Can Lightweight LLM Agents Improve Spatial Transcriptomics Annotation?

Spatial transcriptomics (ST) links gene expression to tissue organization, yet automated annotation of spatial regions remains a persistent challenge. Recent studies have explored large language models (LLMs) for biological reasoning, but their applicability in low-compute, freetier settings is largely unexplored. We investigate whether lightweight LLM agents can improve ST annotation by integrating rule-based heuristics, prototype discovery, and multirole reasoning ( Analyst–Consensus–Reviewer ) within a unified agentic framework. Across six STARmap and MERFISH datasets, we benchmark single- and multi-agent variants using standard clustering and spatial coherence metrics (NMI, ARI, CHAOS, ASW). Our results show that small open-weight models such as llama3.2 and qwen3 match or slightly exceed deterministic baselines in cluster recovery, while producing more spatially consistent and interpretable predictions. These findings high-light the potential of modular LLM agents as resource-efficient components in future spatial omics annotation pipelines.