Paper Citation Record · LEDGER

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning

As of 19 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2607.22777.

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

pith.paper-citation-record.v1
2607.22777 v2

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T05:52:35.830198Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

33 of 33 outbound references displayed

verified exact5
verified fuzzy0
unresolved26
parse uncertain0
malformed identifier2
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 80ca0864-4a0e-41f7-a8f6-2a9de160479b · outbound

This paper cites Attention Is All You Need.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning Attention Is All You Need

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T05:52:30.975036Z

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Observation 13fedc11-c9a4-4fcb-9e6f-0628ae9888de · outbound

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

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-01T05:52:31.151658Z

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Observation 818e3d1a-ce4e-4873-9ea3-2a87ba17379a · outbound

This paper cites Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T05:52:31.238601Z

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Observation 48b77ff8-df5a-4813-be6c-86ac850e578e · outbound

This paper cites Evolutionary-scale prediction of atomic-level protein structure with a language model.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning Evolutionary-scale prediction of atomic-level protein structure with a language model

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T05:52:31.482389Z

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Observation 60cf0704-edbf-42b5-9c43-1722822ca19f · outbound

This paper cites Unified rational protein engineering with sequence- based deep representation learning.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning Unified rational protein engineering with sequence- based deep representation learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T05:52:31.674179Z

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Observation 5448f484-82bf-4fbc-a239-ecf3798b3fa3 · outbound

This paper cites Evaluating protein transfer learning with TAPE.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning Evaluating protein transfer learning with TAPE

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T05:52:31.780163Z

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Observation 56d400e9-e1dc-40ec-a193-b6630a24c2a8 · outbound

This paper cites ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T05:52:31.890594Z

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Observation dc8e4c51-1f82-428b-8a28-304abf19757b · outbound

This paper cites Using deep learning to annotate the protein universe.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning Using deep learning to annotate the protein universe

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0cfb58c6-66bc-46eb-890a-a96f6f912961 · outbound

This paper cites ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T05:52:32.174860Z

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Observation 11daada3-92ba-4490-9ea8-7ad2507a1f15 · outbound

This paper cites FLIP: Benchmark tasks in fitness landscape inference for proteins.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning FLIP: Benchmark tasks in fitness landscape inference for proteins

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T05:52:32.245718Z

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Observation fc94d094-e1d3-4674-b8e8-ed1c66cfd85a · outbound

This paper cites Origins of coevolution between residues distant in protein 3D structures.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning Origins of coevolution between residues distant in protein 3D structures

Reference 11

Resolution: verified exact; doi, observed 2026-08-01T05:53:19.797541Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 253a2bfb-f477-4599-aa0d-07159aae6dd6 · outbound

This paper cites Accurate de novo prediction of protein contact map by ultra-deep learning model.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning Accurate de novo prediction of protein contact map by ultra-deep learning model

Reference 12

Resolution: verified exact; doi, observed 2026-08-01T05:53:19.679579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c9788c57-7a71-4bb1-b4bb-7115f67dc982 · outbound

This paper cites Structure-Aware Transformer for Graph Representation Learning.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning Structure-Aware Transformer for Graph Representation Learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T05:52:32.696332Z

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Observation 04bcd658-2750-4945-a39d-00aaf153d0b8 · outbound

This paper cites Learning from protein structure with geometric vector perceptrons.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning Learning from protein structure with geometric vector perceptrons

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T05:52:32.799182Z

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Observation 500cd1da-2fcd-42fa-88ba-cf508d18c0de · outbound

This paper cites Protein representation learning by geometric structure pretraining.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning Protein representation learning by geometric structure pretraining

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T05:52:32.901428Z

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Observation 43463b3a-324c-4985-9947-5230d5d2d07d · outbound

This paper cites Endowing protein language models with structural knowledge.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning Endowing protein language models with structural knowledge

Reference 16

Resolution: verified exact; doi, observed 2026-08-01T05:53:19.585419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5dc6009d-4a9d-474f-b29b-4471cbf17eff · outbound

This paper cites Structure-based protein function prediction using graph convolutional networks.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning Structure-based protein function prediction using graph convolutional networks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T05:52:33.136059Z

