arXiv Papers

Published: 2026-03-16 17:59:54

Authors: Yulin Luo, Hao Chen, Zhuangzhe Wu, Bowen Sui, Jiaming Liu, Chenyang Gu, Zhuoyang Liu, Qiuxuan Feng, Jiale Yu, Shuo Gu, Peng Jia, Pheng-Ann Heng, Shanghang Zhang

Categories: cs.CV

Abstract:
Vision-Language-Action (VLA) models have recently emerged as a promising paradigm for robotic manipulation, in which reliable action prediction critically depends on accurately interpreting and integrating visual observations conditioned on language instructions. Although recent works have sought to enhance the visual capabilities of VLA models, most approaches treat the LLM backbone as a black box, providing limited insight into how visual information is grounded into action generation. Therefore, we perform a systematic analysis of multiple VLA models across different action-generation paradigms and observe that sensitivity to visual tokens progressively decreases in deeper layers during action generation. Motivated by this observation, we propose \textbf{DeepVision-VLA}, built on a \textbf{Vision-Language Mixture-of-Transformers (VL-MoT)} framework. This framework enables shared attention between the vision foundation model and the VLA backbone, injecting multi-level visual features from the vision expert into deeper layers of the VLA backbone to enhance visual representations for precise and complex manipulation. In addition, we introduce \textbf{Action-Guided Visual Pruning (AGVP)}, which leverages shallow-layer attention to prune irrelevant visual tokens while preserving task-relevant ones, reinforcing critical visual cues for manipulation with minimal computational overhead. DeepVision-VLA outperforms prior state-of-the-art methods by 9.0\% and 7.5\% on simulated and real-world tasks, respectively, providing new insights for the design of visually enhanced VLA models.

            {
  "tldr": "This paper introduces DeepVision-VLA, a framework that enhances Vision-Language-Action (VLA) models by improving visual representation sensitivity in deeper layers, leading to better action prediction in robotic manipulation tasks.",
  "whats_new": [
    "• Proposes the Vision-Language Mixture-of-Transformers (VL-MoT) framework for improved visual grounding.",
    "• Introduces Action-Guided Visual Pruning (AGVP) to filter irrelevant visual tokens.",
    "• Achieves state-of-the-art performance, outperforming prior methods by 9.0% in simulations and 7.5% in real-world tasks.",
    "• Provides insights into the internal processing of visual information in VLA models."
  ],
  "method": "The authors analyze existing VLA models to identify weaknesses in visual token sensitivity in deeper layers. They propose DeepVision-VLA, which integrates multi-level visual features from a Vision Expert into deeper layers of the VLA backbone, enhancing action prediction. AGVP is introduced to prune irrelevant visual tokens based on shallow-layer attention.",
  "results": [
    "• DeepVision-VLA achieves an 83% mean success rate on RLBench, outperforming all baselines.",
    "• Outperforms QwenVLA-OFT by 14% in simulated tasks.",
    "• Achieves a 100% success rate in the 'pour coke to bottle' task.",
    "• Maintains high performance under unseen background and lighting conditions."
  ],
  "limitations": [
    "• The framework's reliance on a specific Vision Expert model may limit generalizability.",
    "• Further exploration of alternative visual foundation models is needed.",
    "• The computational efficiency of the proposed methods in larger-scale applications remains to be evaluated."
  ],
  "why_useful": "This paper is significant for advancing robotic manipulation capabilities by enhancing the integration of visual and language information. The proposed methods can be applied to improve various VLA tasks, making robots more effective in real-world scenarios.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-03-16 17:59:05

Authors: Zhenghong Zhou, Xiaohang Zhan, Zhiqin Chen, Soo Ye Kim, Nanxuan Zhao, Haitian Zheng, Qing Liu, He Zhang, Zhe Lin, Yuqian Zhou, Jiebo Luo

Categories: cs.CV

Abstract:
Recent video diffusion models have made remarkable strides in visual quality, yet precise, fine-grained control remains a key bottleneck that limits practical customizability for content creation. For AI video creators, three forms of control are crucial: (i) scene composition, (ii) multi-view consistent subject customization, and (iii) camera-pose or object-motion adjustment. Existing methods typically handle these dimensions in isolation, with limited support for multi-view subject synthesis and identity preservation under arbitrary pose changes. This lack of a unified architecture makes it difficult to support versatile, jointly controllable video. We introduce Tri-Prompting, a unified framework and two-stage training paradigm that integrates scene composition, multi-view subject consistency, and motion control. Our approach leverages a dual-condition motion module driven by 3D tracking points for background scenes and downsampled RGB cues for foreground subjects. To ensure a balance between controllability and visual realism, we further propose an inference ControlNet scale schedule. Tri-Prompting supports novel workflows, including 3D-aware subject insertion into any scenes and manipulation of existing subjects in an image. Experimental results demonstrate that Tri-Prompting significantly outperforms specialized baselines such as Phantom and DaS in multi-view subject identity, 3D consistency, and motion accuracy.

