026-07-11T03:47:46.800090Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation fc838ed1-31b9-4fe7-9c19-7caedcaf264d · outbound
This paper cites Stop Taking Tokenizers for Granted: They Are Core Design Decisions in Large Language Models.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Stop Taking Tokenizers for Granted: They Are Core Design Decisions in Large Language Models
Reference 2
- Resolution
- verified exact
- doi, observed 2026-07-11T03:47:47.010713Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 4da1c91a-caf6-450d-975e-b003a8b086e7 · outbound
This paper cites Randomized SMILES strings improve the quality of molecular generative models.J.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Randomized SMILES strings improve the quality of molecular generative models.J
Reference 3
- Resolution
- metadata mismatch
- doi, observed 2026-07-11T03:47:46.878624Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation e747b5e6-1036-45ef-8c62-fb459b3c9792 · outbound
This paper cites 2020 , note =.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES 2020 , note =
Reference 4
- Resolution
- metadata mismatch
- doi, observed 2026-07-11T03:47:46.600036Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 90503f89-3e93-45c7-85e5-157611c905a0 · outbound
This paper cites Ellie Pavlick and Tom Kwiatkowski.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Ellie Pavlick and Tom Kwiatkowski
Reference 5
- Resolution
- metadata mismatch
- doi, observed 2026-07-11T03:47:46.306603Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation b32ebd05-36a3-4f29-a6e0-f7deb83a358a · outbound
This paper cites BARTSmiles: Generative masked language models for molecular representations.Journal of Chemical Information and Modeling, 64(15):5832– 5843, 2024.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES BARTSmiles: Generative masked language models for molecular representations.Journal of Chemical Information and Modeling, 64(15):5832– 5843, 2024
Reference 6
- Resolution
- verified exact
- doi, observed 2026-07-11T03:47:46.855731Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 8a65a5e0-d00b-4194-b9b1-d775287052a9 · outbound
This paper cites Chemberta: Large-scale self-supervised pretraining for molecular property prediction, 2020.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Chemberta: Large-scale self-supervised pretraining for molecular property prediction, 2020
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-11T03:47:52.567331Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation f2743ddb-1d27-4399-9069-321606e3a725 · outbound
This paper cites Two counterexamples to tokenization and the noiseless channel.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Two counterexamples to tokenization and the noiseless channel
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-11T03:47:52.542165Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation c5a3a424-1c43-4216-a404-98aac6de07bf · outbound
This paper cites Investigating the effectiveness of BPE: The power of shorter sequences.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Investigating the effectiveness of BPE: The power of shorter sequences
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-11T03:47:52.605978Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 87b02662-1458-40e4-9da3-7c77f1dfc0d3 · outbound
This paper cites Investigating the Effectiveness of BPE : The Power of Shorter Sequences.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Investigating the Effectiveness of BPE : The Power of Shorter Sequences
Reference 10
- Resolution
- metadata mismatch
- doi, observed 2026-07-11T03:47:46.369524Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 6b296931-3b02-4c47-9688-a014f4c1db5f · outbound
This paper cites Measuring chemical LLM robustness to molecular representations: a SMILES variation-based framework.Journal of Cheminformatics, 17(1):164,.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Measuring chemical LLM robustness to molecular representations: a SMILES variation-based framework.Journal of Cheminformatics, 17(1):164,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-11T03:47:52.516143Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation d0e1adae-2431-4eb0-a478-b2157b555c83 · outbound
This paper cites Measuring chemical LLM robustness to molecular representations: a SMILES variation-based framework.J.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Measuring chemical LLM robustness to molecular representations: a SMILES variation-based framework.J
Reference 12
- Resolution
- verified exact
- doi, observed 2026-07-11T03:47:46.653620Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 8d8eecee-2b4f-4814-952e-b2e700fe3425 · outbound
This paper cites Finding the Optimal Vocabulary Size for Neural Machine Translation.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Finding the Optimal Vocabulary Size for Neural Machine Translation
Reference 13
- Resolution
- verified exact
- doi, observed 2026-07-11T03:47:46.458625Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation c5242c0c-59b7-4848-bf5f-45109230c5dc · outbound
This paper cites Grygorenko, Dmytro S.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Grygorenko, Dmytro S
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-07-11T03:47:46.958225Z
Source-reported events for the cited work
correction dated 2020-12-04. Source: crossref record 10.1016/j.isci.2020.101873->10.1016/j.isci.2020.101681:correction, observed 2026-07-11T03:12:21.396765+00:00. This notice travels one citation hop only.
