Paper Citation Record · LEDGER

SeqPE: Transformer with Sequential Position Encoding

As of 8 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:2506.13277.

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

pith.paper-citation-record.v1
2506.13277 v2

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:45:07.090558Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T16:12:55.647909Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

54 of 54 outbound references displayed

verified exact1
verified fuzzy30
unresolved23
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b4c26672-508d-466b-8b47-f94579e1d183 · outbound

This paper cites The Falcon Series of Open Language Models.

SeqPE: Transformer with Sequential Position Encoding The Falcon Series of Open Language Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T00:45:00.999029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8467c5b6-ca6a-48e7-a2e0-2fc7b15db498 · outbound

This paper cites Lex- ical generalization improves with larger models and longer training.

SeqPE: Transformer with Sequential Position Encoding Lex- ical generalization improves with larger models and longer training

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:14.064162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5ee141c4-4d62-4d2a-923a-fd94e56ca50b · outbound

This paper cites Language models are few-shot learners.Ad- vances in Neural Information Processing Systems (NeurIPS), 2020.

SeqPE: Transformer with Sequential Position Encoding Language models are few-shot learners.Ad- vances in Neural Information Processing Systems (NeurIPS), 2020

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:13.919656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 537ab71d-2676-46d2-8287-45283bd7d83f · outbound

This paper cites CLEX: Continuous length PREPRINT 10 extrapolation for large language models.

SeqPE: Transformer with Sequential Position Encoding CLEX: Continuous length PREPRINT 10 extrapolation for large language models

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:13.756266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3710df3c-d7c6-4470-8fbe-89caadadb9af · outbound

This paper cites Neural ordinary differential equations.Advances in Neural Information Processing Systems (NeurIPS), 2018.

SeqPE: Transformer with Sequential Position Encoding Neural ordinary differential equations.Advances in Neural Information Processing Systems (NeurIPS), 2018

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:13.607832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a71f24c5-bcb3-49cd-bc7f-8127f681cab5 · outbound

This paper cites Extending Context Window of Large Language Models via Positional Interpolation.

SeqPE: Transformer with Sequential Position Encoding Extending Context Window of Large Language Models via Positional Interpolation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T00:45:01.470351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a0197b4-ceb7-4bf1-97c0-cd9e52646518 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

SeqPE: Transformer with Sequential Position Encoding A simple framework for contrastive learning of visual representations

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T00:45:01.567913Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5be5d52-a566-441e-a820-68d0537794f0 · outbound

This paper cites Bert: Pre-training of deep bidi- rectional transformers for language understanding.

SeqPE: Transformer with Sequential Position Encoding Bert: Pre-training of deep bidi- rectional transformers for language understanding

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:13.442873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 319e795a-8a01-4836-9a0a-88b8d57aff51 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

SeqPE: Transformer with Sequential Position Encoding An image is worth 16x16 words: Transformers for image recognition at scale

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T00:45:01.760636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7e03d8f-84fd-409f-9ca4-ba062b00cb5c · outbound

This paper cites ViTAR: Vision Transformer with Any Resolution.

SeqPE: Transformer with Sequential Position Encoding ViTAR: Vision Transformer with Any Resolution

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T00:45:01.870482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dff1153e-1b31-46f5-be67-4af43e4efa2e · outbound

This paper cites SimCSE: Simple contrastive learning of sentence embeddings.

SeqPE: Transformer with Sequential Position Encoding SimCSE: Simple contrastive learning of sentence embeddings

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:13.288354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dab4f1d4-7415-497b-ba99-57b12730a6ac · outbound

This paper cites Convolutional sequence to sequence learning.

SeqPE: Transformer with Sequential Position Encoding Convolutional sequence to sequence learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:13.059326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 415ffa9a-92c5-49c8-a3ac-fc93e0f6178a · outbound

This paper cites Imagebind: One embedding space to bind them all.