            {
  "tldr": "Tri-Prompting introduces a unified video diffusion framework that allows for comprehensive control over scene, subject, and motion, significantly improving video generation quality and consistency.",
  "whats_new": [
    "• A unified framework integrating scene, subject, and motion control.",
    "• Dual-conditioning motion control using 3D tracking and low-resolution RGB proxies.",
    "• Outperforms specialized models like Phantom and DaS in multi-view identity and motion accuracy.",
    "• Enables novel applications such as 3D subject insertion and manipulation."
  ],
  "method": "Tri-Prompting employs a two-stage training strategy. The first stage focuses on integrating scene and multi-view subject images, while the second stage incorporates a ControlNet for dual-conditioning motion control, using both XYZ trajectories and low-resolution RGB grids.",
  "results": [
    "• Achieves higher PSNR and LPIPS scores than DaS in video reconstruction tasks.",
    "• Outperforms Phantom by 26.5% in 3D consistency metrics.",
    "• Maintains robust motion alignment under extreme actions, reducing identity inconsistencies.",
    "• Trains efficiently with only 11k tuples in less than 7 hours."
  ],
  "limitations": [
    "• Subject ambiguity can occur with highly symmetric multi-view references.",
    "• Currently supports offline generation; real-time interaction is not yet feasible.",
    "• High inference costs due to the model's complexity."
  ],
  "why_useful": "This paper is significant for AI video creators, providing a versatile tool for generating high-quality videos with precise control over various elements. Its applications extend to creative fields requiring dynamic scene and subject manipulation.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-03-16 17:56:58

Authors: Yi-Ting Lee, Keerthi Kumaran, Bibek Pokharel, Allen Scheie, Colin L. Sarkis, David A. Tennant, Travis Humble, André Schleife, Abhinav Kandala, Arnab Banerjee

Categories: quant-ph, cond-mat.str-el

Abstract:
A central goal of quantum computation is the realistic simulation of quantum materials. Although quantum processors have advanced rapidly in scale and fidelity, it has remained unclear whether pre-fault-tolerant devices can perform quantitatively reliable material simulations within their limited gate budgets. Here, we demonstrate that a superconducting quantum processor operating on up to 50 qubits can already produce meaningful, quantitative comparisons with inelastic neutron-scattering measurements of KCuF$_3$, a canonical realization of a gapless Luttinger liquid system with a strongly correlated ground state and a spectrum of emergent spinons. The quantum simulation is enabled by a quantum-classical workflow for computing dynamical structure factors (DSFs). The resulting spectra are benchmarked against experimental measurements using multiple metrics, highlighting the impact of circuit depth and circuit fidelity on simulation accuracy. Finally, we extend our simulations to 1D XXZ Heisenberg model with next-nearest neighbor interactions and a strong anisotropy, producing a gapped excitation spectrum, which could be used to describe the CsCoX$_3$ compounds above the Néel temperature. Our results establish a framework for computing DSFs for quantum materials in classically challenging regimes of strong entanglement and long-range interactions, enabling quantum simulations that are directly testable against laboratory measurements.

