空间生物学研究者终于有了衡量AI科学推理能力的硬核基准——SpatialBench-Long 要求智能体从复杂空间数据中推导真实结论，而非简单跑流程。做生物信息学或AI for Science的团队，值得看看当前模型的表现差距在哪里。
研究人员推出 SpatialBench-Long 基准测试，专门评估 AI 智能体在空间生物学中的长程科学推理能力。该基准包含 24 个评估任务，涵盖胰腺癌、胶质母细胞瘤、肺癌等多种疾病模型，涉及 CosMx、Visium、Xenium 等多种空间转录组学技术。任务要求智能体从原始或近原始数据中恢复生物学结论，而非执行预设分析流程。当前最佳模型（Gemini 3.5 Flash、GPT-5.5 等）在 72 次运行中仅完成 8 次（11.1%），表明该任务极具挑战性。该基准通过确定性评分和专家审查确保结果可靠性。
Verifiable Benchmarking of Long-Horizon Spatial Biology
AI agents are increasingly useful for biological data analysis, but existing benchmarks mostly test broad biological knowledge, executable workflows, or localized analysis steps rather than end-to-end scientific reasoning over spatial measurements. We introduce SpatialBench-Long, a benchmark for long-horizon spatial biology in which agents must recover biological claims from raw or near-raw data and calibrated experimental context without prescribed methods. SpatialBench-Long contains 24 evaluations across primary pancreatic ductal adenocarcinoma (PDAC), engineered glioblastoma organoids and in vivo tumors, Cas9 lineage-traced lung adenocarcinoma, and mouse optic nerve aging/intervention systems, spanning CosMx, Visium, Xenium, multiplexed error-robust fluorescence in situ hybridization (MERFISH), single-cell RNA sequencing (scRNA-seq), Slide-seq, Slide-tags, histology, and lineage-recording data. Candidate claims are hardened through reproduction, independent scientist review, and trajectory inspection. Final answers are graded deterministically over controlled vocabularies and symbols with companion rubrics capturing progress through key analysis chokepoints. Across the SpatialBench-Long benchmark, three model-harness pairs tie at 8/72 runs (11.1\%): Gemini 3.5 Flash / Pi terminal coding harness, GPT-5.5 / Pi, and GPT-5.5 / OpenAI Codex. SpatialBench-Long tests whether agents can move beyond executing procedural analysis to deriving accurate scientific conclusions from complex spatial measurements.