arXiv:2602.20400v1 Announce Type: new Abstract: To steer language models towards truthful outputs on tasks which are beyond human capability, previous work has suggested training models on easy tasks to steer them on harder ones (easy-to-hard generalization), or using unsupervised training…
arXiv:2602.13314v3 Announce Type: replace-cross Abstract: Millimeter-wave (mmWave) radar provides reliable perception in visually degraded indoor environments (e.g., smoke, dust, and low light), but learning-based radar perception is bottlenecked by the scarcity and cost of collecting and annotating large-scale radar datasets.…
Strahinja Janjusevic brings an international perspective and US Naval Academy education to his graduate research in the MIT Technology and Policy Program.
arXiv:2507.19575v2 Announce Type: replace-cross Abstract: Data scarcity is a major challenge in medical imaging, particularly for deep learning models. While data pooling (combining datasets from multiple sources) and data addition (adding more data from a new dataset) have been shown…
arXiv:2602.15997v3 Announce Type: replace Abstract: Capability emergence during neural network training remains mechanistically opaque. We track five geometric measures across five model scales (405K–85M parameters), 120 task$times$level$times$ model combinations (119 achieving accuracy-based emergence) across eight algorithmic tasks, and three Pythia…
arXiv:2508.01115v2 Announce Type: replace Abstract: User attributes are essential in multiple stages of modern recommendation systems and are particularly important for mitigating the cold-start problem and improving the experience of new or infrequent users. We propose Behavior-based User Segmentation (BUS),…
arXiv:2511.03475v3 Announce Type: replace Abstract: AI applications increasingly depend on long-context inference, where LLMs consume substantial context to support stronger reasoning. Common examples include retrieval-augmented generation, agent memory layers, and multi-agent orchestration. As input contexts get longer, prefill latency becomes…
arXiv:2602.21144v1 Announce Type: cross Abstract: Selective state space models (SSMs) have rapidly become a compelling backbone for large language models, especially for long-context workloads. Yet in deployment, their inference performance is often bounded by the memory capacity, bandwidth, and latency…
arXiv:2503.00229v4 Announce Type: replace Abstract: Armijo line-search (Armijo-LS) is a standard method to set the step-size for gradient descent (GD). For smooth functions, Armijo-LS alleviates the need to know the global smoothness constant L and adapts to the “local” smoothness,…
arXiv:2602.20210v1 Announce Type: new Abstract: Crystal modeling spans a family of conditional and unconditional generation tasks across different modalities, including crystal structure prediction (CSP) and emph{de novo} generation (DNG). While recent deep generative models have shown promising performance, they remain…