            {
  "tldr": "This paper demonstrates that a 50-qubit superconducting quantum processor can effectively simulate the dynamical structure factor of KCuF3, a quantum material, providing a benchmark against inelastic neutron scattering experiments.",
  "whats_new": [
    "• Establishes a quantum-classical workflow for computing dynamical structure factors (DSFs).",
    "• Demonstrates meaningful comparisons between quantum simulations and experimental neutron scattering data.",
    "• Extends simulations to complex models like the 1D XXZ Heisenberg model with next-nearest neighbor interactions.",
    "• Introduces rigorous quantitative metrics for assessing simulation fidelity."
  ],
  "method": "The authors employed a quantum-classical workflow to compute DSFs from retarded Green's functions using a superconducting quantum processor. They benchmarked the quantum results against inelastic neutron scattering measurements of KCuF3, focusing on the impact of circuit depth and fidelity on accuracy.",
  "results": [
    "• Quantum simulations reproduced key features of the INS spectrum of KCuF3 with a mean squared error comparable to classical methods.",
    "• Achieved approximately 50% spectral weight agreement with experimental data.",
    "• Demonstrated that circuit depth and fidelity significantly affect simulation accuracy.",
    "• Extended simulations to a non-integrable XXZ model, capturing essential features of interchain interactions."
  ],
  "limitations": [
    "• Quantum noise led to broadening of spectral signals, affecting low-frequency accuracy.",
    "• Current quantum hardware limitations restrict the simulation of larger and more complex systems.",
    "• The results are sensitive to the choice of metrics used for benchmarking."
  ],
  "why_useful": "This work is significant as it establishes quantum processors as practical tools for simulating quantum materials, bridging the gap between theoretical models and experimental data. It opens avenues for exploring complex quantum systems that are challenging for classical simulations.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-03-16 17:55:56

Authors: Moji Shi, Rajitha de Silva, Hang Yu, Riccardo Polvara, Marija Popović

Categories: cs.RO

Abstract:
Autonomous exploration in unknown environments typically relies on onboard state estimation for localisation and mapping. Existing exploration methods primarily maximise coverage efficiency, but often overlook that visual-inertial odometry (VIO) performance strongly depends on the availability of robust visual features. As a result, exploration policies can drive a robot into feature-sparse regions where tracking degrades, leading to odometry drift, corrupted maps, and mission failure. We propose a hierarchical perception-aware exploration framework for a stereo-equipped unmanned aerial vehicle (UAV) that explicitly couples exploration progress with feature observability. Our approach (i) associates each candidate frontier with an expected feature quality using a global feature map, and prioritises visually informative subgoals, and (ii) optimises a continuous yaw trajectory along the planned motion to maintain stable feature tracks. We evaluate our method in simulation across environments with varying texture levels and in real-world indoor experiments with largely textureless walls. Compared to baselines that ignore feature quality and/or do not optimise continuous yaw, our method maintains more reliable feature tracking, reduces odometry drift, and achieves on average 30\% higher coverage before the odometry error exceeds specified thresholds.

            {
  "tldr": "This paper presents a perception-aware exploration framework for UAVs that improves feature tracking in feature-limited environments, enhancing odometry reliability and exploration success rates.",
  "whats_new": [
    "• Introduces a hierarchical framework that integrates feature quality into exploration and trajectory planning.",
    "• Prioritizes visually informative subgoals to enhance feature observability during exploration.",
    "• Optimizes continuous yaw trajectories to maintain stable feature tracking.",
    "• Achieves 30% higher coverage before exceeding odometry error thresholds compared to baseline methods."
  ],
  "method": "The proposed framework selects exploration subgoals based on expected feature quality and optimizes yaw trajectories to maintain visual observability. It uses a global feature map to assess feature quality and employs a two-stage trajectory optimization process for safe and perceptually reliable navigation.",
  "results": [
    "• Achieves higher exploration success rates across varying texture environments, outperforming baselines by significant margins.",
    "• Maintains more reliable feature tracking, with higher feature counts per frame during exploration.",
    "• Reduces odometry drift, leading to successful exploration under practical VIO error thresholds.",
    "• Demonstrates effective performance in real-world experiments with largely textureless environments."
  ],
  "limitations": [
    "• The framework's performance may degrade in extremely feature-sparse environments.",
    "• Future work is needed to extend the approach for multi-robot scenarios.",
    "• The impact of different feature types on performance is not fully explored."
  ],
  "why_useful": "This paper addresses a critical challenge in autonomous exploration by integrating perceptual considerations into exploration strategies. Its findings can enhance the reliability of UAVs in real-world applications, particularly in environments with limited visual features.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code & data available at https://arxiv.org/abs/2603.15605v1"
  }
}
            

