arXiv:2510.27044v1 Announce Type: new Abstract: Mathematical reasoning is a central challenge for large language models (LLMs), requiring not only correct answers but also faithful reasoning processes. Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising approach for enhancing…
arXiv:2505.11040v3 Announce Type: replace Abstract: Recent advances in transformer architectures deeply enhanced long-context language modeling. Among them, HyperAttention achieves competitive efficiency by combining a single-level LSH-based clustering with uniform residual sampling. However, HyperAttention fails to find all significant keys, which…
arXiv:2510.27062v1 Announce Type: new Abstract: An LLM’s factuality and refusal training can be compromised by simple changes to a prompt. Models often adopt user beliefs (sycophancy) or satisfy inappropriate requests which are wrapped within special text (jailbreaking). We explore emph{consistency…
arXiv:2506.03898v2 Announce Type: replace Abstract: We propose a framework for hypothesis testing on conditional probability distributions, which we then use to construct statistical tests of functionals of conditional distributions. These tests identify the inputs where the functionals differ with high…
arXiv:2510.27063v1 Announce Type: new Abstract: Given two algorithms for the same problem, can we determine whether they are meaningfully different? In full generality, the question is uncomputable, and empirically it is muddied by competing notions of similarity. Yet, in many…
arXiv:2507.23607v2 Announce Type: replace Abstract: Clinical trials are a systematic endeavor to assess the safety and efficacy of new drugs or treatments. Conducting such trials typically demands significant financial investment and meticulous planning, highlighting the need for accurate predictions of…
arXiv:2510.27065v1 Announce Type: new Abstract: We present MLPerf Automotive, the first standardized public benchmark for evaluating Machine Learning systems that are deployed for AI acceleration in automotive systems. Developed through a collaborative partnership between MLCommons and the Autonomous Vehicle Computing…
arXiv:2510.04525v2 Announce Type: replace Abstract: Masked diffusion models have shown promising performance in generating high-quality samples in a wide range of domains, but accelerating their sampling process remains relatively underexplored. To investigate efficient samplers for masked diffusion, this paper theoretically…
arXiv:2510.27072v1 Announce Type: new Abstract: Recent advances in large language model (LLM) reasoning, led by reinforcement learning with verifiable rewards (RLVR), have inspired self-play post-training, where models improve by generating and solving their own problems. While self-play has shown strong…
arXiv:2510.21592v2 Announce Type: replace Abstract: Data-driven deep learning methods like neural operators have advanced in solving nonlinear temporal partial differential equations (PDEs). However, these methods require large quantities of solution pairsu2014the solution functions and right-hand sides (RHS) of the equations.…