SeqPE: Transformer with Sequential Position Encoding Imagebind: One embedding space to bind them all

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:12.866519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a699aa80-0be0-4c0b-ac42-e5b656d60118 · outbound

This paper cites Bootstrap your own latent-a new approach to self-supervised learn- ing.Advances in Neural Information Processing Systems (NeurIPS), 2020.

SeqPE: Transformer with Sequential Position Encoding Bootstrap your own latent-a new approach to self-supervised learn- ing.Advances in Neural Information Processing Systems (NeurIPS), 2020

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:12.711471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 78449ffc-b1b9-47d7-b9ec-ce59d2ec665e · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

SeqPE: Transformer with Sequential Position Encoding DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T00:45:02.332038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aaca7eb5-b6f8-4d09-a665-c5d4e9145c51 · outbound

This paper cites Transformer Language Models without Positional Encodings Still Learn Positional Information.

SeqPE: Transformer with Sequential Position Encoding Transformer Language Models without Positional Encodings Still Learn Positional Information

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T00:45:02.420065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b70eba20-f7a4-4880-88c3-c2f6859010e4 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning.

SeqPE: Transformer with Sequential Position Encoding Momentum contrast for unsupervised visual representation learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T00:45:02.529522Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c40ef30-ec7f-4977-9034-52b1f0127379 · outbound

This paper cites Deberta: Decoding-enhanced bert with disentan- gled attention.

SeqPE: Transformer with Sequential Position Encoding Deberta: Decoding-enhanced bert with disentan- gled attention

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:12.565695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 26d61e26-fd80-4c3f-9725-2bb400f5f1d7 · outbound

This paper cites Rotary position embedding for vision trans- former.

SeqPE: Transformer with Sequential Position Encoding Rotary position embedding for vision trans- former

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:12.378153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fe1381c3-5365-42fb-ae49-b9cefe68637a · outbound

This paper cites RULER: What’s the real context size of your long- context language models? InFirst Conference on Language Modeling, 2024.

SeqPE: Transformer with Sequential Position Encoding RULER: What’s the real context size of your long- context language models? InFirst Conference on Language Modeling, 2024

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:12.168551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 514b9315-dc90-411a-98be-9b81b6177aa4 · outbound

This paper cites Fourier Position Embedding: Enhancing Attention's Periodic Extension for Length Generalization.

SeqPE: Transformer with Sequential Position Encoding Fourier Position Embedding: Enhancing Attention's Periodic Extension for Length Generalization

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T00:45:02.940894Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ff6ec93-4df7-4e4e-bda9-7843449a3841 · outbound

This paper cites Improve Transformer Models with Better Relative Position Embeddings.

SeqPE: Transformer with Sequential Position Encoding Improve Transformer Models with Better Relative Position Embeddings

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T00:45:03.051745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8381f6a9-28ff-495e-a09e-eaf3e6c2ad59 · outbound

This paper cites Adversarial examples for evaluating reading comprehension systems.

SeqPE: Transformer with Sequential Position Encoding Adversarial examples for evaluating reading comprehension systems

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:11.918224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0f516603-bfda-4d77-a4bc-534ee05c4830 · outbound

This paper cites The impact of positional encoding on length generalization in transformers.Advances in Neural Information Process- ing Systems (NeurIPS), 2023.

SeqPE: Transformer with Sequential Position Encoding The impact of positional encoding on length generalization in transformers.Advances in Neural Information Process- ing Systems (NeurIPS), 2023

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:11.704191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6781791a-f000-40ba-8cca-4568d50cc7dc · outbound

This paper cites Rethinking posi- tional encoding in language pre-training.

SeqPE: Transformer with Sequential Position Encoding Rethinking posi- tional encoding in language pre-training

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:11.505839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fae90996-c431-4a1a-a90f-a2cbc48d2684 · outbound

This paper cites Efficient memory management for large language model serving with pagedattention.

SeqPE: Transformer with Sequential Position Encoding Efficient memory management for large language model serving with pagedattention

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:11.296749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 03a9cf9c-51af-453e-893c-cbad769da65f · outbound

This paper cites Aria: An Open Multimodal Native Mixture-of-Experts Model.

