arXiv:2504.02627v2 Announce Type: replace-cross Abstract: Markov chain Monte Carlo (MCMC) methods are a powerful but computationally expensive way of performing non-parametric Bayesian inference. MCMC proposals which utilise gradients, such as Hamiltonian Monte Carlo (HMC), can better explore the parameter space of interest if the additional hyper-parameters are chosen well. The No-U-Turn Sampler (NUTS) is a variant of HMC which is extremely effective at selecting these hyper-parameters but is slow to run and is not suited to GPU architectures. […]

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arXiv:2602.12441v1 Announce Type: new Abstract: Whole slide images (WSIs) enable weakly supervised prognostic modeling via multiple instance learning (MIL). Spatial transcriptomics (ST) preserves in situ gene expression, providing a spatial molecular context that complements morphology. As paired WSI-ST cohorts scale to population level, leveraging their complementary spatial signals for prognosis becomes crucial; however, principled cross-modal fusion strategies remain limited for this paradigm. To this end, we introduce PathoSpatial, an interpretable end-to-end framework integrating co-registered WSIs and ST to […]

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The pattern is becoming familiar across the industry. Teams build impressive AI agent demos, often coding copilots, research assistants, or internal automation tools. Leadership gets excited. Resources get allocated. Expectations rise. Six months later, the project is either quietly shelved or operating with so much human oversight that it would have been faster to build a traditional workflow. What makes this frustrating is that the teams involved are rarely inexperienced. The engineers are competent. The models are capable. […]

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User interests typically encompass both long-term preferences and short-term intentions, reflecting the dynamic nature of user behaviors across different timeframes. The uneven temporal distribution of user interactions highlights the evolving patterns of interests, making it challenging to accurately capture shifts in interests using comprehensive historical behaviors. To address this, we propose SLSRec, a novel Session-based model with the fusion of Long- and Short-term Recommendations that effectively captures the temporal dynamics of user interests by segmenting historical behaviors over […]

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When language models are trained on synthetic data, they (student model) can covertly acquire behavioral traits from the data-generating model (teacher model). Subliminal learning refers to the transmission of traits from a teacher to a student model via training on data unrelated to those traits. Prior work demonstrated this in the training domains of number sequences, code, and math Chain-of-Thought traces including transmission of misaligned behaviors. We investigate whether transmission occurs through natural language paraphrases with fixed semantic […]

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If you’ve been following any entertainment media at all, you probably know it’s time for the Coachella Valley Music and Arts Festival, a massive two-weekend music festival held at the Empire Polo Club in Indio, California. We are approaching the second weekend of Coachella 2026, featuring headliners such as Sabrina Carpenter (Friday), Justin Bieber (Saturday), and Karol G (Sunday). It sold out in 3 hours with about 240,000 tickets sold for the two weekends. In 2022, Coachella partnered […]

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arXiv:2602.00977v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in domains where errors carry high social, scientific, or safety costs. Yet standard confidence estimators, such as token likelihood, semantic similarity and multi-sample consistency, remain brittle under distribution shift, domain-specialised text, and compute limits. In this work, we present Structural Confidence, a single-pass, model-agnostic framework that enhances output correctness prediction based on multi-scale structural signals derived from a model’s final-layer hidden-state trajectory. By combining spectral, local-variation, […]

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arXiv:2601.08901v1 Announce Type: new Abstract: Scientific discovery is a cumulative process and requires new ideas to be situated within an ever-expanding landscape of existing knowledge. An emerging and critical challenge is how to identify conceptually relevant prior work from rapidly growing literature, and assess how a new idea differentiates from existing research. Current embedding approaches typically conflate distinct conceptual aspects into single representations and cannot support fine-grained literature retrieval; meanwhile, LLM-based evaluators are subject to sycophancy biases, failing […]

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