arXiv:2604.07409v1 Announce Type: new Abstract: Layout plays a crucial role in graphic design and poster generation. Recently, the application of deep learning models for layout generation has gained significant attention. This paper focuses on using a GAN-based model conditioned on images to generate advertising poster graphic layouts, requiring a dataset of paired product images and layouts. To address this task, we introduce the Content-aware Graphic Layout Dataset (CGL-Dataset), consisting of 60,548 paired inpainted posters with annotations and 121,000 […]

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Hi, I am currently making the jump to ML from theoretical physics. I just got done with the review period, went from 4333 to 4433, but the remaining two weak rejects said 1) that if I add a parameter sweep and a small section (which I did) they’d raise, and the other reviewer said that if some of their questions were addressed properly they’d also raise the score. I think the most likely outcome is hopefully 4443, but […]

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arXiv:2402.14515v4 Announce Type: replace-cross Abstract: We analyze the frequency spectrum of Quantum Neural Networks (QNNs) using Minkowski sums, which yields a compact algebraic description and permits explicit computation. Using this description, we prove several maximality results for broad classes of QNN architectures. Under some mild technical conditions we establish a bijection between classes of models with the same area $A:=Rcdot L$ that preserves the frequency spectrum, where $R$ denotes the number of qubits and $L$ the number of […]

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arXiv:2603.02491v2 Announce Type: replace-cross Abstract: As artificial agents become increasingly capable, what internal structure is *necessary* for an agent to act competently under uncertainty? Classical results show that optimal control can be *implemented* using belief states or world models, but not that such representations are required. We prove quantitative “selection theorems” showing that strong task performance (low *average-case regret*) forces world models, belief-like memory and — under task mixtures — persistent variables resembling core primitives associated with emotion, […]

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The case for distributed execution without orchestration You have an async function. You want to run it on another machine. How hard could that be? If you’ve gone looking for the answer in the Python ecosystem, you already know: unreasonably so. Not because the problem is complex, but because every framework that offers to solve it insists on solving a dozen other problems you didn’t ask about. Want distributed execution? Great — but first, define your workflow as […]

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arXiv:2602.06087v1 Announce Type: new Abstract: This research presents a dynamic modeling framework and parameter identification methods for describing the highly nonlinear behaviors of flexibly connected dual-AUV systems. The modeling framework is established based on the lumped mass method, integrating axial elasticity, bending stiffness, added mass and hydrodynamic forces, thereby accurately capturing the time-varying response of the forces and cable configurations. To address the difficulty of directly measuring material-related and hydrodynamic coefficients, this research proposes a parameter identification method […]

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arXiv:2602.09847v1 Announce Type: new Abstract: Conditional Value-at-Risk (CVaR) is a central tail-risk measure in stochastic structural mechanics, yet its accurate evaluation under high-dimensional, spatially correlated material uncertainty remains computationally prohibitive for classical Monte Carlo methods. Leveraging bounded-expectation reformulations of CVaR compatible with quantum amplitude estimation, we develop a quantum-enhanced inference framework that casts CVaR evaluation as a statistically consistent, confidence-constrained maximum-likelihood amplitude estimation problem. The proposed method extends iterative quantum amplitude estimation (IQAE) by embedding explicit maximum-likelihood inference […]

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arXiv:2601.08071v1 Announce Type: new Abstract: In this short paper, we advocate for the idea that continuation-based intermediate languages correspond to intermediate logics. The goal of intermediate languages is to serve as a basis for compiler intermediate representations, allowing to represent expressive program transformations for optimisation and compilation, while preserving the properties that make programs compilable efficiently in the first place, such as the “stackability” of continuations. Intermediate logics are logics between intuitionistic and classical logic in terms of […]

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arXiv:2603.20212v1 Announce Type: new Abstract: Reward models (RMs) are critical for aligning Large Language Models via Reinforcement Learning from Human Feedback (RLHF). While Generative Reward Models (GRMs) achieve superior accuracy through chain-of-thought (CoT) reasoning, they incur substantial computational costs. Conversely, Scalar Reward Models (SRMs) offer efficiency but suffer from limited performance and adaptability in complex scenarios. We introduce Fast-Slow Thinking Reward Models (F/S-RM), a hybrid RM architecture inspired by Dual Process Theory. It trains a single model to […]

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