SpatialBench: Can Agents Analyze Real-World Spatial Biology Data?
This addresses the bottleneck in computational analysis for biologists using spatial transcriptomics, though it is incremental as it focuses on benchmarking rather than a new method.
The authors tackled the problem of evaluating AI agents' ability to analyze real-world spatial biology data by introducing SpatialBench, a benchmark of 146 verifiable problems derived from practical workflows, and found that base model accuracy remains low (20-38% across model families).
Spatial transcriptomics assays are rapidly increasing in scale and complexity, making computational analysis a major bottleneck in biological discovery. Although frontier AI agents have improved dramatically at software engineering and general data analysis, it remains unclear whether they can extract biological insight from messy, real-world spatial datasets. We introduce SpatialBench, a benchmark of 146 verifiable problems derived from practical spatial analysis workflows spanning five spatial technologies and seven task categories. Each problem provides a snapshot of experimental data immediately prior to an analysis step and a deterministic grader that evaluates recovery of a key biological result. Benchmark data on frontier models shows that base model accuracy remains low (20-38% across model families), with strong model-task and model-platform interactions. Harness design has a large empirical effect on performance, indicating that tools, prompts, control flow, and execution environment should be evaluated and improved as first-class objects. SpatialBench serves both as a measurement tool and a diagnostic lens for developing agents that can interact with real spatial datasets faithfully, transparently, and reproducibly.