Paper Citation Record · LEDGER

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning

As of 23 August 2026, this Paper Citation Record lists 76 of 76 outbound references and 3 inbound Pith citation observations for arXiv:2506.02911.

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

pith.paper-citation-record.v1
2506.02911 v1

Coverage vector

measured 76 of 76 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:17:01.431576Z

measured 79 of 79 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T18:48:59.629102Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T20:56:14.438810Z

Reference resolution

76 of 76 outbound references displayed

verified exact3
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 96474c8c-d8e5-4fc0-8303-02681b2abdd3 · outbound

This paper cites Challenges in unsupervised clustering of single-cell rna-seq data.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Challenges in unsupervised clustering of single-cell rna-seq data

Reference 1

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Observation 8e01376e-887b-4bf9-8b7d-49477baf1075 · outbound

This paper cites Current best practices in single-cell rna-seq analysis: a tutorial.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Current best practices in single-cell rna-seq analysis: a tutorial

Reference 2

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Observation 5717a930-e9b8-43d0-bb34-c2a913b6669d · outbound

This paper cites Integrating single-cell transcriptomic data across different conditions, technologies, and species.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Integrating single-cell transcriptomic data across different conditions, technologies, and species

Reference 3

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Observation 054f247f-850a-4c3b-8dc8-6cf0c8983e40 · outbound

This paper cites From louvain to leiden: guaranteeing well-connected communities.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning From louvain to leiden: guaranteeing well-connected communities

Reference 4

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Observation bbbb5311-4b20-4e96-b1c3-ba4d198de141 · outbound

This paper cites Com- prehensive integration of single-cell data.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Com- prehensive integration of single-cell data

Reference 5

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Observation ece4455e-2a93-4239-83f4-c4ff31dcf799 · outbound

This paper cites Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage

Reference 6

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Observation 112bed47-9f3e-4031-b4cf-080df221e0ae · outbound

This paper cites Eleven grand challenges in single-cell data science.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Eleven grand challenges in single-cell data science

Reference 7

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Observation b665e430-4b9e-4488-88c2-f03e24550502 · outbound

This paper cites A comparison of automatic cell identification methods for single-cell rna sequencing data.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning A comparison of automatic cell identification methods for single-cell rna sequencing data

Reference 8

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Observation 7f50af10-5d1e-4121-ba93-f111d41cf5f8 · outbound

This paper cites scbert as a large-scale pretrained deep language model for cell type annotation of single-cell rna-seq data.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning scbert as a large-scale pretrained deep language model for cell type annotation of single-cell rna-seq data

Reference 9

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Observation 297f3e38-a97e-40bc-81cd-30610bfbe306 · outbound

This paper cites Transfer learning enables predictions in network biology.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Transfer learning enables predictions in network biology

Reference 10

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Observation f586aefe-ae76-4bc7-927b-0c5b1e9fdd19 · outbound

This paper cites Large-scale foundation model on single-cell transcriptomics.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Large-scale foundation model on single-cell transcriptomics

Reference 11

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This paper cites Assessing gpt-4 for cell type annotation in single-cell rna-seq analysis.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Assessing gpt-4 for cell type annotation in single-cell rna-seq analysis

Reference 12

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Observation e6e6f8dc-ca3d-4aa2-80bf-3ac343704c2e · outbound

This paper cites Cell2sentence: Teaching large language models the language of biology.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Cell2sentence: Teaching large language models the language of biology

Reference 13

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Observation b34b11f4-4909-4044-bdf7-87b52819f27c · outbound

This paper cites scelmo: Embeddings from language models are good learners for single-cell data analysis.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning scelmo: Embeddings from language models are good learners for single-cell data analysis

Reference 14

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Observation 60a29201-8f2c-4ce0-96c0-5bfe3aec2136 · outbound

This paper cites Simple and effective embedding model for single-cell biology built from chatgpt.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Simple and effective embedding model for single-cell biology built from chatgpt

Reference 15

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Observation 34c0df96-8d0d-4f7f-91e5-efe7fcf123f4 · outbound

This paper cites Langcell: Language- cell pre-training for cell identity understanding.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Langcell: Language- cell pre-training for cell identity understanding

Reference 16

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Observation efcb3d14-48ee-4304-b950-af8d68c60081 · outbound

