arXiv:2602.12389v1 Announce Type: new Abstract: Temporal knowledge graph (TKG) forecasting requires predicting future facts by jointly modeling structural dependencies within each snapshot and temporal evolution across snapshots. However, most existing methods are stateless: they recompute entity representations at each timestamp from a limited query window, leading to episodic amnesia and rapid decay of long-term dependencies. To address this limitation, we propose Entity State Tuning (EST), an encoder-agnostic framework that endows TKG forecasters with persistent and continuously evolving entity […]

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I didn’t set out to build a persistent AI persona. I set out to write better content. Somewhere between “I need a consistent voice across articles” and “this thing just connected two questions I asked three hours apart without being told to,” the project became something else entirely. This is a technical walkthrough of the Anima Architecture, a system I built on top of Claude that gives the AI externalized memory, behavioral rules, self-correction protocols, and identity markers […]

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Large-scale foundation models for scientific machine learning adapt to physical settings unseen during training, such as zero-shot transfer between turbulent scales. This phenomenon, in-context learning, challenges conventional understanding of learning and adaptation in physical systems. Here, we study in-context learning of dynamical systems in a minimal setting: we train a small two-layer, single-head transformer to forecast one dynamical system, and then evaluate its ability to forecast a different dynamical system without retraining. We discover an early tradeoff in […]

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arXiv:2603.02255v1 Announce Type: new Abstract: We propose MEBM-Speech, a multi-scale enhanced neural decoder for speech activity detection from non-invasive magnetoencephalography (MEG) signals. Built upon the BrainMagic backbone, MEBM-Speech integrates three complementary temporal modeling mechanisms: a multi-scale convolutional module for short-term pattern extraction, a bidirectional LSTM (BiLSTM) for long-range context modeling, and a depthwise separable convolutional layer for efficient cross-scale feature fusion. A lightweight temporal jittering strategy and average pooling further improve onset robustness and boundary stability. The model […]

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arXiv:2601.18817v1 Announce Type: new Abstract: This study presents a hybrid reduced-order modeling (ROM) framework for turbulent incompressible flows on collocated finite volume grids. The methodology employs the “discretize-then-project” consistent flux strategy, which ensures mass conservation and pressure-velocity coupling without requiring auxiliary stabilization like boundary control or pressure stabilization techniques. However, because standard Galerkin projection fails to yield physically consistent results for the turbulent viscosity field, a hybrid strategy is adopted: velocity and pressure are resolved via intrusive projection, […]

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arXiv:2601.13874v2 Announce Type: replace Abstract: The maximum mean discrepancy (MMD) is a kernel-based nonparametric statistic for two-sample testing, whose inferential accuracy depends critically on variance characterization. Existing work provides various finite-sample estimators of the MMD variance, often differing under the null and alternative hypotheses and across balanced or imbalanced sampling schemes. In this paper, we study the variance of the MMD statistic through its U-statistic representation and Hoeffding decomposition, and establish a unified finite-sample characterization covering different hypotheses […]

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arXiv:2403.15711v2 Announce Type: replace-cross Abstract: Causal representation learning (CRL) offers the promise of uncovering the underlying causal model by which observed data was generated, but the practical applicability of existing methods remains limited by the strong assumptions required for identifiability and by challenges in applying them to real-world settings. Most current approaches are applicable only to relatively restrictive model classes, such as linear or polynomial models, which limits their flexibility and robustness in practice. One promising approach to […]

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arXiv:2601.00142v1 Announce Type: new Abstract: This paper compares three methodological categories of neural reasoning: LLM reasoning, supervised learning-based reasoning, and explicit model-based reasoning. LLMs remain unreliable and struggle with simple decision-making that animals can master without extensive corpora training. Through disjunctive syllogistic reasoning testing, we show that reasoning via supervised learning is less appealing than reasoning via explicit model construction. Concretely, we show that an Euler Net trained to achieve 100.00% in classic syllogistic reasoning can be trained […]

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Introduction: Audio as a First-Class Intelligence Modality Audio is one of the most information-dense and perceptually sensitive modalities through which humans interact with the world. Unlike vision or text, audio is continuous in time, governed by physical acoustics, and immediately evaluated by the human auditory system with millisecond-level sensitivity. These properties make audio uniquely challenging for artificial intelligence systems. Even small distortions—phase discontinuities, temporal smearing, and spectral artifacts—can result in degraded intelligibility, listener fatigue, or loss of immersion. […]

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The current implementation of pneumonia diagnosis remains challenging to achieve better performance and improve results. The aim of this research is to propose an innovative framework for pediatric pneumonia diagnosis that unites three fine-tuned pre-trained CNN models through feature fusion at the EfficientNetB0, RestNet50, and MobileNetV2 to achieve better performance and results. The mixed-model architecture framework provides an ideal solution for time-sensitive clinical applications operating in resource-constrained environments. This research experiment used the Chest X-Ray Images (Pneumonia) dataset, […]

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