arXiv preprints from January 1, 2026 through September 26, 2026 — 23:36:06 EST
Defining agency is an extremely important challenge for cognitive science and artificial intelligence. Physics generally describes mechanical happenings, but there remains an unbridgeable gap between these and the acts of agents. To discuss the morality and responsibility of agents, it is necessary to model acts; whether such...
This paper provides a comprehensive comparison of domain generalization techniques applied to time series data within a drilling context, focusing on the prediction of a continuous Stick-Slip Index (SSI), a critical metric for assessing torsional downhole vibrations at the drill bit. The study aims to develop a robust regression model...
The interplay among quantum degrees of freedom-spin, orbital and momentum-has emerged as a fertile ground for realizing magnetic quantum states with transformative potential for electronic and spintronic technologies. Prominent examples include ferromagnetic Weyl semimetals and antiferromagnetic axion insulators. Recently,...
We propose Space-time in situ postprocessing (STIPP), a machine learning model that generates spatio-temporally consistent weather forecasts for a network of station locations. Gridded forecasts from classical numerical weather prediction or data-driven models often lack the necessary precision due to unresolved local effects. Typical...
This paper introduces a diffusion-based framework for universal image segmentation, making agnostic segmentation possible without depending on mask-based frameworks and instead predicting the full segmentation in a holistic manner. We present several key adaptations to diffusion models, which are important in this discrete setting....
Every existing inference-time reasoning framework discards all failure context at problem boundaries, leaving a model solving problem 500 no wiser than it was on problem 1. We present ReTreVal (Reasoning Tree with Validation), a training-free framework that closes this gap through adaptive tree exploration with tool-augmented node...
Beyond active material intrinsic properties, the electrode manufacturing process is a crucial step to reach high energy density and long-life of Li-ion batteries. In particular, very high pressures are applied to the electrode during the calendering step, that directly influence the microstructure and the electrochemical performances....
This study proposes a novel hybrid deep learning framework that integrates a Large Language Model (LLM) with a Transformer architecture for stock price forecasting. The research addresses a critical theoretical gap in existing approaches that empirically combine textual and numerical data without a formal understanding of their...
We investigate interaction-induced corrections to entanglement entropy by mapping a screened Yukawa-type interaction to an effective harmonic oscillator system with controlled anharmonic perturbations. Starting from a one-dimensional interaction $V(x) = -g^2 e^{-αm x}/x$, we reformulate the problem in terms of a four-dimensional...
We introduce a new two-parameter fractional time operator with Volterra structure, denoted by ${}^{W}D_{t}^{α,β}$, defined through the Laplace symbol \[ Φ_{α,β}(s) = \frac{s^α}{\bigl(1+(1-α)s^{α-1}\bigr)^β}, \qquad 0<α<1, \ β\ge0. \] The operator preserves the Caputo-type high-frequency behavior while allowing a controlled...
In this report, we investigate the potential use of large language models (LLM's) in the task of data compression. Previous works have demonstrated promising results in applying LLM's towards compressing not only text, but also a wide range of multi-modal data. Despite the favorable performance achieved, there still remains several...
Distributed radar sensors enable robust human activity recognition. However, scaling the number of coordinated nodes introduces challenges in feature extraction from large datasets, and transparent data fusion. We propose an end-to-end framework that operates directly on raw radar data. Each radar node employs a lightweight 2D...
Acting in cluttered environments requires predicting and avoiding collisions while still achieving precise control. Conventional optimization-based controllers can enforce physical constraints, but they struggle to produce feasible solutions quickly when many obstacles are present. Diffusion models can generate diverse trajectories...
The rapid expansion of context length in large language models (LLMs) has outpaced existing evaluation benchmarks. Current long-context benchmarks often trade off scalability and realism: synthetic tasks underrepresent real-world complexity, while fully manual annotation is costly to scale to extreme lengths and diverse scenarios. We...
Task-oriented proactive dialogue agents play a pivotal role in recruitment, particularly for steering conversations towards specific business outcomes, such as acquiring social-media contacts for private-channel conversion. Although supervised fine-tuning and reinforcement learning have proven effective for training such agents, their...
Vision-Language-Action (VLA) models, which integrate pretrained large Vision-Language Models (VLM) into their policy backbone, are gaining significant attention for their promising generalization capabilities. This paper revisits a fundamental yet seldom systematically studied question: how VLM choice and competence translate to...
Stochastic contextual bandits are fundamental for sequential decision-making but pose significant challenges for existing neural network-based algorithms, particularly when scaling to quantum neural networks (QNNs) due to issues such as massive over-parameterization, computational instability, and the barren plateau phenomenon. This...
Solid-state batteries (SSB) are emerging as next-generation electrochemical energy storage devices. Achieving high energy density in SSB relies on solid polymer electrolytes (SPE) that are electrochemically stable against both lithium metal and high-potential positive electrodes, two conditions that are difficult to satisfy without...
Large language models (LLMs) substantially enhance developer productivity in repository-level code generation through interactive collaboration. However, as interactions progress, repository context must be continuously preserved and updated to integrate newly validated information. Meanwhile, the expanding session history increases...
Subword tokenization introduces a computational layer in language models where many distinct token sequences decode to the same surface form and preserve meaning, yet induce different internal computations. Despite this non-uniqueness, language models are typically trained using a single canonical longest-prefix tokenization. We...
We exhibit new biorthogonal sequences generated by index integrals of the squares of the modified Bessel functions and gamma functions. The composition orthogonality, involving differential operators is employed. Generalized Wilson polynomials are introduced. Some properties are investigated.
We present JWST/MIRI imaging of eight nearby Active Galactic Nuclei (AGN) from the GATOS survey to investigate the physical conditions of extended dust in their narrow line regions (NLRs). In four galaxies (ESO 428-G14, NGC 4388, NGC 3081, and NGC 5728), we detect spatially resolved dust structures extending ~100-200 pc along the NLR....
This paper investigates the performance of multi-user covert communications over a fixed bandwidth in a multi-cell scenario with both eavesdroppers and malicious jammers. We propose an intelligent spectrum control (ISC) scheme that combines high-accuracy spectrum sensing with AI-assisted real-time decision-making to generate...
Functional-structural plant models (FSPM) replicate plants' responses to their environment and are useful for predicting behavior in a changing climate. However, they rely on detailed measurements of traits, which are difficult to collect consistently across scales, often limiting model parameterization and thorough evaluation, and...
The most general mathematical law for summing bounded quantities is not the arithmetic law, but a composition law of which the summation law for velocities in special relativity is only one particular example. We believe that this composition law, should also be used in quantum theory. We present it with a few examples, we discuss its...