arXiv:2604.03473v1 Announce Type: cross Abstract: Uncertainty quantification (UQ) methods for large language models are predominantly designed by hand based on domain knowledge and heuristics, limiting their scalability and generality. We apply LLM-powered evolutionary search to automatically discover unsupervised UQ methods represented as Python programs. On the task of atomic claim verification, our evolved methods outperform […]
MAVEN: A Mesh-Aware Volumetric Encoding Network for Simulating 3D Flexible Deformation
arXiv:2604.04474v1 Announce Type: cross Abstract: Deep learning-based approaches, particularly graph neural networks (GNNs), have gained prominence in simulating flexible deformations and contacts of solids, due to their ability to handle unstructured physical fields and nonlinear regression on graph structures. However, existing GNNs commonly represent meshes with graphs built solely from vertices and edges. These approaches […]
Representation learning to advance multi-institutional studies with electronic health record data from US and France
arXiv:2502.08547v2 Announce Type: replace Abstract: The widespread adoption of electronic health records has created new opportunities for translational clinical research, yet this promise remains constrained by fragmented data across privacy-siloed institutions and substantial heterogeneity in local coding practices. While privacy-preserving collaborative learning allows institutions to work together without sharing patient-level data, it does not address […]
Hume’s Representational Conditions for Causal Judgment: What Bayesian Formalization Abstracted Away
arXiv:2604.03387v1 Announce Type: new Abstract: Hume’s account of causal judgment presupposes three representational conditions: experiential grounding (ideas must trace to impressions), structured retrieval (association must operate through organized networks exceeding pairwise connection), and vivacity transfer (inference must produce felt conviction, not merely updated probability). This paper extracts these conditions from Hume’s texts and argues that […]
Metaphors We Compute By: A Computational Audit of Cultural Translation vs. Thinking in LLMs
arXiv:2604.04732v1 Announce Type: cross Abstract: Large language models (LLMs) are often described as multilingual because they can understand and respond in many languages. However, speaking a language is not the same as reasoning within a culture. This distinction motivates a critical question: do LLMs truly conduct culture-aware reasoning? This paper presents a preliminary computational audit […]
SoSBench: Benchmarking Safety Alignment on Six Scientific Domains
arXiv:2505.21605v3 Announce Type: replace-cross Abstract: Large language models (LLMs) exhibit advancing capabilities in complex tasks, such as reasoning and graduate-level question answering, yet their resilience against misuse, particularly involving scientifically sophisticated risks, remains underexplored. Existing safety benchmarks typically focus either on instructions requiring minimal knowledge comprehension (e.g., “tell me how to build a bomb”) or […]
Adaptive Domain Models: Bayesian Evolution, Warm Rotation, and Principled Training for Geometric and Neuromorphic AI
arXiv:2603.18104v2 Announce Type: replace Abstract: Prevailing AI training infrastructure assumes reverse-mode automatic differentiation over IEEE-754 arithmetic. The memory overhead of training relative to inference, optimizer complexity, and structural degradation of geometric properties through training are consequences of this arithmetic substrate. This paper develops an alternative training architecture grounded in three prior results: the Dimensional Type […]
Three Phases of Expert Routing: How Load Balance Evolves During Mixture-of-Experts Training
arXiv:2604.04230v1 Announce Type: cross Abstract: We model Mixture-of-Experts (MoE) token routing as a congestion game with a single effective parameter, the congestion coefficient gamma_eff, that quantifies the balance-quality tradeoff. Tracking gamma_eff across training checkpoints of two open-source MoE models, OLMoE-1B-7B (20 checkpoints, with dense sampling in the surge region) and OpenMoE-8B (6 checkpoints), reveals a […]
General Explicit Network (GEN): A novel deep learning architecture for solving partial differential equations
arXiv:2604.03321v1 Announce Type: cross Abstract: Machine learning, especially physics-informed neural networks (PINNs) and their neural network variants, has been widely used to solve problems involving partial differential equations (PDEs). The successful deployment of such methods beyond academic research remains limited. For example, PINN methods primarily consider discrete point-to-point fitting and fail to account for the […]
AEGIS: Scaling Long-Sequence Homomorphic Encrypted Transformer Inference via Hybrid Parallelism on Multi-GPU Systems
arXiv:2604.03425v1 Announce Type: cross Abstract: Fully Homomorphic Encryption (FHE) enables privacy-preserving Transformer inference, but long-sequence encrypted Transformers quickly exceed single-GPU memory capacity because encoded weights are already large and encrypted activations grow rapidly with sequence length. Multi-GPU execution therefore becomes unavoidable, yet scaling remains challenging because communication is jointly induced by application-level aggregation and encryption-level […]
Incentives shape how humans co-create with generative AI
arXiv:2604.03529v1 Announce Type: cross Abstract: Generative AI is quickly becoming an integral part of people’s everyday workflows. Early evidence has shown that while generative AI can increase individual-level productivity, it does so at the cost of collective diversity, potentially narrowing the set of ideas and perspectives produced. Our research stands in contrast to this concern: […]
CountsDiff: A Diffusion Model on the Natural Numbers for Generation and Imputation of Count-Based Data
arXiv:2604.03779v1 Announce Type: cross Abstract: Diffusion models have excelled at generative tasks for both continuous and token-based domains, but their application to discrete ordinal data remains underdeveloped. We present CountsDiff, a diffusion framework designed to natively model distributions on the natural numbers. CountsDiff extends the Blackout diffusion framework by simplifying its formulation through a direct […]