arXiv:2508.21438v2 Announce Type: replace Abstract: The development of continuous biomanufacturing processes requires robust and early anomaly detection, since even minor deviations can compromise yield and stability, leading to disruptions in scheduling, reduced weekly production, and diminished economic performance. These processes…
arXiv:2511.15487v2 Announce Type: replace Abstract: Implicit Neural Representations (INRs) parameterize continuous signals via multilayer perceptrons (MLPs), enabling compact, resolution-independent modeling for tasks like image, audio, and 3D reconstruction. However, fitting high-resolution signals demands optimizing over millions of coordinates, incurring prohibitive…
By leveraging idle computing time, researchers can double the speed of model training while preserving accuracy.
MIT researchers uncovered the physics behind bubble-removing membranes that could improve bioreactors, chemical production, and more.
arXiv:2506.10947v2 Announce Type: replace-cross Abstract: We show that reinforcement learning with verifiable rewards (RLVR) can elicit strong mathematical reasoning in certain language models even with spurious rewards that have little, no, or even negative correlation with the correct answer. For…
arXiv:2602.16642v3 Announce Type: replace Abstract: Neural Collapse (NC) refers to the emergence of highly symmetric geometric structures in the representations of deep neural networks during the terminal phase of training. Despite its prevalence, the theoretical understanding of NC remains limited.…
arXiv:2404.07849v2 Announce Type: replace-cross Abstract: This work demonstrates that applying a fixed-effect multiple linear regression (MLR) model to an overparameterized dataset is mathematically equivalent to fitting a hyper-curve parameterized by a single scalar. This reformulation shifts the focus from global…
By leveraging idle computing time, researchers can double the speed of model training while preserving accuracy.
MIT researchers uncovered the physics behind bubble-removing membranes that could improve bioreactors, chemical production, and more.
arXiv:2506.10947v2 Announce Type: replace-cross Abstract: We show that reinforcement learning with verifiable rewards (RLVR) can elicit strong mathematical reasoning in certain language models even with spurious rewards that have little, no, or even negative correlation with the correct answer. For…