Observation c8011620-85f0-45dc-9165-22b65608add0 · outbound
This paper cites Smirk fork for the vocabulary–tokenizer comparison study: shared glyph-id front-end with GpeTrainer (bpe) and a unigram-lm sibling trainer.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Smirk fork for the vocabulary–tokenizer comparison study: shared glyph-id front-end with GpeTrainer (bpe) and a unigram-lm sibling trainer
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-11T03:47:52.630194Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation d5766850-77ea-4625-ae20-1d4e9ab4e451 · outbound
This paper cites Dynamic Chunking for End-to-End Hierarchical Sequence Modeling.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Dynamic Chunking for End-to-End Hierarchical Sequence Modeling
Reference 16
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-11T03:47:48.606361Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 2e69c206-158f-461b-ad73-256544162c11 · outbound
This paper cites an unresolved cited work.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Unresolved cited work
Reference 17
- Resolution
- unresolved
- raw_fallback, observed 2026-07-11T03:47:52.613299Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 2c8da5db-f55c-4866-8df6-cb5201df592f · outbound
This paper cites The tokenization bottleneck: How vocabulary extension improves chemistry representation learning in pretrained language models, 2025.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES The tokenization bottleneck: How vocabulary extension improves chemistry representation learning in pretrained language models, 2025
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-11T03:47:52.578748Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 15670ed5-18b3-47c7-b6e3-99b77615ef55 · outbound
This paper cites Shoemaker, Paul A.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Shoemaker, Paul A
Reference 19
- Resolution
- verified exact
- doi, observed 2026-07-11T03:47:46.706233Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 017ae20d-1532-4077-bd45-471b51371ea5 · outbound
This paper cites Self-Referencing Embedded Strings (SELFIES): A 100% Robust Molecular String Representation.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Self-Referencing Embedded Strings (SELFIES): A 100% Robust Molecular String Representation
Reference 20
- Resolution
- verified exact
- doi, observed 2026-07-11T03:47:46.988060Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 5269996b-4cfc-4851-8f7b-638709bbe410 · outbound
This paper cites Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates
Reference 21
- Resolution
- verified exact
- doi, observed 2026-07-11T03:47:46.638522Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 571a6f32-97b8-4183-9d0b-99818882bc21 · outbound
This paper cites SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing
Reference 22
- Resolution
- verified exact
- doi, observed 2026-07-11T03:47:46.758373Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 40ee98c9-4b8f-451f-b0ab-8d787553f7ad · outbound
This paper cites Fishing for Magikarp: Automatically Detecting Under-trained Tokens in Large Language Models.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Fishing for Magikarp: Automatically Detecting Under-trained Tokens in Large Language Models
Reference 23
- Resolution
- verified exact
- doi, observed 2026-07-11T03:47:46.694437Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 447ab5a8-70a8-4ab9-a7a8-bf4cf8fcd373 · outbound
This paper cites Scalfani and Yakov Pechersky and Kazuya Ujihara and Daniel Probst and Jeremy Monat and Juuso Lehtivarjo , title =.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Scalfani and Yakov Pechersky and Kazuya Ujihara and Daniel Probst and Jeremy Monat and Juuso Lehtivarjo , title =
Reference 24
- Resolution
- metadata mismatch
- doi, observed 2026-07-11T03:47:46.432262Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 93b3af8e-9fb0-4840-8cc4-62f930f2e312 · outbound
This paper cites Comparing SMILES and SELFIES tokenization for enhanced chemical language modeling.Scientific Reports, 14(1):25016, 2024.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Comparing SMILES and SELFIES tokenization for enhanced chemical language modeling.Scientific Reports, 14(1):25016, 2024
Reference 25
- Resolution
- verified exact
- doi, observed 2026-07-11T03:47:46.932430Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 5d7d37ee-b699-4ab4-924a-8314de86aad2 · outbound
