arXiv:2508.04581v2 Announce Type: replace-cross Abstract: Large language models have revolutionized AI applications, yet their high computational and memory demands hinder their widespread deployment. Existing compression techniques focus on intra-block optimizations (e.g., low-rank approximation or attention pruning), while the repetitive layered…
arXiv:2510.08570v2 Announce Type: replace Abstract: Neural networks are famously nonlinear. However, linearity is defined relative to a pair of vector spaces, $f:X to Y$. Leveraging the algebraic concept of transport of structure, we propose a method to explicitly identify non-standard…
arXiv:2601.11924v2 Announce Type: replace Abstract: We study cooperative stochastic multi-armed bandits with vector-valued rewards under adversarial corruption and limited verification. In each of $T$ rounds, each of $N$ agents selects an arm, the environment generates a clean reward vector, and…
arXiv:1803.09319v2 Announce Type: replace Abstract: Deep neural networks are often used to implement powerful generative models for real-world data. Notable applications include image denoising, as well as other classical inverse problems like compressed sensing and super-resolution. To provide a rigorous…
arXiv:2506.05647v4 Announce Type: replace Abstract: We study gradient-based data attribution, aiming to identify which training examples most influence a given output. Existing methods for this task either treat network parameters uniformly or rely on implicit weighting derived from Hessian approximations,…
arXiv:2602.17685v1 Announce Type: new Abstract: This paper addresses the challenge of multi target active debris removal (ADR) in Low Earth Orbit (LEO) by introducing a unified coelliptic maneuver framework that combines Hohmann transfers, safety ellipse proximity operations, and explicit refueling…
arXiv:2602.18148v1 Announce Type: cross Abstract: Inverse design, particularly geometric shape optimization, provides a systematic approach for developing high-performance nanophotonic devices. While numerous optimization algorithms exist, previous global approaches exhibit slow convergence and conversely local search strategies frequently become trapped in…
arXiv:2602.17683v1 Announce Type: new Abstract: Accurate short-term forecasting of vegetation dynamics is a key enabler for data-driven decision support in precision agriculture. Normalized Difference Vegetation Index (NDVI) forecasting from satellite observations, however, remains challenging due to sparse and irregular sampling…
arXiv:2602.17684v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has driven recent progress in code large language models by leveraging execution-based feedback from unit tests, but its scalability is fundamentally constrained by the availability and reliability of high-quality…
arXiv:2602.17682v1 Announce Type: new Abstract: Consistency-based generative models like Shortcut and MeanFlow achieve impressive results via a target-aware design for solving the Probability Flow ODE (PF-ODE). Typically, such methods introduce a target time $r$ alongside the current time $t$ to…
