arXiv:2602.18982v2 Announce Type: replace-cross Abstract: Common deep learning approaches for antibody engineering focus on modeling the marginal distribution of sequences. By treating sequences as independent samples, however, these methods overlook affinity maturation as a rich and largely untapped source of information about the evolutionary process by which antibodies explore the underlying fitness landscape. In contrast, […]
K-MaT: Knowledge-Anchored Manifold Transport for Cross-Modal Prompt Learning in Medical Imaging
arXiv:2603.06340v1 Announce Type: cross Abstract: Large-scale biomedical vision-language models (VLMs) adapted on high-end imaging (e.g., CT) often fail to transfer to frontline low-end modalities (e.g., radiography), collapsing into modality-specific shortcuts. We propose K-MaT (Knowledge-Anchored Manifold Transport), a prompt-learning framework that transfers decision structures to low-end modalities without requiring low-end training images. K-MaT factorizes prompts, anchors […]
DEX-AR: A Dynamic Explainability Method for Autoregressive Vision-Language Models
arXiv:2603.06302v1 Announce Type: cross Abstract: As Vision-Language Models (VLMs) become increasingly sophisticated and widely used, it becomes more and more crucial to understand their decision-making process. Traditional explainability methods, designed for classification tasks, struggle with modern autoregressive VLMs due to their complex token-by-token generation process and intricate interactions between visual and textual modalities. We present […]
MatRIS: Toward Reliable and Efficient Pretrained Machine Learning Interatomic Potentials
arXiv:2603.02002v3 Announce Type: replace-cross Abstract: Foundation MLIPs demonstrate broad applicability across diverse material systems and have emerged as a powerful and transformative paradigm in chemical and computational materials science. Equivariant MLIPs achieve state-of-the-art accuracy in a wide range of benchmarks by incorporating equivariant inductive bias. However, the reliance on tensor products and high-degree representations makes […]
Learning Where the Physics Is: Probabilistic Adaptive Sampling for Stiff PDEs
arXiv:2603.06287v1 Announce Type: cross Abstract: Modeling stiff partial differential equations (PDEs) with sharp gradients remains a significant challenge for scientific machine learning. While Physics-Informed Neural Networks (PINNs) struggle with spectral bias and slow training times, Physics-Informed Extreme Learning Machines (PIELMs) offer a rapid, closed-form linear solution but are fundamentally limited by physics-agnostic, random initialization. We […]
LTLGuard: Formalizing LTL Specifications with Compact Language Models and Lightweight Symbolic Reasoning
arXiv:2603.05728v1 Announce Type: cross Abstract: Translating informal requirements into formal specifications is challenging due to the ambiguity and variability of natural language (NL). This challenge is particularly pronounced when relying on compact (small and medium) language models, which may lack robust knowledge of temporal logic and thus struggle to produce syntactically valid and consistent formal […]
Balancing Domestic and Global Perspectives: Evaluating Dual-Calibration and LLM-Generated Nudges for Diverse News Recommendation
arXiv:2603.05780v1 Announce Type: cross Abstract: In this study, we applied the “personalized diversity nudge framework” with the goal of expanding user reading coverage in terms of news locality (i.e., domestic and world news). We designed a novel topic-locality dual calibration algorithmic nudge and a large language model-based news personalization presentation nudge, then launched a 5-week […]
Classroom AI: Large Language Models as Grade-Specific Teachers
arXiv:2601.06225v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) offer a promising solution to complement traditional teaching and address global teacher shortages that affect hundreds of millions of children, but they fail to provide grade-appropriate responses for students at different educational levels. We introduce a framework for finetuning LLMs to generate age-appropriate educational content across […]
Agentic retrieval-augmented reasoning reshapes collective reliability under model variability in radiology question answering
arXiv:2603.06271v1 Announce Type: cross Abstract: Agentic retrieval-augmented reasoning pipelines are increasingly used to structure how large language models (LLMs) incorporate external evidence in clinical decision support. These systems iteratively retrieve curated domain knowledge and synthesize it into structured reports before answer selection. Although such pipelines can improve performance, their impact on reliability under model variability […]
DataChef: Cooking Up Optimal Data Recipes for LLM Adaptation via Reinforcement Learning
arXiv:2602.11089v2 Announce Type: replace-cross Abstract: In the current landscape of Large Language Models (LLMs), the curation of large-scale, high-quality training data is a primary driver of model performance. A key lever is the emphdata recipe, which comprises a data processing pipeline to transform raw sources into training corpora. Despite the growing use of LLMs to […]
HiPP-Prune: Hierarchical Preference-Conditioned Structured Pruning for Vision-Language Models
arXiv:2603.06270v1 Announce Type: cross Abstract: Pruning vision-language models (VLMs) for efficient deployment is challenging because compression can affect not only task utility but also visual grounding, often amplifying object hallucinations even at the same sparsity level. We present HiPP-Prune, a hierarchical preference-conditioned structured pruning framework that treats pruning as conditional resource allocation under multiple objectives. […]
Towards Autonomous Mathematics Research
arXiv:2602.10177v3 Announce Type: replace-cross Abstract: Recent advances in foundational models have yielded reasoning systems capable of achieving a gold-medal standard at the International Mathematical Olympiad. The transition from competition-level problem-solving to professional research, however, requires navigating vast literature and constructing long-horizon proofs. In this work, we introduce Aletheia, a math research agent that iteratively generates, […]