This paper cites Multimodal learning of transcriptomes and text enables interactive single-cell rna-seq data exploration with natural-language chats.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Multimodal learning of transcriptomes and text enables interactive single-cell rna-seq data exploration with natural-language chats

Reference 17

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This paper cites A Multi-Modal AI Copilot for Single-Cell Analysis with Instruction Following.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning A Multi-Modal AI Copilot for Single-Cell Analysis with Instruction Following

Reference 18

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Observation 6ae63305-8ee6-4157-bc6a-e8d6cf22e131 · outbound

This paper cites Language-Enhanced Representation Learning for Single-Cell Transcriptomics.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Language-Enhanced Representation Learning for Single-Cell Transcriptomics

Reference 19

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Observation c4ef9fbd-bcf7-40ce-9627-72261937b22e · outbound

This paper cites Au- tomated methods for cell type annotation on scrna-seq data.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Au- tomated methods for cell type annotation on scrna-seq data

Reference 20

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Observation 8f439dd7-d1a4-49b1-9e48-1a70186cfd37 · outbound

This paper cites Opening the black box: interpretable machine learning for geneticists.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Opening the black box: interpretable machine learning for geneticists

Reference 21

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Observation d49b6564-8e10-4e70-ba55-519949e89df7 · outbound

This paper cites OpenAI o1 System Card.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning OpenAI o1 System Card

Reference 22

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This paper cites Self-Reflection in LLM Agents: Effects on Problem-Solving Performance.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Self-Reflection in LLM Agents: Effects on Problem-Solving Performance

Reference 23

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Observation 5745bcd5-c6f8-4eba-934f-d6517a3067ae · outbound

This paper cites Evaluating large language models through role-guide and self-reflection: A comparative study.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Evaluating large language models through role-guide and self-reflection: A comparative study

Reference 24

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This paper cites A mathematical model for curriculum learning for parities.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning A mathematical model for curriculum learning for parities

Reference 25

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Observation 26372481-4fbc-4e29-a570-33c28b71b145 · outbound

This paper cites On curriculum learning for commonsense reasoning.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning On curriculum learning for commonsense reasoning

Reference 26

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Observation 43593b7a-07ca-4c48-b31a-ecafb66ba442 · outbound

This paper cites Defining cell types and states with single-cell genomics.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Defining cell types and states with single-cell genomics

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:17:05.202061Z

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This paper cites A scalable scenic workflow for single-cell gene regulatory network analysis.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning A scalable scenic workflow for single-cell gene regulatory network analysis

Reference 28

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This paper cites sctenifoldnet: a machine learning workflow for constructing and comparing transcriptome-wide gene regulatory networks from single-cell data.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning sctenifoldnet: a machine learning workflow for constructing and comparing transcriptome-wide gene regulatory networks from single-cell data

Reference 29

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Observation dc8306e7-3d04-4120-9652-e7abf495d114 · outbound

This paper cites scgen predicts single-cell perturbation responses.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning scgen predicts single-cell perturbation responses

Reference 30

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This paper cites Machine learning for perturbational single-cell omics.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Machine learning for perturbational single-cell omics

Reference 31

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This paper cites scgpt: toward building a foundation model for single-cell multi-omics using generative ai.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning scgpt: toward building a foundation model for single-cell multi-omics using generative ai

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Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning GPT-4 Technical Report

Reference 33

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Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning LLaMA: Open and Efficient Foundation Language Models

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This paper cites Large language model instruction following: A survey of progresses and challenges.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Large language model instruction following: A survey of progresses and challenges

Reference 35

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Observation ac48ee0e-d853-41e8-8ec5-a551e9c10bbc · outbound

This paper cites Language models are few-shot learners.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Language models are few-shot learners

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This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Chain-of-thought prompting elicits reasoning in large language models

Reference 37

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Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Large language models are zero-shot reasoners

Reference 38

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This paper cites Cz cellxgene discover: a single-cell data platform for scalable exploration, analysis and modeling of aggregated data.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Cz cellxgene discover: a single-cell data platform for scalable exploration, analysis and modeling of aggregated data

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:17:04.604384Z

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Observation b0e8e1be-c5f3-4b0c-97a4-92a7e1afc757 · outbound

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

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 40

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This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 41

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Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Qwen Technical Report