SeqPE: Transformer with Sequential Position Encoding Aria: An Open Multimodal Native Mixture-of-Experts Model

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T00:45:03.582120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed3925fa-7096-4394-a566-9e20715b40a4 · outbound

This paper cites Learning to encode position for transformer with continuous dynamical model.

SeqPE: Transformer with Sequential Position Encoding Learning to encode position for transformer with continuous dynamical model

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:11.131311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3dbe7e5e-5205-4153-adbf-04fac29e2835 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

SeqPE: Transformer with Sequential Position Encoding Swin transformer: Hierarchical vision transformer using shifted windows

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:11.001776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0c97f88d-dee0-478f-bef3-3f04709645c0 · outbound

This paper cites Base of RoPE Bounds Context Length.

SeqPE: Transformer with Sequential Position Encoding Base of RoPE Bounds Context Length

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T00:45:03.934025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e0a1b9a-3b7e-4bd9-9874-872f0d867ccb · outbound

This paper cites LieRE: Lie Rotational Positional Encodings.

SeqPE: Transformer with Sequential Position Encoding LieRE: Lie Rotational Positional Encodings

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T00:45:04.109141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f6ff22c-e239-4022-b992-a2981dbb7b05 · outbound

This paper cites Yarn: Efficient context window extension of large language models.

SeqPE: Transformer with Sequential Position Encoding Yarn: Efficient context window extension of large language models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T00:45:04.234158Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31ac0734-48c7-40a3-8d86-1ba065ebea8f · outbound

This paper cites Train short, test long: Attention with linear biases enables input length extrapolation.

SeqPE: Transformer with Sequential Position Encoding Train short, test long: Attention with linear biases enables input length extrapolation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:10.861753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 618b2fa7-2274-41bb-9b91-3cc8ab016db2 · outbound

This paper cites Learning transferable visual models from natural lan- guage supervision.

SeqPE: Transformer with Sequential Position Encoding Learning transferable visual models from natural lan- guage supervision

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:10.813472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d785368e-3271-4ce1-9898-e6e49bebdb97 · outbound

This paper cites Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019.

SeqPE: Transformer with Sequential Position Encoding Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T00:45:04.747048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ece5f877-70fe-4b82-b53c-185d11f4116f · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.Journal of Machine Learning Research, 21:1–67, 2020.

SeqPE: Transformer with Sequential Position Encoding Exploring the limits of transfer learning with a unified text-to-text transformer.Journal of Machine Learning Research, 21:1–67, 2020

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:10.576725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 54926fba-5acf-4e9b-8f2d-d728c182600d · outbound

This paper cites SQuAD: 100,000+ questions for machine comprehension of text.

SeqPE: Transformer with Sequential Position Encoding SQuAD: 100,000+ questions for machine comprehension of text

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:10.017690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3d8d4f1f-9a6b-4e1d-b3ae-af838a9b7a02 · outbound

This paper cites Masked jigsaw puzzle: A versatile position embedding for vision transformers.

SeqPE: Transformer with Sequential Position Encoding Masked jigsaw puzzle: A versatile position embedding for vision transformers

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:09.735457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d9f8bac7-f1de-4333-aafe-137432d24355 · outbound

This paper cites Randomized positional en- codings boost length generalization of transformers.

SeqPE: Transformer with Sequential Position Encoding Randomized positional en- codings boost length generalization of transformers

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:09.464603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 045a7d73-d0ed-43f1-a9db-58dc3ac37653 · outbound

This paper cites Berg, and Li Fei-Fei.

SeqPE: Transformer with Sequential Position Encoding Berg, and Li Fei-Fei

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:09.185234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6d364f78-d86f-4995-81a8-df0a26af711c · outbound

This paper cites Self-Attention with Relative Position Representations.

SeqPE: Transformer with Sequential Position Encoding Self-Attention with Relative Position Representations

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T00:45:05.524625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1016d1e4-8d87-4f21-927a-2189a1ce1e0f · outbound

This paper cites Roformer: Enhanced trans- former with rotary position embedding.Neurocomputing, 568:127063, 2024.

