arXiv:2605.04282v1 Announce Type: new
Abstract: Visual SLAM is a core component of spatial computing systems, yet deploying learned local feature extractors on microcontroller-class hardware remains challenging due to memory, bandwidth, and quantizati…
arXiv:2605.04325v1 Announce Type: new
Abstract: We introduce a unified theoretical framework for the rigorous analysis and systematic construction of deep neural networks (DNNs). This framework addresses a gap in existing theory by explicitly modeling…
arXiv:2605.04930v1 Announce Type: cross
Abstract: Despite theoretical advantages, causal methods for Gene Regulatory Network (GRN) inference from single-cell RNA-seq data consistently fail to match or outperform correlation-based baselines in many rea…
arXiv:2605.05118v1 Announce Type: cross
Abstract: Recently, Deng et al. (2026) proposed Generative Modeling via Drifting (GMD), a novel framework for generative tasks. This note presents an analysis of GMD through the lens of Wasserstein Gradient Flow…
arXiv:2605.05104v1 Announce Type: cross
Abstract: Materials science data collection can be expensive, making the reuse and long-term utility of datasets critical important for future discovery campaigns. In practice, researchers prioritize a subset of…
arXiv:2605.04363v1 Announce Type: new
Abstract: TabPFN has recently gained attention as a foundation model for tabular datasets, achieving strong performance by leveraging in-context learning on synthetic data. However, we find that TabPFN is vulnerab…
arXiv:2602.10144v2 Announce Type: replace
Abstract: Minimizing the inference cost and latency of foundation models has become a crucial area of research. Optimization approaches include theoretically lossless methods and others without accuracy guaran…
arXiv:2511.01553v2 Announce Type: replace
Abstract: AI systems on edge devices require online continual learning — adapting to non-stationary streams and unfamiliar classes without catastrophic forgetting — under strict power constraints. We present…
arXiv:2602.02924v2 Announce Type: replace
Abstract: Diffusion policy sampling enables reinforcement learning (RL) to represent multimodal action distributions beyond suboptimal unimodal Gaussian policies. However, existing diffusion-based RL methods p…
arXiv:2602.05890v2 Announce Type: replace
Abstract: Training reinforcement learning (RL) systems in real-world environments remains challenging due to noisy supervision and poor out-of-domain (OOD) generalization, especially in LLM post-training. Rece…