Quality follows upgrading
Academic
arXiv:2603.18237v1 Announce Type: new Abstract: Researchers train neural simulators on uniformly sampled numerical simulation data. But under the same budget, does systematically sampled data provide …
arXiv:2603.18247v1 Announce Type: new Abstract: Existing XAI metrics measure faithfulness for a single model, ignoring model multiplicity where near-optimal classifiers rely on different or spurious …
arXiv:2603.18256v1 Announce Type: new Abstract: Recent advances in reasoning-based large language models (LLMs) have demonstrated substantial improvements in complex problem-solving tasks. Motivated by these advances, …
arXiv:2603.18257v1 Announce Type: new Abstract: Selecting relevant state dimensions in the presence of confounded distractors is a causal identification problem: observational statistics alone cannot reliably …
arXiv:2603.18258v1 Announce Type: new Abstract: Direct Preference Optimization (DPO) has emerged as a popular algorithm for aligning pretrained large language models with human preferences, owing …
arXiv:2603.18266v1 Announce Type: new Abstract: Traffic microsimulators are widely used to evaluate road network performance under various ``what-if" conditions. However, the behavior models controlling the …
arXiv:2603.18280v1 Announce Type: new Abstract: Current alignment evaluation mostly measures whether models encode dangerous concepts and whether they refuse harmful requests. Both miss the layer …
arXiv:2603.18281v1 Announce Type: new Abstract: Population-based Structural Health Monitoring (PBSHM) aims to share information between similar machines or structures. This paper takes a population-level perspective, …
arXiv:2603.18297v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) architectures enable efficient scaling by activating only a subset of parameters for each input. However, conventional MoE …
arXiv:2603.18299v1 Announce Type: new Abstract: Intracortical brain-computer interfaces (BCIs) can decode speech from neural activity with high accuracy when trained on data pooled across recording …
arXiv:2603.18314v1 Announce Type: new Abstract: Approximate subgraph matching (ASM) is a task that determines the approximate presence of a given query graph in a large …
arXiv:2603.18325v1 Announce Type: new Abstract: Chain-of-thought reasoning, where language models expend additional computation by producing thinking tokens prior to final responses, has driven significant advances …
