This paper describes a new auto-associative network called a Unit-Merge Network. It is so-called because novel compound keys are used to link 2 nodes in 1 layer, with 1 node in the next layer. Unit nodes at the base store integer values that can represent binary words. The word size is critical and specific to the dataset and it also provides a first level of consistency over the input patterns. A second cohesion network then links the unit […]

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Manual observation and monitoring of individual cows for disease detection present significant challenges in large-scale farming operations, as the process is labor-intensive, time-consuming, and prone to reduced accuracy. The reliance on human observation often leads to delays in identifying symptoms, as the sheer number of animals can hinder timely attention to each cow. Consequently, the accuracy and precision of disease detection are significantly compromised, potentially affecting animal health and overall farm productivity. Furthermore, organizing and managing human resources […]

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arXiv:2601.04261v1 Announce Type: new Abstract: The widespread adoption of Large Language Model (LLM) in commercial and research settings has intensified the need for robust intellectual property protection. Backdoor-based LLM fingerprinting has emerged as a promising solution for this challenge. In practical application, the low-cost multi-model collaborative technique, LLM ensemble, combines diverse LLMs to leverage their complementary strengths, garnering significant attention and practical adoption. Unfortunately, the vulnerability of existing LLM fingerprinting for the ensemble scenario is unexplored. In order […]

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arXiv:2505.21400v2 Announce Type: replace-cross Abstract: Diffusion models have emerged as a powerful paradigm for modern generative modeling, demonstrating strong potential for large language models (LLMs). Unlike conventional autoregressive (AR) models that generate tokens sequentially, diffusion models allow for parallel sampling, offering a promising path to accelerate generation and eliminate the left-to-right generation constraints. Despite their empirical success, theoretical understandings of diffusion language models remain underdeveloped. In this work, we develop convergence guarantees for diffusion language models from an […]

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Applications are now open for OpenAI Grove Cohort 2, a 5-week founder program designed for individuals at any stage, from pre-idea to product. Participants receive $50K in API credits, early access to AI tools, and hands-on mentorship from the OpenAI team.

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arXiv:2601.03321v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have shown strong potential for radiology report generation, yet their clinical translation is hindered by architectural heterogeneity and the prevalence of factual hallucinations. Standard supervised fine-tuning often fails to strictly align linguistic outputs with visual evidence, while existing reinforcement learning approaches struggle with either prohibitive computational costs or limited exploration. To address these challenges, we propose a comprehensive framework for self-consistent radiology report generation. First, we conduct a […]

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is a philosophical essay by Aldous Huxley in which he discusses the relationship between bright, colorful objects, geometric designs, psychoactives, art, and profound experience. Heaven and Hell metaphorically refer to what Huxley conceives to be two contrary mystical experiences that potentially await when one opens the “doors of perception”—not only in a mystical experience, but in prosaic life.

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I propose a novel framework that integrates stochastic differential equations (SDEs) with deep generative models to improve uncertainty quantification in machine learning applications involving structured and temporal data. This approach, termed Stochastic Latent Differential Inference (SLDI), embeds an Itô SDE in the latent space of a variational autoencoder, allowing for flexible, continuous-time modeling of uncertainty while preserving a principled mathematical foundation. The drift and diffusion terms of the SDE are parameterized by neural networks, enabling data-driven inference and […]

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In this last recorded interview, Terence McKenna reflects on psychedelics, society, and mortality. He calls the War on Drugs a war on imagination, laments capitalism’s desecration of the sacred, and praises ayahuasca as nature’s teacher reconnecting humans to empathy and the planet. Facing death, he remains curious, unafraid, and grateful—still exploring the mystery he’d spent a lifetime mapping.

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arXiv:2601.02390v1 Announce Type: new Abstract: We make modifications to the unscented Kalman filter (UKF) which bestow almost complete practical identifiability upon a lumped-parameter cardiovascular model with 10 parameters and 4 output observables – a highly non-linear, stiff problem of clinical significance. The modifications overcome the challenging problems of rank deficiency when applying the UKF to parameter estimation. Rank deficiency usually means only a small subset of parameters can be estimated. Traditionally, pragmatic compromises are made, such as selecting […]

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