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…
arXiv:2602.20217v1 Announce Type: new Abstract: Self-speculative decoding (SSD) accelerates LLM inference by skipping layers to create an efficient draft model, yet existing methods often rely on static heuristics that ignore the dynamic computational overhead of attention in long-context scenarios. We…
arXiv:2602.20207v1 Announce Type: new Abstract: Knowledge editing in Large Language Models (LLMs) aims to update the model’s prediction for a specific query to a desired target while preserving its behavior on all other inputs. This process typically involves two stages:…
arXiv:2602.20208v1 Announce Type: new Abstract: Model merging aims to integrate multiple task-specific fine-tuned models derived from a shared pre-trained checkpoint into a single multi-task model without additional training. Despite extensive research, task interference remains a major obstacle that often undermines…
