Stanford researcher: single-cell encoding works fine without billion-parameter foundation models
anshulkundaje · x · 2026-11-12
Two closing points from Anshul Kundaje in an academic thread:
- He argues that cell state–conditioned architectures offer a natural way to unify sequence models with multimodal cell state encoding models.
- For "virtual cell" enthusiasts, he notes an empirical observation: the single-cell encoder in this work was just a tweak on the classic scVI and performed great — no billion-parameter hyperscaled encoder-decoder "foundation world model" needed.
More from Research
- NeurIPS paper proves when RNN+MLP can provably recover true robot state — chriswolfvision · 2026-11-12
- Curated 9-paper reading list for LLM quantization beginners, from QLoRA to BitNet — East-Muffin-6472 · 2026-11-12
- A Quick Diagnostic Guide to 4 Common ML Training Errors and Their Causes — cneuralnetwork · 2026-11-12
- Anthropic's Lance Martin Scaled 1,000 Claude Agents to Formalize Fermat's Last Theorem — FinanceYF5 · 2026-11-12
- DLS runs 1.5x-2.4x faster than tangermeme on A100, verified within numerical precision — anshulkundaje · 2026-11-12
- Vertex exec calls AI-cures-all claims "complete BS": 50 AI firms, zero working drugs — AlexTensor · 2026-11-12