Reference 42

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Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 43

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Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Reference 44

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This paper cites DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 45

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This paper cites Supervising the search process produces reliable and generalizable information-seeking agents.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Supervising the search process produces reliable and generalizable information-seeking agents

Reference 46

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Observation e33be5f4-da44-49b8-895f-66da14033761 · outbound

This paper cites ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning

Reference 47

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This paper cites The role of ontologies in biological and biomedical research: a functional perspective.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning The role of ontologies in biological and biomedical research: a functional perspective

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:17:04.519626Z

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Observation b95b1192-41da-4a1d-8e5a-12099638567e · outbound

This paper cites Gene ontology: tool for the unification of biology.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Gene ontology: tool for the unification of biology

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:17:04.459346Z

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Observation 3dcc2852-3a6e-49bf-82cc-ffd3dcd0bcc4 · outbound

This paper cites The Impact of Reasoning Step Length on Large Language Models.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning The Impact of Reasoning Step Length on Large Language Models

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:59.137930Z

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Observation 095a7ad4-357b-44b2-9a39-c234102d7d8e · outbound

This paper cites Opportunities and challenges for chatgpt and large language models in biomedicine and health.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Opportunities and challenges for chatgpt and large language models in biomedicine and health

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:17:04.390642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1fe3ab29-a6ba-419e-adbf-c1dd12806dab · outbound

This paper cites Single cell rna sequencing of human microglia uncovers a subset associated with alzheimer’s disease.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Single cell rna sequencing of human microglia uncovers a subset associated with alzheimer’s disease

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:17:04.286171Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a6127949-2ba2-4861-acf0-359482a84946 · outbound

This paper cites Single-cell rna-seq analysis reveals cell subsets and gene signatures associated with rheumatoid arthritis disease activity.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Single-cell rna-seq analysis reveals cell subsets and gene signatures associated with rheumatoid arthritis disease activity

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:17:04.193112Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5e1b902a-d9e3-43dc-8c05-d61fa4d7633c · outbound

This paper cites High- resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning High- resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:17:04.134146Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1f394338-bf2d-4b6b-bb56-c2bdea754bcd · outbound

This paper cites Cells of the human intestinal tract mapped across space and time.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Cells of the human intestinal tract mapped across space and time

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:17:04.080111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c2d467af-8292-455f-93ab-090062646b3b · outbound

This paper cites Persistent t cell unresponsiveness associated with chronic visceral leishmaniasis in hiv-coinfected patients.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Persistent t cell unresponsiveness associated with chronic visceral leishmaniasis in hiv-coinfected patients

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:17:03.954065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fe3d8965-122b-4830-8a66-84e20374ce99 · outbound

This paper cites Single-cell multi-omics analysis of human pancreatic islets reveals novel cellular states in type 1 diabetes.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Single-cell multi-omics analysis of human pancreatic islets reveals novel cellular states in type 1 diabetes

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:17:03.866585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d2eb3e37-3678-4444-a463-7af50a0c2974 · outbound

This paper cites An integrated cell atlas of the lung in health and disease.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning An integrated cell atlas of the lung in health and disease

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:17:03.771923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6df45982-5f83-4e13-bea6-fc152f5b731c · outbound

This paper cites Single-cell transcriptomics of the human retinal pigment epithelium and choroid in health and macular degeneration.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Single-cell transcriptomics of the human retinal pigment epithelium and choroid in health and macular degeneration

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:17:03.669440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cffba966-6346-4b2e-81fb-cc797b91df07 · outbound

This paper cites An atlas of healthy and injured cell states and niches in the human kidney.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning An atlas of healthy and injured cell states and niches in the human kidney

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:17:03.584176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c2558940-4ce9-43f5-8612-24154dc35015 · outbound

This paper cites Ovarian cancer mutational processes drive site-specific immune evasion.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Ovarian cancer mutational processes drive site-specific immune evasion

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:17:03.507627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c09b4e6b-7884-42c8-9393-e6082662b9bd · outbound

This paper cites Single-cell multiomics reveals increased plasticity, resistant populations, and stem-cell–like blasts in kmt2a-rearranged leukemia.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Single-cell multiomics reveals increased plasticity, resistant populations, and stem-cell–like blasts in kmt2a-rearranged leukemia

