Excited to share our @eccvconf paper: Resolution-Agnostic Neural Operators for Multi-Rate Sparse-View CT. We introduce Computed Tomography neural Operator (CTO), the first neural operator framework for sparse-view CT reconstruction. Sparse-view CT cuts radiation dose and scan time by taking fewer X-ray projections, but reconstruction then becomes ill-posed and needs a learned prior. Existing deep learning models are tied to one subsampling rate. However, this is not scalable since clinical protocols vary across organs and diagnostic purposes, so in practice you need a separate model for each subsampling rate. CTO instead learns a mapping between function spaces. Because a function has no fixed resolution, one model ingests sinograms at any subsampling rate and outputs high quality reconstructions, with no retraining required. On an average, CTO beats traditional unrolled CNN variational network by 3.42 dB PSNR and is 500x faster than diffusion models while being 6.02 dB PSNR better. @Caltech Paper: Project page: Code:

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VeriLoop E2 is now available, bringing verifier-governed reasoning to code, mathematics, and physics in a 27B open-weight model. 🤖 🏆 The evaluated E2 system scores 76.2% on SWE-bench Pro, 88.8% on Terminal-Bench 2.1, 98.3% on AIME 2026, 93.9% on GPQA Diamond, and 89.6% on Apex 2025. 🔁 VeriLoop-Governed Recurrence proposes candidates, verifies them externally, and commits only improvements without regression. 🧪 Two evidence-backed scientific demonstrations cover a 67.35% Riemann ζ zero-proportion certificate and Asymptotic Graviton Tomography. Neither claims to solve the underlying open problem. 📚 Built on Qwen3.8-27B with a 262K native context window and OpenAI-compatible vLLM serving validated at 131K. 📜 Model weights and public inference utilities: Apache 2.0. The production VeriLoop Harness is not included, and the scientific artifacts carry separate terms.