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Human brain is two separate organs, Stanford Medicine-led research finds
Stanford researchers discovered that the human brain develops from two separate progenitor lineages—one for forebrain/midbrain (Otx2) and one for hindbrain (Gbx2)—and used this insight to grow functional hindbrain neurons from pluripotent stem cells. This breakthrough provides a platform for studying brain‑stem diseases such as SMA and ALS and reshapes our understanding of brain evolution.
Hacker News front pagestanford.edu5 minreleaseHN654257Our brain evolved from two primitive nervous systems that merged: Study
A new study shows that the forebrain and hindbrain develop from distinct progenitor cells (OTX2‑expressing and GBX2‑expressing) in mouse, human, chicken, zebrafish and acorn worm embryos, suggesting the vertebrate brain originated from the merger of two primitive nervous systems over 550 M years ago. This insight enabled the first successful in‑vitro generation of human hindbrain motor neurons.
Hacker News front pagenewscientist.com3 minHN13275ScienceBuddy: Recursive-in-Recursive Self-Improvement for Interactive Scientific Agents
ScienceBuddy is an interactive workspace that converts researcher prompts, feedback, and execution traces into continual‑learning tasks for AI agents. It introduces a "recursive‑in‑recursive" self‑improvement loop that alternates harness refinement and model training, and showcases case studies across four scientific task families.
Hugging Face Daily Papersarxiv.org1 minpaperHN2Gricea: An Open Science Platform for Conversational AI Research
Gricea is an open‑science platform that packages conversational AI studies as reusable artifacts. It demonstrated high reproducibility on prior papers and enabled diverse users to build runnable experiments, addressing fragmentation in CAI research.
Hugging Face Daily Papersarxiv.org1 minpaperVABench: Measuring Embodied Spatial Intelligence through Visual Demonstrations, Active Perception, and Metric Control
VA‑Bench is a new benchmark that evaluates general‑purpose multimodal LLMs on the full observe‑reason‑act‑revise loop in embodied robotics, using RGB demonstrations, active camera control, and metric Cartesian commands. The best model reaches 53.9% average task success, showing active perception helps but long‑horizon tasks remain unsolved.
Hugging Face Daily Papersarxiv.org1 minpaperAssessing nnU-Net Generalization across Brain Tumor Populations in BraTS-GoAT 2026
Paper evaluates a standard 3‑D nnU‑Net on the new BraTS‑GoAT benchmark, training on 1,351 cases with five‑fold cross‑validation and test‑time mirroring. It reports Dice scores of 0.78/0.83/0.89 (ET/TC/WT) and shows a ~0.07 drop on heterogeneous validation, with limited benefit from ensembling or mirroring and failure linked to small, fragmented tumors.
Hugging Face Daily Papersarxiv.org1 minpaper
