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Tensor networks were developed in the context of many-body physics as compressed representations of multiparticle quantum states. These representations mitigate the exponential complexity of many-body systems by capturing only the most relevant dependencies. Due to the formal similarity between quantum entanglement and statistical correlations, tensor networks have recently been integrated in machine learning, operating both as alternative learning architectures and as decompositions of components of neural networks. The expectation is that the theoretical understanding of tensor networks developed […]

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While reinforcement learning with verifiable rewards (RLVR) significantly enhances LLM reasoning by optimizing the conditional distribution P(y|x), its potential is fundamentally bounded by the base model’s existing output distribution. Optimizing the marginal distribution P(y) in the Pre-train Space addresses this bottleneck by encoding reasoning ability and preserving broad exploration capacity. Yet, conventional pre-training relies on static corpora for passive learning, leading to a distribution shift that hinders targeted reasoning enhancement. In this paper, we introduce PreRL (Pre-train Space […]

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When Grammy-nominated singer-songwriter Aloe Blacc got COVID despite being vaccinated and boosted, he tried to fund research for a better solution. What he quickly found out? You can’t just write a check in biotech. Regulators require a commercialization plan, and philanthropy doesn’t move science through clinical trials or get you a license on university IP. Now, he’s bootstrapping a cancer drug platform targeting pancreatic cancer, a disease that […]

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Given two symmetric positive-definite matrices $A, B in mathbb{R}^{n times n}$, we study the spectral properties of the interpolation $A^{1-x} B^x$ for $0 leq x leq 1$. The presence of `common structures’ in $A$ and $B$, eigenvectors pointing in a similar direction, can be investigated using this interpolation perspective. Generically, exact log-linearity of the operator norm $|A^{1-x} B^x|$ is equivalent to the existence of a shared eigenvector in the original matrices; stability bounds show that approximate log-linearity forces […]

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Sequential recommendation has become increasingly prominent in both academia and industry, particularly in e-commerce. The primary goal is to extract user preferences from historical interaction sequences and predict items a user is likely to engage with next. Recent advances have leveraged contrastive learning and graph neural networks to learn more expressive representations from interaction histories — graphs capture relational structure between nodes, while ID-based representations encode item-specific information. However, few studies have explored multi-view contrastive learning between ID […]

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