arXiv Page | PDF

Published: 2026-03-16 17:53:06

Authors: Naoto Tani, Futoshi Futami

Categories: cs.LG

Abstract:
We study linear contextual bandits under adversarial corruption and heavy-tailed noise with finite $(1+ε)$-th moments for some $ε\in (0,1]$. Existing work that addresses both adversarial corruption and heavy-tailed noise relies on a finite variance (i.e., finite second-moment) assumption and suffers from computational inefficiency. We propose a computationally efficient algorithm based on online mirror descent that achieves robustness to both adversarial corruption and heavy-tailed noise. While the existing algorithm incurs $\mathcal{O}(t\log T)$ computational cost, our algorithm reduces this to $\mathcal{O}(1)$ per round. We establish an additive regret bound consisting of a term depending on the $(1+ε)$-moment bound of the noise and a term depending on the total amount of corruption. In particular, when $ε= 1$, our result recovers existing guarantees under finite-variance assumptions. When no corruption is present, it matches the best-known rates for linear contextual bandits with heavy-tailed noise. Moreover, the algorithm requires no prior knowledge of the noise moment bound or the total amount of corruption and still guarantees sublinear regret.

            {
  "tldr": "This paper presents a new algorithm for linear contextual bandits that effectively handles adversarial corruption and heavy-tailed noise, achieving computational efficiency with O(1) updates per round.",
  "whats_new": [
    "• Introduces CR-Hvt-UCB, a novel algorithm for linear contextual bandits.",
    "• Achieves robustness against both adversarial corruption and heavy-tailed noise under bounded (1 + ε)-moment conditions.",
    "• Reduces computational cost from O(t log T) to O(1) per round.",
    "• Provides regret guarantees even when noise moment bounds and corruption levels are unknown."
  ],
  "method": "The proposed algorithm uses online mirror descent (OMD) combined with a Huber-based loss function. It adaptively scales the loss to mitigate the effects of adversarial corruption and heavy-tailed noise, ensuring both robustness and computational efficiency.",
  "results": [
    "• The algorithm achieves a regret bound of ̃O(dT^(1−ε)/(2(1+ε)) √∑Tt=1ν²t + dT^(1−ε)/(2(1+ε)) C).",
    "• When ε = 1, it recovers existing guarantees for finite-variance noise.",
    "• In the absence of corruption, it matches the best-known rates for linear contextual bandits with heavy-tailed noise.",
    "• Experimental results show significantly lower runtime compared to existing methods."
  ],
  "limitations": [
    "• The algorithm's performance may degrade with high levels of corruption.",
    "• The theoretical guarantees assume bounded (1 + ε)-moment conditions, which may not hold in all scenarios.",
    "• Future work is needed to extend the framework beyond linear models."
  ],
  "why_useful": "This paper addresses critical challenges in linear contextual bandits by providing a robust and efficient algorithm suitable for real-world applications, such as online advertising and recommendation systems, where adversarial corruption and heavy-tailed noise are prevalent.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-03-16 17:51:57

Authors: Zachery A. Enderson, Jiyuan Fang, Wei-Chen Wang, Li Xiang, Mykhaylo Ozerov, Dmitry Smirnov, Zhigang Jiang, Samuel D. Hawkins, Aaron J. Muhowski, John F. Klem, Wei Pan

Categories: cond-mat.mes-hall

Abstract:
Quantum materials constitute a novel category of substances wherein quantum effects and electron-electron (e-e) interactions give rise to unforeseen phenomena on a macroscopic scale. Of particular interest within the realm of quantum materials are flat bands, which promote heavy conduction electrons and enhance e-e correlation effects. While the engineering of such flat bands has been demonstrated in graphene and two-dimensional transition metal dichalcogenides moiré superlattices and in lithography defined semiconductor moiré superlattices, conventional tear-and-stack fabrication methods face challenges due to inevitable twist-angle disorder, strain, and relaxation effects, leading to issues with reproducibility and scalability. Here, we explore the creation and modification of flat bands through vertically engineered III-V semiconductor heterostructures, without the need for twisting. These artificial quantum materials offer a reproducible and scalable means for producing high-quality flat-band materials via molecular beam epitaxy growth. Our investigation includes magnetotransport and infrared magneto-spectroscopy studies of quad-layer InAs/GaSb quantum wells, accompanied by k*p band structure calculations, which illustrate the flattening of bands in vertically designed heterostructures.

