arXiv preprints from January 1, 2026 through September 21, 2026 — 16:10:21 EST
Counterfactual regret minimization (CFR) is one of the few large numerical workloads that still runs faster on CPUs than on GPUs. Each iteration sweeps a game tree with up to billions of states in millions of small, interdependent gather and scatter steps issued through a generic tree interface. On a GPU every kernel finishes in...
Gaussian boson sampling (GBS) has emerged as a leading photonic paradigmfor demonstrating quantum computational advantage. Nevertheless, state-ofthe-art GBS setups face practical barriers including stringent optical alignment, phase instability, and limited programmability, which impede scalable engineering deployment. The chip-scale...
We investigate how quadratic torsion modifies the equilibrium structure of white dwarfs in covariant $f(T)=T+ΞT^2$ gravity. Static spherical configurations are calculated with a fixed equation of state for cold carbon matter, including relativistic electron degeneracy and Coulomb lattice corrections. The stellar interior is matched to...
Reliable relative localization between aerial and ground robots is a key requirement for tightly coordinated heterogeneous teams. This can be difficult to do using conventional frame-based cameras and fiducial markers because they are sensitive to motion blur, lighting variations, and payload constraints. This paper presents...
We answer in the negative the question of Thomas, recorded by Conley, Conley--Marks--Unger, and Conley--Tamuz, of whether every locally finite Borel graph admits a Borel unfriendly partition. Our counterexample has unbounded degree and is closed on a zero-dimensional Polish space; its connectedness relation is hyperfinite, and its...
Generalization under distribution shift remains a core challenge in modern machine learning, yet existing learning bound theory is limited to narrow, idealized settings and is non-estimable from samples. In this paper, we bridge the gap between theory and practical applications. We first show that existing definition of concept shift...
As the supply of human-written text is exhausted, it has become standard practice to repeat language model training data. Prior work has studied data repetition for densely activated Transformers, but the effects of data repetition remains largely unexplored for recently dominant sparse architectures such as Mixture-of-Experts (MoE),...
Camera traps often run in the field on edge hardware with limited or no connectivity, making small, locally-deployable vision-language models (VLMs) -- not frontier-scale ones -- the practically relevant class to evaluate for species identification. We test whether models in this deployment-relevant 2--8B range carry genuine taxonomic...
Generative artificial intelligence changes how firms reach customers, but standard marketing data do not record how often users see and notice a firm's name in generated answers. We develop Generative Marketing Mix Modeling (GMMM) to estimate the causal effects of Generative Engine Optimization (GEO) and Generative Engine Marketing...
We explore a fermionic dark matter (DM) extension of the Standard Model Effective Field Theory (SMEFT) and establish a complete renormalisation-group framework for its phenomenological investigation. We derive the anomalous-dimension matrix of all relevant dimension-five and dimension-six operators involving Standard Model (SM) and DM...
Out-of-distribution length generalization, namely to extrapolate a task from short to longer context, has been studied intensively for transformers. Here we focus on distance generalization, which probes performance when inter-token distances are changed between training and inference, while keeping a fixed context length. We...
We settle the main open question in the theory of approval-based multi-winner elections: we show that there always exists a committee in the core. The core is a stability and group fairness concept. The proof introduces a new voting rule that optimizes an entropy-like objective function over committees and payment systems. All local...
Agentic AI is moving from bounded task execution toward systems that retain consequential state, continue operating and adapt across task boundaries. That shift creates a control problem that current harnesses largely solve by hand: objectives, retries, verification, stopping rules and other behavioral transitions are specified...
Artificial Intelligence does more than create a governance problem. It can also reveal where institutions have already failed to provide responsiveness, belonging, care, and accountability. Once deployed, AI becomes an intervention in those conditions. It can repair, compound, substitute for, or conceal the failures it encounters....
In this paper, we prove that continuous $2$-convex solutions of the $σ_{2}$ equation with positive continuous density on bounded strictly mean-convex $C^{3}$ domains with $C^{3}$ boundary values belong to $W^{2,p}$ for every $1<p<\infty$ in every dimension. If, in addition, the density is $C^α$, $0<α<1$, the solutions belong to...
We formulate the theoretical aspects of direct waves (DWs) in the case of extreme-mass merger. A DW is a source-driven waveform characterized by a complex frequency $ω_{\rm G}$, which reflects the orbital motion of the particle in the vicinity of the black hole, including a part of the orbit inside the ergoregion: its real part is...
Tensor algebra workloads, of which deep neural networks are prominent examples, are energy-intensive workloads in modern datacenter and edge deployments, making accelerators necessary to achieve energy efficiency and high throughput. To quickly evaluate and iterate on accelerator designs, we need an accelerator modeling framework that...
What if dark energy is recurrent throughout cosmic history? In this picture, episodes of scalar-field dark energy become less surprising and more natural features of cosmic evolution. We construct a homogeneous multi-axion cosmology with a transient contribution during Big Bang nucleosynthesis, two early dark energy components before...
This paper provides a quantitative performance comparison between Orthogonal Time Frequency Space (OTFS) and Orthogonal Frequency-Division Multiplexing (OFDM) modulation schemes, focusing on mobile wireless communication scenarios. We evaluate and compare both schemes based on critical communication scenarios and configurations such...
Robust optimization under interval uncertainty aims to compute solutions that perform well on a range of scenarios that are described by interval-constrained costs. In this paper, we revisit a framework introduced by Ganesh, Maggs and Panigrahi in 2020 to study the robust optimization of NP-hard problems under interval uncertainty. We...
High-dimensional dense text embeddings and large language models face real obstacles in financial-disclosure analysis: context-window limits, hallucination risk, high computational cost, and the arbitrary rotation of vector spaces across independently trained models. We present a training-free, alignment-free framework for corporate...
Language Gaussian fields implicitly assume that the primitive carrying semantics remains identifiable across views. This assumption breaks in scalable anchor-decoded representations, where persistent anchors generate view-conditioned child Gaussians whose geometry and appearance vary with the camera. We introduce Ours, a persistent...
This paper presents a novel approach for data-driven self-learning control of highly flexible, modular manufacturing systems. Specifically, we employ a novel framework for model-based reinforcement learning which introduces approximate inverse process models within the training of reinforcement policies. This approach disentangles the...
Fully Homomorphic Encryption (FHE) enables secure computation on encrypted data, making it a promising solution for privacy-preserving applications in the cloud. Among various FHE schemes, FHE over the Torus (TFHE) stands out due to its support for arbitrary operations. However, its high computation and communication overhead,...
Generative artificial intelligence is entering transportation through traveler-facing advisories, synthetic crash-record generation, and policy decision support. Existing governance frameworks lack transport-specific statistical tools to measure distributional risks across heterogeneous populations. We develop a Distributional...