arXiv:2512.08074v3 Announce Type: replace Abstract: Virtually every biological rate depends on temperature, yet the resulting rate-temperature relationships often deviate strongly from simple Arrhenius behavior. In this first part of a two-part review, we survey phenomenological models used to describe biological temperature responses across scales, from enzymatic reactions to organismal performance. We discuss common functional forms, […]
MIL-PF: Multiple Instance Learning on Precomputed Features for Mammography Classification
arXiv:2603.09374v1 Announce Type: cross Abstract: Modern foundation models provide highly expressive visual representations, yet adapting them to high-resolution medical imaging remains challenging due to limited annotations and weak supervision. Mammography, in particular, is characterized by large images, variable multi-view studies and predominantly breast-level labels, making end-to-end fine-tuning computationally expensive and often impractical. We propose Multiple […]
Sparse Variational Student-t Processes for Heavy-tailed Modeling
arXiv:2408.06699v2 Announce Type: replace-cross Abstract: The Gaussian process (GP) is a powerful tool for nonparametric modeling, but its sensitivity to outliers limits its applicability to data distributions with heavy-tails. Studentt processes offer a robust alternative for heavy tail modeling, but they lack the scalable developments of the GP to large datasets necessary for practical applications. […]
The Geometric Inductive Bias of Grokking: Bypassing Phase Transitions via Architectural Topology
arXiv:2603.05228v2 Announce Type: replace-cross Abstract: Mechanistic interpretability typically relies on post-hoc analysis of trained networks. We instead adopt an interventional approach: testing hypotheses a priori by modifying architectural topology to observe training dynamics. We study grokking – delayed generalization in Transformers trained on cyclic modular addition (Zp) – investigating if specific architectural degrees of freedom […]
OPENXRD: A Comprehensive Benchmark Framework for LLM/MLLM XRD Question Answering
arXiv:2507.09155v2 Announce Type: replace-cross Abstract: We introduce OPENXRD, a comprehensive benchmarking framework for evaluating large language models (LLMs) and multimodal LLMs (MLLMs) in crystallography question answering. The framework measures context assimilation, or how models use fixed, domain-specific supporting information during inference. The framework includes 217 expert-curated X-ray diffraction (XRD) questions covering fundamental to advanced crystallographic […]
M3GCLR: Multi-View Mini-Max Infinite Skeleton-Data Game Contrastive Learning For Skeleton-Based Action Recognition
arXiv:2603.09367v1 Announce Type: cross Abstract: In recent years, contrastive learning has drawn significant attention as an effective approach to reducing reliance on labeled data. However, existing methods for self-supervised skeleton-based action recognition still face three major limitations: insufficient modeling of view discrepancies, lack of effective adversarial mechanisms, and uncontrollable augmentation perturbations. To tackle these issues, […]
RECODE: Reasoning Through Code Generation for Visual Question Answering
arXiv:2510.13756v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) struggle with precise reasoning for structured visuals like charts and diagrams, as pixel-based perception lacks a mechanism for verification. To address this, we propose to leverage derendering — the process of reverse-engineering visuals into executable code — as a new modality for verifiable visual reasoning. […]
SPARC: Spatial-Aware Path Planning via Attentive Robot Communication
arXiv:2603.02845v2 Announce Type: replace-cross Abstract: Efficient communication is critical for decentralized Multi-Robot Path Planning (MRPP), yet existing learned communication methods treat all neighboring robots equally regardless of their spatial proximity, leading to diluted attention in congested regions where coordination matters most. We propose Relation enhanced Multi Head Attention (RMHA), a communication mechanism that explicitly embeds […]
Enhancing Retrieval-Augmented Generation with Entity Linking for Educational Platforms
arXiv:2512.05967v2 Announce Type: replace-cross Abstract: In the era of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) architectures are gaining significant attention for their ability to ground language generation in reliable knowledge sources. Despite their effectiveness, RAG systems based solely on semantic similarity often fail to ensure factual accuracy in specialized domains, where terminological ambiguity can […]
Democratising Clinical AI through Dataset Condensation for Classical Clinical Models
arXiv:2603.09356v1 Announce Type: cross Abstract: Dataset condensation (DC) learns a compact synthetic dataset that enables models to match the performance of full-data training, prioritising utility over distributional fidelity. While typically explored for computational efficiency, DC also holds promise for healthcare data democratisation, especially when paired with differential privacy, allowing synthetic data to serve as a […]
REAP the Experts: Why Pruning Prevails for One-Shot MoE compression
arXiv:2510.13999v2 Announce Type: replace-cross Abstract: Sparsely-activated Mixture-of-Experts (SMoE) models offer efficient pre-training and low latency but their large parameter counts create significant memory overhead, motivating research into expert compression. Contrary to recent findings favouring expert merging on discriminative benchmarks, we find that expert pruning is a superior strategy for generative tasks. We demonstrate that existing […]
Reasoning as Gradient: Scaling MLE Agents Beyond Tree Search
arXiv:2603.01692v2 Announce Type: replace-cross Abstract: LLM-based agents for machine learning engineering (MLE) predominantly rely on tree search, a form of gradient-free optimization that uses scalar validation scores to rank candidates. As LLM reasoning capabilities improve, exhaustive enumeration becomes increasingly inefficient compared to directed updates, analogous to how accurate gradients enable efficient descent over random search. […]