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cs.LG2026
In-Cell Learning: Language Models That Update Their Own Weights in Sequence Without Changing the File They Ship
Zifeng Liu, Yaxin Lu, Xuanhan Wu +6
A 4-bit quantized weight specifies a rounding cell rather than a single full-precision value. We introduce in-cell learning, a paradigm for writing new knowledge only within these…
cs.LG2025
Position: The Hidden Costs and Measurement Gaps of Reinforcement Learning with Verifiable Rewards
Fang Wu, Aaron Tu, Weihao Xuan +21
Reinforcement learning with verifiable rewards (RLVR) is a practical, scalable way to improve large language models on math, code, and other structured tasks. However, we argue tha…
cs.LG2025
MENDR: Manifold Explainable Neural Data Representations
Matthew Chen, Micky Nnamdi, Justin Shao +7
Foundation models for electroencephalography (EEG) signals have recently demonstrated success in learning generalized representations of EEGs, outperforming specialized models in v…