Tootfinder

Next stop in our NLP timeline is 2013, the introduction of low dimensional dense word vectors - so-called "word embeddings" - based on distributed semantics, as e.g. word2vec by Mikolov et al. from Google, which enabled representation learning on text.
T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space.
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Efficient Estimation of Word Representations in Vector Space
We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Fur…

Gavin Newsom and His Cruel Notion of 'Cruel' (Victor Davis Hanson/American Greatness)
https://amgreatness.com/2025/06/09/gavin-newsom-and-his-cruel-notion-of-cruel/
http://www.memeorandum.com/250609/p58#a250609p58

This https://arxiv.org/abs/2506.02475 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Comba: Improving Nonlinear RNNs with Closed-loop Control
Recent efficient sequence modeling methods such as Gated DeltaNet, TTT, and RWKV-7 have achieved performance improvements by supervising the recurrent memory management through Delta learning rule. Unlike previous state-space models (e.g., Mamba) and gated linear attentions (e.g., GLA), these models introduce interactions between the recurrent state and the key vector, resulting in a nonlinear recursive structure. In this paper, we first introduce the concept of Nonlinear RNNs with a comprehens…

Unveiling the role of vector potential in the Aharonov-Bohm effect
Masashi Wakamatsu
https://arxiv.org/abs/2506.07018 https://arxiv.o…

Unveiling the role of vector potential in the Aharonov-Bohm effect
The most popular interpretation of the Aharonov-Bohm (AB) effect is that the electromagnetic potential locally affects the complex phase of a charged particle's wave function in the magnetic field free region. However, since the vector potential is a gauge-variant quantity, not a few researchers suspect that it is just a convenient tool for calculating the force field. This motivates them to explain the AB effect without using the vector potential, which inevitably leads to some sort of non-loc…

Investigation: Russian network engineer Viktor Vedeneev controls thousands of Telegram IP addresses and maintains its servers; his companies have FSB links (Roman Anin/OCCRP)
https://www.occrp.org/en/investigation/telegram-the-fsb-and-the-man-in-t…

Telegram, the FSB, and the Man in the Middle
The technical infrastructure that underpins Telegram is controlled by a man whose companies have collaborated with Russian intelligence services.

Remarks on Higgs bundles twisted by a vector bundle
David Alfaya, Indranil Biswas, Pradip Kumar
https://arxiv.org/abs/2506.06573 https://

Remarks on Higgs bundles twisted by a vector bundle
For any V-twisted Higgs bundle on a compact Riemann surface X, where V is a holomorphic vector bundle of rank two on X, there are two associated Higgs bundles on X, twisted by line bundles, which are constructed using a Hecke transformation on V. We characterize all such pairs of Higgs bundles (twisted by line bundles) given by V-twisted Higgs bundles. Using this characterization, we provide a spectral correspondence for the moduli space, identifying V-twisted Higgs bundles with the direct imag…

This https://arxiv.org/abs/2505.22119 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hept…

Massless limit of massive self-interacting vector fields
We study massive self-interacting vector field theories with mass added ``by hand". We show that the massless limit of the quartic self-interacting vector field theory is not smooth. Pathological behavior of the theory is not limited only at the classical level, even at the quantum level unitarity is violated at the two-loop. Using the Vainshtein mechanism, we show that it fails beyond the strong-coupling scale and hence a massless limit is not smooth even in quantum theory.

This https://arxiv.org/abs/2506.05960 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models
Significant investments have been made towards the commodification of diffusion models for generation of diverse media. Their mass-market adoption is however still hobbled by the intense hardware resource requirements of diffusion model inference. Model quantization strategies tailored specifically towards diffusion models have been useful in easing this burden, yet have generally explored the Uniform Scalar Quantization (USQ) family of quantization methods. In contrast, Vector Quantization (VQ…

This https://arxiv.org/abs/2410.16860 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Typical Quantum States of the Universe are Observationally Indistinguishable
We establish three new impossibility results regarding our knowledge of the quantum state of the universe -- a central object in quantum theory. We show that, if the universal quantum state is a typical unit vector from a high-dimensional subspace H_0 of Hilbert space H (such as the one defined by a low-entropy macro-state as prescribed by the Past Hypothesis), then no observation can determine or just significantly narrow down which vector it is. In other words, the overwhelming majority of po…

This https://arxiv.org/abs/2505.11864 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Learning Pareto-Optimal Rewards from Noisy Preferences: A Framework for Multi-Objective Inverse Reinforcement Learning
As generative agents become increasingly capable, alignment of their behavior with complex human values remains a fundamental challenge. Existing approaches often simplify human intent through reduction to a scalar reward, overlooking the multi-faceted nature of human feedback. In this work, we introduce a theoretical framework for preference-based Multi-Objective Inverse Reinforcement Learning (MO-IRL), where human preferences are modeled as latent vector-valued reward functions. We formalize …