arXiv:2602.17689v1 Announce Type: new Abstract: Medical vision-language models show strong potential for joint reasoning over medical images and clinical text, but their performance often degrades under domain shift caused by variations in imaging devices, acquisition protocols, and reporting styles. Existing…
arXiv:2503.19605v4 Announce Type: replace Abstract: Understanding and certifying the generalization performance of machine learning algorithms — i.e. obtaining theoretical estimates of the test error from a finite training sample — is a central theme of statistical learning theory. Among the…
arXiv:2602.17691v1 Announce Type: new Abstract: Quantized language models face a fundamental dilemma: low sampling temperatures yield repetitive, mode-collapsed outputs, while high temperatures (T > 2.0) cause trajectory divergence and semantic incoherence. We present HELIX, a geometric framework that decouples output…
arXiv:2509.06484v2 Announce Type: replace Abstract: The excess Gibbs energy plays a central role in chemical engineering and chemistry, providing a basis for modeling thermodynamic properties of liquid mixtures. Predicting the excess Gibbs energy of multi-component mixtures solely from molecular structures…
arXiv:2602.17692v1 Announce Type: new Abstract: In this paper, we introduce textbf{agentic unlearning} which removes specified information from both model parameters and persistent memory in agents with closed-loop interaction. Existing unlearning methods target parameters alone, leaving two critical gaps: (i) parameter-memory…
arXiv:2511.19269v2 Announce Type: replace Abstract: Diffusion Language Models (DLMs) offer a promising parallel generation paradigm but suffer from slow inference due to numerous refinement steps and the inability to use standard KV caching. We introduce CDLM (Consistency Diffusion Language Models),…
arXiv:2602.17693v1 Announce Type: new Abstract: Post-Training Quantization (PTQ) is crucial for efficient model deployment, yet its effectiveness on Ascend NPU remains under-explored compared to GPU architectures. This paper presents a case study of representative PTQ baselines applied to reasoning-oriented models…
arXiv:2602.15997v2 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+ emergence events in eight algorithmic tasks, and three Pythia language models (160M-2.8B). We find:…
arXiv:2602.17694v1 Announce Type: new Abstract: With the rapid development of large language models (LLMs), an increasing number of applications leverage cloud-based LLM APIs to reduce usage costs. However, since cloud-based models’ parameters and gradients are agnostic, users have to manually…
arXiv:2505.11228v4 Announce Type: replace-cross Abstract: The spreading dynamics in social networks are often studied under the assumption that individuals’ statuses, whether informed or infected, are fully observable. However, in many real-world situations, such statuses remain unobservable, which is crucial for…
