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1, TITLE: A Primer on Quantum Machine Learning
AUTHORS: Su Yeon Chang ; M. Cerezo
CATEGORY: quant-ph [quant-ph, cs.AI, cs.LG, stat.ML]
HIGHLIGHT: We outline the field's tensions between practicality and guarantees, access models and speedups, and classical baselines and claimed quantum advantages-flagging where evidence is strong, where it is conditional or still lacking, and where open questions remain. By shedding light on these nuances and debates, we aim to provide a friendly map of the QML landscape so that the reader can judge when-and under what assumptions-quantum approaches may offer real benefits.

2, TITLE: Dataset Distillation for Pre-Trained Self-Supervised Vision Models
AUTHORS: George Cazenavette ; Antonio Torralba ; Vincent Sitzmann
CATEGORY: cs.CV [cs.CV, cs.AI, cs.LG]
HIGHLIGHT: In contrast, state-of-the-art vision approaches are increasingly building on large, pre-trained self-supervised models rather than training from scratch. In this paper, we investigate the problem of distilling datasets that enable us to optimally train linear probes on top of such large, pre-trained vision models.

3, TITLE: Nemotron Elastic: Towards Efficient Many-in-One Reasoning LLMs
AUTHORS: ALI TAGHIBAKHSHI et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this paper, we present Nemotron Elastic, a framework for building reasoning-oriented LLMs, including hybrid Mamba-Attention architectures, that embed multiple nested submodels within a single parent model, each optimized for different deployment configurations and budgets.

4, TITLE: Cognitive Foundations for Reasoning and Their Manifestation in LLMs
AUTHORS: PRIYANKA KARGUPTA et. al.
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: We propose a fine-grained cognitive evaluation framework and conduct the first large-scale analysis of 170K traces from 17 models across text, vision, and audio modalities, alongside 54 human think-aloud traces, which we make publicly available.

5, TITLE: Agent0: Unleashing Self-Evolving Agents from Zero Data Via Tool-Integrated Reasoning
AUTHORS: PENG XIA et. al.
CATEGORY: cs.LG [cs.LG]
HIGHLIGHT: We introduce Agent0, a fully autonomous framework that evolves high-performing agents without external data through multi-step co-evolution and seamless tool integration.