SeqPE: Transformer with Sequential Position Encoding Roformer: Enhanced trans- former with rotary position embedding.Neurocomputing, 568:127063, 2024

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T00:45:05.645116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af7abcf0-206d-45a5-a182-ebc0c74839e3 · outbound

This paper cites Training data-efficient image transformers & distillation through attention.

SeqPE: Transformer with Sequential Position Encoding Training data-efficient image transformers & distillation through attention

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:08.880167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c7c6735d-94d0-4490-a28d-066974923268 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

SeqPE: Transformer with Sequential Position Encoding Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T00:45:05.930536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f24ebe96-1486-4ad4-961c-929e2fc71cf3 · outbound

This paper cites Neural discrete representation learning.Advances in Neural Information Processing Systems (NeurIPS), 2017.

SeqPE: Transformer with Sequential Position Encoding Neural discrete representation learning.Advances in Neural Information Processing Systems (NeurIPS), 2017

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:08.549619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 34acc6fe-9ffc-49cd-9af3-ceb4b8532718 · outbound

This paper cites Gomez, Lukasz Kaiser, and Illia Polosukhin.

SeqPE: Transformer with Sequential Position Encoding Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:08.262950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 53af0900-39fc-4d9a-8388-3e07b958318c · outbound

This paper cites Encoding word order in complex embeddings.

SeqPE: Transformer with Sequential Position Encoding Encoding word order in complex embeddings

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:08.017345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 18b3d858-4843-4de1-9cf0-4ddb4cf95d73 · outbound

This paper cites Scaling Context, Not Parameters: Training a Compact 7B Language Model for Efficient Long-Context Processing.

SeqPE: Transformer with Sequential Position Encoding Scaling Context, Not Parameters: Training a Compact 7B Language Model for Efficient Long-Context Processing

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-08-07T00:45:07.462889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 49a27808-1168-4832-85ae-db7ec72b3a0c · outbound

This paper cites Qwen3 Technical Report.

SeqPE: Transformer with Sequential Position Encoding Qwen3 Technical Report

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T00:45:06.470613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f45b07c-543d-4761-885f-d12285e3c759 · outbound

This paper cites Rope to nope and back again: A new hybrid attention strategy.arXiv preprint arXiv:2501.18795, 2025.

SeqPE: Transformer with Sequential Position Encoding Rope to nope and back again: A new hybrid attention strategy.arXiv preprint arXiv:2501.18795, 2025

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T00:45:06.595748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a83ecd89-2525-47a1-9369-02773535c69e · outbound

This paper cites Length Extrapolation of Transformers: A Survey from the Perspective of Positional Encoding.

SeqPE: Transformer with Sequential Position Encoding Length Extrapolation of Transformers: A Survey from the Perspective of Positional Encoding

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T00:45:06.736553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 977641ad-e15a-4f3a-bd4f-61d87cf7fd34 · outbound

This paper cites Gonzalez, Clark Bar- rett, and Ying Sheng.

SeqPE: Transformer with Sequential Position Encoding Gonzalez, Clark Bar- rett, and Ying Sheng

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:45:07.786520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d8c591f1-df36-4c79-9e62-5873c2e52e81 · outbound

This paper cites Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model.

SeqPE: Transformer with Sequential Position Encoding Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T00:45:06.966912Z

Source-reported events for the cited work

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Observation 72172605-5ec6-4225-9f6a-e932e8fca891 · outbound

This paper cites PoSE: Efficient Context Window Extension of LLMs via Positional Skip-wise Training.

SeqPE: Transformer with Sequential Position Encoding PoSE: Efficient Context Window Extension of LLMs via Positional Skip-wise Training

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T00:45:07.090558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 6211d7a9-37f4-4ccc-9241-221efd67906d · inbound

RePo: Language Models with Context Re-Positioning cites this paper.

RePo: Language Models with Context Re-Positioning SeqPE: Transformer with Sequential Position Encoding

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-03T16:12:55.647909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.