Paper Citation Record · LEDGER

Scaling an Autoregressive Transformer for Single-Cell Generation

As of 17 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2608.02961.

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

pith.paper-citation-record.v1
2608.02961 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:00:12.818619Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

14 of 14 outbound references displayed

verified exact2
verified fuzzy2
unresolved8
parse uncertain0
malformed identifier2
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 871bcacd-c1a1-466c-acb3-d063391c259d · outbound

This paper cites an unresolved cited work.

Scaling an Autoregressive Transformer for Single-Cell Generation Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:00:13.148946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ddb8e016-e537-4caf-8741-ad2d0e27dee7 · outbound

This paper cites Scaling Laws for Masked-Reconstruction Transformers on Single-Cell Transcriptomics.

Scaling an Autoregressive Transformer for Single-Cell Generation Scaling Laws for Masked-Reconstruction Transformers on Single-Cell Transcriptomics

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-15T15:00:13.022610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 60dd904d-55be-4d2a-a0b6-34288fb6154e · outbound

This paper cites Lopez, R.; Regier, J.; Cole, M.

Scaling an Autoregressive Transformer for Single-Cell Generation Lopez, R.; Regier, J.; Cole, M

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:00:13.097885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7be81eb2-7476-45e9-a912-48a72d163ddb · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Scaling an Autoregressive Transformer for Single-Cell Generation LLaMA: Open and Efficient Foundation Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T15:00:12.799282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0d03bb7-e216-45fe-a189-e1241fc8f263 · outbound

This paper cites PRiMeFlow: Capturing Complex Expression Heterogeneity in Perturbation Response Modelling.

Scaling an Autoregressive Transformer for Single-Cell Generation PRiMeFlow: Capturing Complex Expression Heterogeneity in Perturbation Response Modelling

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-15T15:00:12.967389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 351b99ae-6941-4605-a51f-d7f0785e0a4e · outbound

This paper cites arXiv:2603.25240.

Scaling an Autoregressive Transformer for Single-Cell Generation arXiv:2603.25240

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T15:00:12.809282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4261f688-a9bf-4629-ba2d-45e99695ce2e · outbound

This paper cites Formally, writeC= 131for the number of cell types and G= 18,080for the number of genes.

Scaling an Autoregressive Transformer for Single-Cell Generation Formally, writeC= 131for the number of cell types and G= 18,080for the number of genes

Reference 14

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T15:00:13.084305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 861bc977-7af3-4ef2-ac9f-482d9d8b0804 · outbound

This paper cites Scaling Laws for Neural Language Models.

Scaling an Autoregressive Transformer for Single-Cell Generation Scaling Laws for Neural Language Models

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-15T15:00:12.780313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dfa6b8b4-b592-4eb5-b6e4-d21fa533e7a8 · outbound

This paper cites Scaling Language Models: Methods, Analysis & Insights from Training Gopher.

Scaling an Autoregressive Transformer for Single-Cell Generation Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-15T15:00:12.794424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ae71ee9-1746-41a2-bd31-28d18600ff04 · outbound

This paper cites Training Compute-Optimal Large Language Models.

Scaling an Autoregressive Transformer for Single-Cell Generation Training Compute-Optimal Large Language Models

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-15T15:00:12.774927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 575f7764-fed7-4ab0-9cbc-95d0716c315a · outbound

This paper cites Sequential Modeling Enables Scalable Learning for Large Vision Models.

Scaling an Autoregressive Transformer for Single-Cell Generation Sequential Modeling Enables Scalable Learning for Large Vision Models

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-15T15:00:12.760231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59992910-26d6-4f46-8b02-72ddc01cdd05 · outbound

This paper cites REAL: Response Embedding-based Alignment for LLMs.

Scaling an Autoregressive Transformer for Single-Cell Generation REAL: Response Embedding-based Alignment for LLMs

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T15:00:12.814132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a832647-5e5d-45fe-b7fc-db9fc5462640 · outbound

This paper cites bioRxiv 2025.10.23.683759.

Scaling an Autoregressive Transformer for Single-Cell Generation bioRxiv 2025.10.23.683759

Reference 2025

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T15:00:13.112976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation df9076f1-a747-4ee9-a0e2-37f890f08d58 · outbound

This paper cites bioRxiv 2026.02.04.703804.

Scaling an Autoregressive Transformer for Single-Cell Generation bioRxiv 2026.02.04.703804

Reference 2026

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:00:13.132290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.