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Observation b61cec61-513f-486b-9156-b3e26c1f0a16 · outbound

This paper cites SaProt: Protein Language Modeling with Structure-aware Vocabulary.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning SaProt: Protein Language Modeling with Structure-aware Vocabulary

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T05:52:33.216599Z

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Observation c3b2a634-ad99-4db5-a216-2690b0e6c78a · outbound

This paper cites Fast and accurate protein structure search with Foldseek.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning Fast and accurate protein structure search with Foldseek

Reference 19

Resolution: malformed identifier; no resolver link, observed 2026-08-01T05:52:33.368713Z

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Unavailable: canonical work link unavailable.

Observation fdfd6cd6-63ed-4027-8206-0f8e5f742f93 · outbound

This paper cites ProstT5: Bilingual Language Model for Protein Sequence and Structure.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning ProstT5: Bilingual Language Model for Protein Sequence and Structure

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T05:52:33.492788Z

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Observation 9a5a6bbb-d072-4737-9085-3a92bc246bf7 · outbound

This paper cites Structure-Informed Protein Language Model.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning Structure-Informed Protein Language Model

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T05:52:33.640744Z

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Observation 44eb5e15-6064-4b19-a0a8-3ce26a9335ef · outbound

This paper cites Robust deep learning-based protein sequence design using ProteinMPNN.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning Robust deep learning-based protein sequence design using ProteinMPNN

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T05:52:33.806736Z

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Observation 8095c967-0883-4e4f-ad46-9298640f0756 · outbound

This paper cites Learning inverse folding from millions of predicted structures.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning Learning inverse folding from millions of predicted structures

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T05:52:33.970836Z

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Observation 44a87902-bfb5-4a93-982c-7021844fba73 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning LoRA: Low-Rank Adaptation of Large Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-01T05:52:34.169880Z

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Observation f1170260-d164-40e7-880e-7063f640d769 · outbound

This paper cites Highly accurate protein structure prediction with AlphaFold.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning Highly accurate protein structure prediction with AlphaFold

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-01T05:52:34.346331Z

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Observation 0b711b86-f715-4408-ace8-1abb65e09ab1 · outbound

This paper cites AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T05:52:34.501723Z

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Observation c07452a3-3d70-4978-8b57-18a4ceda4531 · outbound

This paper cites UniProt: the Universal Protein Knowledgebase in 2023.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning UniProt: the Universal Protein Knowledgebase in 2023

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-01T05:52:34.633531Z

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Observation 7c8867c2-0c16-4c78-bf6b-1d6e5afa0f20 · outbound

This paper cites ProteinShake: Building datasets and benchmarks for deep learning on protein structures.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning ProteinShake: Building datasets and benchmarks for deep learning on protein structures

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-01T05:52:34.846614Z

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Observation 2eea27c4-3f61-4e71-a3be-8310c3c59a18 · outbound

This paper cites Pfam: The protein families database in 2021.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning Pfam: The protein families database in 2021

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-01T05:52:35.008755Z

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Observation 8540e3ff-7cd2-4600-af59-66445d79908a · outbound

This paper cites The Gene Ontology resource: enriching a GOld mine.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning The Gene Ontology resource: enriching a GOld mine

Reference 30

Resolution: verified exact; doi, observed 2026-08-01T05:53:19.457997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2243ff3c-9355-4e35-bcee-d18ad9c95283 · outbound

This paper cites DeepLoc: prediction of protein subcellular localization using deep learning.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning DeepLoc: prediction of protein subcellular localization using deep learning

Reference 31

Resolution: verified exact; doi, observed 2026-08-01T05:53:19.401920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 48107fcc-4029-42b7-83fb-9d98da2049d3 · outbound

This paper cites MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-01T05:52:35.592003Z

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Observation 80c51412-d394-408a-bdfd-d9a00723ad2f · outbound

This paper cites LC-SEPLM scores are compared with reported ESM-S 150M results on EC, fold-classification and GO-MF splits.

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning LC-SEPLM scores are compared with reported ESM-S 150M results on EC, fold-classification and GO-MF splits

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-01T05:52:35.830198Z

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Pith citing papers

No inbound Pith citation observations are available.