ncbi/CellPuzzles
Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning [!Note] Please refer to our repository and paper for more details. 🧠 Overview Cell type annotation is a key task in analyzing the heterogeneity of single-cell RNA sequencing data. Although recent foundation models automate this process, they typically annotate cells independently, without considering batch-level cellular context or providing explanatory reasoning. In… See the full description on the dataset page: https://huggingface.co/datasets/ncbi/CellPuzzles.
| Author / organization | ncbi |
|---|---|
| License (as declared) | other |
| Downloads | 125 (as reported by Hugging Face) |
| Likes | 3 |
| Created | 3 June 2025 |
| Last updated at source | 5 June 2025 |
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125 now (+1 since 2026-10-16). Counts as reported by the source, snapshotted daily by Silytics.
Connected in the knowledge graph
- mradermacher/Cell-o1-GGUFModel · used to train
Recent changes
- 17 Oct 2026 Description: Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning [!Note] Please refer to our repository and paper for more details. 🧠 Overview Cell type annotation is a key task in analyzing the heterogeneity of si → Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning [!Note] Please refer to our repository and paper for more details. 🧠 Overview Cell type annotation is a key task in analyzing the heterogeneity of si
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