Topic: cs.LG

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1. MEG-XL: Data-Efficient Brain-to-Text via Long-Context Pre-Training

   Paper • Feb 12, 2026 • arXiv • Dulhan Jayalath, Oiwi Parker Jones

   Decoding natural language from non-invasive brain recordings like Magnetoencephalography (MEG) remains a significant challenge due to the low signal-to-noise ratio and the scarcity of paired brain-...

2. High-accuracy sampling for diffusion models and log-concave distributions

   Paper • Feb 1, 2026 • arXiv • Fan Chen, Sinho Chewi, Constantinos Daskalakis, Alexander Rakhlin

   We present algorithms for diffusion model sampling which obtain δ-error in polylog(1/δ) steps, given access to eO(δ)-accurate score estimates in L2. This is an exponential improvement over all prev...

3. Learning Glioblastoma Tumor Heterogeneity Using Brain Inspired Topological Neural Networks

   Paper • Feb 12, 2026 • arXiv • Ankita Paul, Wenyi Wang

   Glioblastoma (GBM) is characterized by high intra-tumoral heterogeneity, which poses a significant challenge for diagnosis and treatment planning. We propose a brain-inspired Topological Neural Net...

4. KEPO: Knowledge-Enhanced Preference Optimization for Reinforcement Learning with Reasoning

   Paper • Feb 4, 2026 • arXiv • Fan Yang, Rui Meng, Trudi Di Qi, Ali Ezzati, Yuxin Wen

   Direct Preference Optimization (DPO) and its variants have revolutionized LLM alignment, yet they struggle when the preferred choice requires deep, multi-step reasoning. We introduce KEPO, a framew...