# GRIT: Graph-Regularized Logit Refinement for Zero-shot Cell Type Annotation

arXiv: https://arxiv.org/abs/2508.04747 · HTML: https://mlsanity.com/paper/2508.04747/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 57 references that are in the map.
- First posted on arXiv 6 Aug 2025.

## Builds on (most cited first, 15 of 15)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [MedCLIP: Contrastive Learning from Unpaired Medical Images and Text](https://mlsanity.com/paper/2210.10163/) (2022)
- [Med-Flamingo: a Multimodal Medical Few-shot Learner](https://mlsanity.com/paper/2307.15189/) (2023)
- [BioCLIP: A Vision Foundation Model for the Tree of Life](https://mlsanity.com/paper/2311.18803/) (2023)
- [Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems](https://mlsanity.com/paper/1905.04413/) (2019)
- [A ChatGPT Aided Explainable Framework for Zero-Shot Medical Image Diagnosis](https://mlsanity.com/paper/2307.01981/) (2023)
- [Intra-Modal Proxy Learning for Zero-Shot Visual Categorization with CLIP](https://mlsanity.com/paper/2310.19752/) (2023)
- [LangCell: Language-Cell Pre-training for Cell Identity Understanding](https://mlsanity.com/paper/2405.06708/) (2024)
- [PCA Reduced Gaussian Mixture Models with Applications in Superresolution](https://mlsanity.com/paper/2009.07520/) (2020)
- [GeneFormer: Learned Gene Compression using Transformer-based Context Modeling](https://mlsanity.com/paper/2212.08379/) (2022)
- [KPL: Training-Free Medical Knowledge Mining of Vision-Language Models](https://mlsanity.com/paper/2501.11231/) (2025)
- [Graph Neural Network approaches for single-cell data: A recent overview](https://mlsanity.com/paper/2310.09561/) (2023)

## Abstract (arXiv)

Cell type annotation is a fundamental step in the analysis of single-cell RNA sequencing (scRNA-seq) data. In practice, human experts often rely on the structure revealed by principal component analysis (PCA) followed by k-nearest neighbor (k-NN) graph construction to guide annotation. While effective, this process is labor-intensive and does not scale to large datasets. Recent advances in CLIP-style models offer a promising path toward automating cell type annotation. By aligning scRNA-seq profiles with natural language descriptions, models like LangCell enable zero-shot annotation. While LangCell demonstrates decent zero-shot performance, its predictions remain suboptimal. In this paper, we propose a principled inference-time paradigm for zero-shot cell type annotation (GRIT) which bridges the scalability of pre-trained foundation models with the structural robustness relied upon in human expert annotation workflows. Specifically, we enforce local consistency of the zero-shot CLIP logits over the task-specific PCA-based k-NN graph. We evaluate our approach on 14 annotated human scRNA-seq datasets from 4 distinct studies, spanning 11 organs and over 200,000 single cells. Our method consistently improves zero-shot annotation accuracy, achieving accuracy gains of up to 10%. Further analysis showcase the mechanism by which GRIT effectively propagates correct signals through the graph, pulling back mislabeled cells toward more accurate predictions. The method is training-free, model-agnostic, and serves as a simple yet effective plug-in for enhancing zero-shot cell type annotation.