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:17:03.438412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a435344f-2d4c-4dfa-af24-a62a453ef8a4 · outbound

This paper cites Single-cell atlas of common variable immunodefi- ciency shows germinal center-associated epigenetic dysregulation in b-cell responses.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Single-cell atlas of common variable immunodefi- ciency shows germinal center-associated epigenetic dysregulation in b-cell responses

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:17:03.360178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 683ee068-e380-48ad-861d-1a6b66801ef5 · outbound

This paper cites Single-cell resolution characterization of myeloid-derived cell states with implication in cancer outcome.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Single-cell resolution characterization of myeloid-derived cell states with implication in cancer outcome

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:17:03.290176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3a89a888-d1ea-4b28-afad-320b2c929129 · outbound

This paper cites Distribution-independent cell type identification for single-cell rna-seq data.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Distribution-independent cell type identification for single-cell rna-seq data

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:17:03.225274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 90ad1f8f-8708-44ad-92a1-a114baebff62 · outbound

This paper cites Celler:A Genomic Language Model for Long-Tailed Single-Cell Annotation.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Celler:A Genomic Language Model for Long-Tailed Single-Cell Annotation

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:17:01.798590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1d78876e-2c5a-4db4-8bc7-69a775814f40 · outbound

This paper cites Trl: Transformer reinforce- ment learning.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Trl: Transformer reinforce- ment learning

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T11:17:00.590584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8fb9d8c-c22b-4bc9-a22a-5384a4e27167 · outbound

This paper cites Lora: Low-rank adaptation of large language models.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Lora: Low-rank adaptation of large language models

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T11:17:00.618313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84ca8230-ddc9-4526-b4e1-92b88305c321 · outbound

This paper cites Qwen2.5: A party of foundation models, September 2024.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Qwen2.5: A party of foundation models, September 2024

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:17:03.125187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 780c8b66-c312-430a-a861-bd189270c019 · outbound

This paper cites Qwen2 Technical Report.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Qwen2 Technical Report

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T11:17:00.827840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a9f7399-e5b9-461a-b03e-1e1fcbf73ffe · outbound

This paper cites HybridFlow: A Flexible and Efficient RLHF Framework.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning HybridFlow: A Flexible and Efficient RLHF Framework

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-07T11:17:00.927387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21b79bf0-b282-4cad-90d6-5d7256f32b32 · outbound

This paper cites Gonzalez, Hao Zhang, and Ion Stoica.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning Gonzalez, Hao Zhang, and Ion Stoica

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-07T11:17:01.042121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a5959f8-91ed-4d46-8caa-0833888a3c12 · outbound

This paper cites chain-of-thought.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning chain-of-thought

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:17:03.021654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a5b343b4-af07-43e3-86f4-c301089be1bd · outbound

This paper cites These genes alone are not sufficient for cell type identification.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning These genes alone are not sufficient for cell type identification

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:17:02.932795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b95fb062-9d1d-4ae4-a2be-d2003e91aec2 · outbound

This paper cites • Some cell types are more specific (e.g., IgA plasma cell, activated CD4-positive T cell), while others are broader (e.g., plasma cell, vein endothelial cell).

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning • Some cell types are more specific (e.g., IgA plasma cell, activated CD4-positive T cell), while others are broader (e.g., plasma cell, vein endothelial cell)

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:17:02.852002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 924e6c46-b86a-4c62-bbb5-89ed2d0e5b92 · outbound

This paper cites T-helper 1 cell.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning T-helper 1 cell

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:17:02.764815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

Observation 931a8e8e-ad3b-42e9-a1f7-a90113ca7e37 · inbound

Gene-R1: Reasoning with Data-Augmented Lightweight LLMs for Gene Set Analysis cites this paper.

Gene-R1: Reasoning with Data-Augmented Lightweight LLMs for Gene Set Analysis Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:59.629102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f3b5f4c-df96-4550-80b1-9a364ae5c934 · inbound

Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches cites this paper.

Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning

Reference 74

Resolution: verified exact; arxiv_id, observed 2026-07-01T20:56:14.440258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2f77efaf-05ed-45d9-b62b-22ddd243659e · inbound

Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches cites this paper.

Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning

Reference 80

Resolution: verified exact; arxiv_id, observed 2026-07-01T08:35:34.111016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.