This paper cites CycPeptM- PDB: A comprehensive database of membrane permeability of cyclic peptides.Journal of Chemical Information and Modeling, 63(7):2240–2250, 2023.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES CycPeptM- PDB: A comprehensive database of membrane permeability of cyclic peptides.Journal of Chemical Information and Modeling, 63(7):2240–2250, 2023
Reference 26
- Resolution
- verified exact
- doi, observed 2026-07-11T03:47:46.732281Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation d8c6c11c-9266-496e-a0d1-cb6929004913 · outbound
This paper cites SMILES pair encoding: A data-driven substructure tokenization algorithm for deep learning.Journal of Chemical Information and Modeling, 61(4):1560–1569, 2021.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES SMILES pair encoding: A data-driven substructure tokenization algorithm for deep learning.Journal of Chemical Information and Modeling, 61(4):1560–1569, 2021
Reference 27
- Resolution
- verified exact
- doi, observed 2026-07-11T03:47:46.906150Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation aa1383ad-533b-4fbf-9ce6-ddf2b5e4bf44 · outbound
This paper cites Scaffold-BPE: Enhancing Byte Pair Encoding for Large Language Models with Simple and Effective Scaffold Token Removal.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Scaffold-BPE: Enhancing Byte Pair Encoding for Large Language Models with Simple and Effective Scaffold Token Removal
Reference 28
- Resolution
- verified exact
- local_arxiv, observed 2026-07-11T03:47:48.550408Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 64990453-bd85-4645-8489-16cee6ea418f · outbound
This paper cites SuperBPE: Space Travel for Language Models.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES SuperBPE: Space Travel for Language Models
Reference 29
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-11T03:47:48.553716Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation b9b0fa1a-3e17-4e50-a9f9-477f6a31213f · outbound
This paper cites HuggingFace’s tokenizers: Fast state-of-the-art tokenizers optimized for research and production.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES HuggingFace’s tokenizers: Fast state-of-the-art tokenizers optimized for research and production
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-11T03:47:52.711742Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation c8705c67-22f6-4b94-8b48-f2e04376b670 · outbound
This paper cites Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models
Reference 31
- Resolution
- verified exact
- local_arxiv, observed 2026-07-11T03:47:48.584286Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation c9ddf76a-e7a6-4842-9e83-471f432030fa · outbound
This paper cites an unresolved cited work.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Unresolved cited work
Reference 32
- Resolution
- verified exact
- doi, observed 2026-07-11T03:47:46.772764Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 445a4887-3dfb-424f-8792-47608d75d32b · outbound
This paper cites O’Boyle and Andrew Dalke.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES O’Boyle and Andrew Dalke
Reference 33
- Resolution
- verified exact
- doi, observed 2026-07-11T03:47:46.822759Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 05d66f14-9838-4e25-9a40-c438a24c5c58 · outbound
This paper cites Byte Latent Transformer: Patches Scale Better Than Tokens.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Byte Latent Transformer: Patches Scale Better Than Tokens
Reference 34
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-11T03:47:48.485819Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 64530f2c-2391-4eb3-9f66-65817d9f2cdd · outbound
This paper cites 2020 , journal =.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES 2020 , journal =
Reference 35
- Resolution
- metadata mismatch
- doi, observed 2026-07-11T03:47:46.571580Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 52cac4bd-5d78-4dd6-9751-a24a18b8ef28 · outbound
This paper cites Optimizing SMILES token sequences via trie-based refinement and transition graph filtering.Journal of Cheminformatics, 18(1):13, 2026.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Optimizing SMILES token sequences via trie-based refinement and transition graph filtering.Journal of Cheminformatics, 18(1):13, 2026
Reference 36
- Resolution
