arXiv:2603.04177v1 Announce Type: cross Abstract: Large language model (LLM) coding agents can generate working code, but their solutions often accumulate complexity, duplication, and architectural debt. Human developers address such issues through refactoring: behavior-preserving program transformations that improve structure and maintainability.…
arXiv:2603.03529v1 Announce Type: new Abstract: We introduce mlx-snn, the first spiking neural network (SNN) library built natively on Apple’s MLX framework. As SNN research grows rapidly, all major libraries — snnTorch, Norse, SpikingJelly, Lava — target PyTorch or custom backends,…
arXiv:2603.04317v1 Announce Type: cross Abstract: Recent work interprets the linear recoverability of geographic and temporal variables from large language model (LLM) hidden states as evidence for world-like internal representations. We test a simpler possibility: that much of the relevant structure…
arXiv:2603.03530v1 Announce Type: new Abstract: Frozen self-supervised representations often transfer well with only a few labels across many semantic tasks. We argue that a single geometric quantity, emph{directional} CDNV (decision-axis variance), sits at the core of two favorable behaviors: strong…
arXiv:2603.04385v1 Announce Type: cross Abstract: Feed-forward transformer models have driven rapid progress in 3D vision, but state-of-the-art methods such as VGGT and $pi^3$ have a computational cost that scales quadratically with the number of input images, making them inefficient when…
arXiv:2603.03531v1 Announce Type: new Abstract: Accurate prediction of terrestrial ecosystem carbon fluxes (e.g., CO$_2$, GPP, and CH$_4$) is essential for understanding the global carbon cycle and managing its impacts. However, prediction remains challenging due to strong spatiotemporal heterogeneity: ecosystem flux…
arXiv:2411.15272v2 Announce Type: replace Abstract: In subpopulation shift scenarios, a Curriculum Learning (CL) approach would only serve to imprint the model weights, early on, with the easily learnable spurious correlations featured. To the best of our knowledge, none of the…
arXiv:2603.03535v1 Announce Type: new Abstract: While large language models (LLMs) fine-tuned with lightweight adapters achieve strong performance across diverse tasks, their performance on individual tasks depends on the fine-tuning strategy. Fusing independently trained models with different strengths has shown promise…
arXiv:2505.12506v4 Announce Type: replace Abstract: We propose a novel unsupervised framework for emph{Invariant Risk Minimization} (IRM), extending the concept of invariance to settings where labels are unavailable. Traditional IRM methods rely on labeled data to learn representations that are robust…
arXiv:2603.03538v1 Announce Type: new Abstract: Large language models with chain-of-thought generation have demonstrated great potential for producing complex mathematical proofs. However, their reasoning can often go astray, leading to increasing interest in formal and learned verifiers. A major challenge in…
