very comprehensive Review on single-cell foundation models from @JiayuanDing, @Xiaojie_Qiu, @TheodorisLab, and colleagues. After going over it, I would encourage the authors to make it more critical rather than primarily an inventory of models. First, I think Fig 1 needs some rethinking. The definition of scFM has become too loose. For example, the actual model unit of GET is celltype pseudobulk aggregated from scATAC-seq, hence not single cell model. Also thanks for highlighting our TissueNarrator, but we certainly do not consider it a bona fide scFM - it is a specialized spatial transcriptomics model built by leverging an existing foundation LLM (Qwen). TissueNarrator is very interesting and we re having lots of fun w/ it but it's not a scFM. Anyway - Fig 1 seems to mix FMs, specialized models built on FMs, and task-specific models into one big family tree. More broadly, I think the more interesting question for a scFM review in 2026 is not how many scFMs now exist or how their architectures differ, but what has the scFM paradigm actually bought us? Since the first wave around scBERT/Geneformer/scGPT, the number of models, parameters, and pretraining cells has exploded, but as a field we are not convinced that capabilities have advanced proportionally. - Where has large scale pretraining enabled something that a strong specialized model could not do ? - Where do scFMs consistently beat strong task specific or even simple baselines? - Where is the broad evidence for true cross-context generalization on biologically interesting tasks? The Review mentions many of these challenges indeed, but I think they should be much closer to the central theme of the article rather than appearing mainly in a later Challenges section :-) I might almost suggest to flip the review around and instead ask: - what have we actually learned after several years and many scFMs? - What claims have survived independent benchmarking? - What has scaled and what has saturated? - What capabilities are genuinely attributable to pretraining and where do we still need much more evidnece? - What important biological problems remain essentially unsolved by scFMs ? Given the current rate of model proliferation, there may be another 50 scFMs by this time next year and this inventory / figure will become outdated very quickly. To me, a more useful and timely "state of the field" critique would be much more valuable and long-lasting than another "model zoo" review .

As a collaboration between my group at @SCSatCMU, Aviv Regev's team at @genentech, and @michellearning's team at @medra_ai, we are pleased to share our Perspective, "Towards human-led, agent-driven autonomous laboratories for the life sciences." 🤖 preprints.org/manuscript/202… This preprint release is coordinated with a complementary Perspective by @MengdiWang10, @lecong, and colleagues: preprints.org/manuscript/202…. Together, the two Perspectives examine the future of autonomous science from complementary angles. Our Perspective was Led by @WenduoC in my group, with @mishamamq, @ShenShuaik4260 and @zocean636 at CMU; Anna Hupalowska, Jennifer Rood, Christine Bakan, and Aviv Regev at @genentech/@Roche; and Gaurav Agrawal and @michellearning at @medra_ai. 🚀preprints.org/manuscript/202…

Very excited to share our @ScienceMagazine paper on single-cell #3D #genome reorganization in #Alzheimer's disease. We jointly measured gene expression and 3D genome architecture in individual human brain cells using #GAGEseq, then integrated these data w/ chromatin accessibility and spatial transcriptomics. We uncovered increased #compartment #mingling and distance-dependent rewiring of gene regulatory contacts in AD. We also developed #Hicformer, a transformer-based model that integrates DNA sequence with 3D genome features to predict cell type-specific gene expression and prioritize candidate regulatory elements. Huge kudos to co-first authors @zocean636 and @xinyuelu1999; and many thanks to Zhijun Duan @UW, Hansruedi Mathys @PittTweet, & David Bennett @rushalzheimers for the wonderful collaboration, as well as to all our co-authors. @CarnegieMellon @SCSatCMU @CMUCompBio #AlzheimersDisease #3DGenome #SingleCell #AI science.org/doi/10.1126/scie…

Happy to share #DNALONGBENCH @NatureComms. We challenge DNA #FoundationModels w/ long-range sequence context and hope this sparks more meaningful ways to evaluate growing AI+BIO models. Kudos to @WenduoC @ZhenqiaoSong @zocean636. Great collab w/ @lileics. nature.com/articles/s41467-0…

Final version of our paper @eLife (led by @belmont_andrew): Major nuclear locales define nuclear genome organization and function beyond A and B compartments. elifesciences.org/articles/9…

Proper and meaningful benchmark datasets are crucial for advancing genomic LLMs/FMs, and ML methods for genomics in general. Fantastic collab w/ @lileics's group. Amazing work led by @WenduoC @ZhenqiaoSong @zocean636