[2604.06269] MAT-Cell: A Multi-Agent Tree-Structured Reasoning Framework for Batch-Level Single-Cell Annotation

Quantitative Biology > Quantitative Methods arXiv:2604.06269 (q-bio) [Submitted on 7 Apr 2026] Title:MAT-Cell: A Multi-Agent Tree-Structured Reasoning Framework for Batch-Level Single-Cell Annotation Authors:Yehui Yang, Zelin Zang, Changxi Chi, Jingbo Zhou, Xienan Zheng, Yuzhe Jia, Chang Yu, Jinlin Wu, Fuji Yang, Jiebo Luo, Zhen Lei, Stan Z. Li View a PDF of the paper titled MAT-Cell: A Multi-Agent Tree-Structured Reasoning Framework for Batch-Level Single-Cell Annotation, by Yehui Yang and 11 other authors View PDF HTML (experimental) Abstract:Automated cellular reasoning faces a core dichotomy: supervised methods fall into the Reference Trap and fail to generalize to out-of-distribution cell states, while large language models (LLMs), without grounded biological priors, suffer from a Signal-to-Noise Paradox that produces spurious associations. We propose MAT-Cell, a neuro-symbolic reasoning framework that reframes single-cell analysis from black-box classification into constructive, verifiable proof generation. MAT-Cell injects symbolic constraints through adaptive Retrieval-Augmented Generation (RAG) to ground neural reasoning in biological axioms and reduce transcriptomic noise. It further employs a dialectic verification process with homogeneous rebuttal agents to audit and prune reasoning paths, forming syllogistic derivation trees that enforce logical this http URL large-scale and cross-species benchmarks, MAT-Cell significantly outperforms state-of-the-art (SOTA) m...