technocracy

Accurate prediction of hydrogen sorption in fine-grained geological materials is essential for evaluating underground hydrogen storage capacity, assessing caprock integrity, and characterizing hydrogen migration in subsurface energy systems. Classical isotherm models perform well at the individual-sample level but fail when generalized across heterogeneous populations, with the coefficient of determination collapsing from 0.80-0.90 for single-sample fits to 0.09-0.38 for aggregated multi-sample datasets. We present a multi-scale physics-informed neural network framework that addresses this limitation by embedding classical adsorption theory […]

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One of the fundamental challenges in applying RL to discrete diffusion language models has been the intractable sequence level log likelihood computation. Unlike autoregressive models where you can decompose the probability chain rule style, diffusion models generate tokens in parallel across multiple denoising steps, making gradient estimation for policy optimization computationally prohibitive. The new LLaDA2.1 paper (arXiv:2602.08676v1) introduces ELBO based Block level Policy Optimization (EBPO) that I think deserves more attention from the RL community. Here’s the core […]

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The U.S. Treasury sanctioned six individuals and two entities for involvement in a North Korean IT fraud scheme targeting crypto, according to Reuters reporting. The network spans countries and uses crypto to move funds, and the next crypto to explode will be the project that builds intelligence infrastructure against exactly these threats. Security threats accelerate demand for intelligence tools, and the project building that layer captures the wave. Pepeto crossed $7.99 million raised with wallets earning 198% APY […]

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Reinforcement learning algorithms often suffer from slow convergence due to sparse reward signals, particularly in complex environments where feedback is delayed or infrequent. This paper introduces the Psychological Regret Model (PRM), a novel approach that accelerates learning by incorporating regret-based feedback signals after each decision step. Rather than waiting for terminal rewards, PRM computes a regret signal based on the difference between the expected value of the optimal action and the value of the action taken in each […]

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arXiv:2601.05339v1 Announce Type: new Abstract: In recent years, the security vulnerabilities of Multi-modal Large Language Models (MLLMs) have become a serious concern in the Generative Artificial Intelligence (GenAI) research. These highly intelligent models, capable of performing multi-modal tasks with high accuracy, are also severely susceptible to carefully launched security attacks, such as jailbreaking attacks, which can manipulate model behavior and bypass safety constraints. This paper introduces MJAD-MLLMs, a holistic framework that systematically analyzes the proposed Multi-turn Jailbreaking Attacks […]

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arXiv:2603.06462v1 Announce Type: cross Abstract: Distribution regression, where the goal is to predict a scalar response from a distribution-valued predictor, arises naturally in settings where observations are grouped and outcomes depend on group-level characteristics rather than on individual measurements. We introduce DistBART, a Bayesian nonparametric approach to distribution regression that models the regression function as a linear functional with the Riesz representer assigned a Bayesian additive regression trees (BART) prior. We argue that shallow decision tree ensembles encode […]

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Hi everyone, I recently wrote this whitepaper https://github.com/RippnerLabs/meridian-link/blob/main/whitepaper/whitepaper.pdf And i’m blocked on publishing to arXiv, due to lack of endorsement for DC (Distributed, Parallel, and Cluster Computing) Can anyone please let support with this endorsement. (Jayanth Kumar Morem should forward this email to someone who’s registered as an endorser for the cs.DC (Distributed, Parallel, and Cluster Computing) subject class of arXiv.) Jayanth Kumar Morem requests your endorsement to submit an article to the cs.DC section of arXiv. To […]

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arXiv:2605.15688v1 Announce Type: new Abstract: Concept Activation Vectors (CAVs) are a fundamental tool for concept-based explainability in deep learning, yet their practical utility is limited by statistical instability. We analyze the stochastic nature of CAVs and the Testing with CAVs (TCAV) method, deriving the distributions of major CAV classes including PatternCAV, FastCAV, and ridge regression-based CAVs. We then identify a fundamental flaw in the standard TCAV score: its reliance on a discontinuous indicator function induces non-decaying variance in […]

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