Archives AI News

ServiceNow AI Research Lab has released Apriel-1.5-15B-Thinker, a 15-billion-parameter open-weights multimodal reasoning model trained with a data-centric mid-training recipe—continual pretraining followed by supervised fine-tuning—without reinforcement learning or preference optimization. The model attains an Artificial Analysis Intelligence Index score of 52…

Find great deals and promo codes for Visible at WIRED and save big, whether you’re a long-time customer or a newbie.

Distributed ledger technology (DLT) emerged with Bitcoin as a censorship-resistant way to conduct payments between distrusting peers. After a period, traditional financial institutions began to explore the technology, recognizing the potential of its immutability, decentralization, and programmability to redesign financial…

Comparing model-free and model-based RL methods on a dynamic grid world The post Temporal-Difference Learning and the Importance of Exploration: An Illustrated Guide appeared first on Towards Data Science.

arXiv:2510.00073v1 Announce Type: new Abstract: Motivated by the need to efficiently identify multiple candidates in high trial-and-error cost tasks such as drug discovery, we propose a near-optimal algorithm to identify all {epsilon}-best arms (i.e., those at most {epsilon} worse than…

arXiv:2510.01020v1 Announce Type: cross Abstract: We study the problem of sequentially testing individuals for a binary disease outcome whose true risk is governed by an unknown logistic model. At each round, a patient arrives with feature vector $x_t$, and the…

arXiv:2508.15051v2 Announce Type: replace-cross Abstract: We study the problem of robust estimation under heterogeneous corruption rates, where each sample may be independently corrupted with a known but non-identical probability. This setting arises naturally in distributed and federated learning, crowdsourcing, and…

arXiv:2510.00875v1 Announce Type: cross Abstract: Simultaneously performing variable selection and inference in high-dimensional models is an open challenge in statistics and machine learning. The increasing availability of vast amounts of variables requires the adoption of specific statistical procedures to accurately…

arXiv:2507.02442v2 Announce Type: replace-cross Abstract: Enhancing the intelligibility and interpretability of machine learning is a crucial task in responding to the demand for Explicability as an AI principle, and in promoting the better social implementation of AI. The aim of…

arXiv:2506.04118v2 Announce Type: replace-cross Abstract: We propose Guided Speculative Inference (GSI), a novel algorithm for efficient reward-guided decoding in large language models. GSI combines soft best-of-$n$ test-time scaling with a reward model $r(x,y)$ and speculative samples from a small auxiliary…