- verified exact
- doi, observed 2026-07-11T03:47:46.749898Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation b84a5871-00b0-43ff-871e-62082d5f1496 · outbound
This paper cites O’Reilly Media, 2019.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES O’Reilly Media, 2019
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-11T03:47:52.734509Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation dac804ef-6bcb-43dd-8deb-5fce4d35e3e0 · outbound
This paper cites How Much is Enough? The Diminishing Returns of Tokenization Training Data.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES How Much is Enough? The Diminishing Returns of Tokenization Training Data
Reference 38
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-11T03:47:48.502700Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 439a5b4a-94d3-4980-a915-7adcaec10880 · outbound
This paper cites How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models , booktitle =.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models , booktitle =
Reference 39
- Resolution
- verified exact
- doi, observed 2026-07-11T03:47:46.928886Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 0ad5d913-b6f6-4b78-9128-114754b17405 · outbound
This paper cites ReactionT5: a large-scale pre-trained model towards application of limited reaction data.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES ReactionT5: a large-scale pre-trained model towards application of limited reaction data
Reference 40
- Resolution
- verified exact
- local_arxiv, observed 2026-07-11T03:47:46.825551Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation dbcae29c-69bb-4317-be48-03d1300d9366 · outbound
This paper cites Schmidt, Varshini Reddy, Haoran Zhang, Alec Alameddine, Omri Uzan, Yuval Pinter, and Chris C.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Schmidt, Varshini Reddy, Haoran Zhang, Alec Alameddine, Omri Uzan, Yuval Pinter, and Chris C
Reference 41
- Resolution
- malformed identifier
- raw_fallback, observed 2026-07-11T03:47:52.757696Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 991dc72e-7aef-41e6-a93e-cd8c6bb89ca7 · outbound
This paper cites Schmidt, Varshini Reddy, Chris Tanner, and Yuval Pinter.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Schmidt, Varshini Reddy, Chris Tanner, and Yuval Pinter
Reference 42
- Resolution
- verified exact
- arxiv_id, observed 2026-07-11T03:47:48.613839Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 61918727-984f-4392-8937-2015451fc2fa · outbound
This paper cites Small-footprint keyword spotting using deep neural networks.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Small-footprint keyword spotting using deep neural networks
Reference 43
- Resolution
- malformed identifier
- doi_truncated, observed 2026-07-11T03:47:46.981057Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation f87d3533-8a22-43f0-86d1-549e0077e730 · outbound
This paper cites found in translation.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES found in translation
Reference 44
- Resolution
- verified exact
- doi, observed 2026-07-11T03:47:46.306101Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 50489d37-0294-43b9-9855-51a6e4ba4350 · outbound
This paper cites Neural machine translation of rare words with subword units.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Neural machine translation of rare words with subword units
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-11T03:47:52.638351Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation d77d4d9f-b228-43d4-a81b-eebd0581f86f · outbound
This paper cites Neural machine translation of rare words with subword units.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Neural machine translation of rare words with subword units
Reference 46
- Resolution
- metadata mismatch
- doi, observed 2026-07-11T03:47:46.017589Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 32fb769d-68dd-4722-a9bb-46039949b027 · outbound
This paper cites Skinnider.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Skinnider
Reference 47
- Resolution
- verified exact
- doi, observed 2026-07-11T03:47:46.790296Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 2c067427-c970-4e4f-b6a9-df28c2c1f67f · outbound
This paper cites COCONUT online: Collection of open natural products database.Journal of Cheminformatics, 13(1):2, 2021.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES COCONUT online: Collection of open natural products database.Journal of Cheminformatics, 13(1):2, 2021
Reference 48
- Resolution
- malformed identifier
- raw_fallback, observed 2026-07-11T03:47:52.664769Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 2d911181-81a0-4a6c-8a63-96f29dfeb033 · outbound
This paper cites Linguistic laws meet protein sequences: A comparative analysis of subword tokenization methods, 2024.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Linguistic laws meet protein sequences: A comparative analysis of subword tokenization methods, 2024
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-11T03:47:52.689876Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 93329e57-421c-4892-b768-bea2048311f7 · outbound
This paper cites Ülgen, Nilgün Karalı, and Arzucan Özgür.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Ülgen, Nilgün Karalı, and Arzucan Özgür
Reference 50
- Resolution
- verified exact
- doi, observed 2026-07-11T03:47:46.901649Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation ab4828f2-b58f-4b8e-86b6-57ef7ca03d99 · outbound
This paper cites Tingle, Khanh G.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Tingle, Khanh G
Reference 51
- Resolution
- verified exact
- doi, observed 2026-07-11T03:47:46.957064Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation bafcdac0-f60d-43b5-b7f4-c299d951637f · outbound
This paper cites LLaMA: Open and Efficient Foundation Language Models.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES LLaMA: Open and Efficient Foundation Language Models
Reference 52
- Resolution
- verified exact
- local_arxiv, observed 2026-07-11T03:47:48.527944Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 33871cd9-2fbf-42da-9e9f-2a38ce5aa8f9 · outbound
This paper cites Ucak, Islambek Ashyrmamatov, and Juyong Lee.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Ucak, Islambek Ashyrmamatov, and Juyong Lee
Reference 53
- Resolution
- malformed identifier
- raw_fallback, observed 2026-07-11T03:47:52.591354Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation ac076060-408f-4c26-9f84-cf9a135f6171 · outbound
This paper cites Tokenization for molecular foundation models.Journal of Chemical Information and Modeling, 66(3):1384–1393, 2026.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Tokenization for molecular foundation models.Journal of Chemical Information and Modeling, 66(3):1384–1393, 2026
Reference 54
- Resolution
- verified exact
- doi, observed 2026-07-11T03:47:46.095034Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation d44610aa-489a-4383-925b-1f2e96ed8e7d · outbound
This paper cites In: Che, W., Nabende, J., Shutova, E., Pilehvar, M.T.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES In: Che, W., Nabende, J., Shutova, E., Pilehvar, M.T
Reference 55
- Resolution
- metadata mismatch
- doi, observed 2026-07-11T03:47:46.722668Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 976f5da0-ca25-4135-8a82-e9e48fdca348 · outbound
This paper cites SMILES, a chemical language and information system.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES SMILES, a chemical language and information system
Reference 56
- Resolution
- malformed identifier
- raw_fallback, observed 2026-07-11T03:47:52.781862Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 5dd37a15-1741-4077-b6c7-ec15b1cc6295 · outbound
This paper cites Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S
Reference 57
- Resolution
- verified exact
- doi, observed 2026-07-11T03:47:46.547235Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 0b4d4730-7f96-4d5a-abd5-2c41c69465c4 · outbound
This paper cites Manners, James Blackshaw, Sybilla Corbett, Marleen de Veij, Haris Ioannidis, David Mendez Lopez, Juan F.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Manners, James Blackshaw, Sybilla Corbett, Marleen de Veij, Haris Ioannidis, David Mendez Lopez, Juan F
Reference 58
- Resolution
- verified exact
- doi, observed 2026-07-11T03:47:46.515804Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 49a3c7cb-578a-488f-b57f-ef9b53a37dcf · outbound
This paper cites Tokenization and the Noiseless Channel.
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Tokenization and the Noiseless Channel
Reference 59
- Resolution
- verified exact
- doi, observed 2026-07-11T03:47:46.460245Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Pith citing papers
No inbound Pith citation observations are available.