            {
  "tldr": "This paper presents a method for generating flat bands in InAs/GaSb quantum wells using vertically engineered heterostructures, enhancing electron-electron interactions without twisting. The approach is scalable and reproducible.",
  "whats_new": [
    "• Introduces a novel method for creating flat bands in quantum materials without twist-angle disorder.",
    "• Utilizes quad-layer InAs/GaSb quantum wells for enhanced band flattening.",
    "• Combines experimental magnetotransport and theoretical k·p calculations to validate findings.",
    "• Achieves effective mass values significantly higher than those reported for InAs."
  ],
  "method": "The study employs molecular beam epitaxy to grow quad-layer InAs/GaSb quantum wells. It combines experimental magnetotransport measurements with eight-band k·p band structure calculations to analyze the electronic properties and band flattening.",
  "results": [
    "• Achieved a total carrier density of 1.9 × 10^11 cm^-2 with a Hall mobility of 7.3 × 10^4 cm^2/V·s.",
    "• Effective mass measured at 0.053 m_e from temperature-dependent SdH oscillations.",
    "• Effective mass from FIRMS measurements was found to be 0.055 m_e.",
    "• The model predicts a significant flattening of bands around the Γ-point."
  ],
  "limitations": [
    "• The impact of unintentional doping on band structure is not fully explored.",
    "• The effects of finite doping on band gap closing are not completely accounted for in the k·p model.",
    "• Further investigation is needed to understand the splitting observed in cyclotron resonance."
  ],
  "why_useful": "This research provides a scalable method for producing flat-band materials, which can enhance electron-electron correlations and potentially lead to new superconducting materials. It opens avenues for exploring flat-band physics in practical applications.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "All data needed to evaluate the conclusions in the paper are present in the paper."
  }
}
            

arXiv Page | PDF

Published: 2026-03-16 17:49:15

Authors: Andrea Bonato, Davide Marenduzzo, Enzo Orlandini, Giuseppe Negro

Categories: cond-mat.soft

Abstract:
Nonequilibrium active polymers provide a minimal framework to investigate biopolymers such as DNA and chromatin under the action of molecular motors. Here we study active ring polymers with controlled topology and show that knot type qualitatively determines their nonequilibrium behaviour. We find that activity induces opposite localisation responses in different topological families: torus knots systematically delocalise and inflate, whereas twist knots tighten and remain localised. We trace this divergent behaviour to the distinct symmetry properties of their tangent fields, which control the alignment of active forces along the chain. We show that topology also governs internal and emergent dynamics. Active torus knots behave as soft chiral self-propelled particles exhibiting persistent motion with a well-defined handedness fixed by their topological chirality. In contrast, achiral knots show no net handedness. The knot thus acts as a deformable topological quasiparticle whose morphology and propulsion are selected by topology. These results suggest potential routes toward programmable soft chiral particles with controllable morphology and emergent motility modes.

            {
  "tldr": "This study explores how the topology of active ring polymers affects their nonequilibrium behavior, revealing that torus knots delocalize while twist knots localize under activity. The findings suggest that topology can control the dynamics and morphology of soft chiral particles.",
  "whats_new": [
    "• Demonstrates that knot type influences localization and motility in active polymers.",
    "• Identifies distinct behaviors for torus and twist knots under active forcing.",
    "• Establishes that topology governs internal dynamics and emergent motility modes.",
    "• Suggests potential for programmable soft chiral particles based on topology."
  ],
  "method": "The study employs molecular dynamics simulations of active knots with varying topologies. It uses a custom modification of LAMMPS to implement activity, examining the effects of active forces on knot behavior and dynamics.",
  "results": [
    "• Torus knots delocalize and inflate with increased activity, while twist knots tighten and localize.",
    "• Active torus knots exhibit persistent motion with a defined handedness, unlike achiral knots.",
    "• The average size of the knotted region varies significantly with activity, expanding for torus knots and contracting for twist knots.",
    "• Torus knots show greater directional persistence in motion compared to twist knots."
  ],
  "limitations": [
    "• The study focuses on a limited number of knot types, leaving broader implications unexplored.",
    "• The effects of collective behavior in active knot systems remain to be investigated.",
    "• Potential interactions between multiple knots on the same chain are not addressed."
  ],
  "why_useful": "This research enhances understanding of how topology influences the dynamics of active polymers, which is relevant for designing synthetic materials and understanding biological processes involving DNA and chromatin. It opens avenues for creating programmable soft matter with tailored properties.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-03-16 17:46:58

Authors: Anton Kolonin, Vladimir Krykov

Categories: cs.AI, eess.SY

Abstract:
This article presents an overview of approaches to modeling the human psyche in the context of constructing an artificial one. Based on this overview, a concept of cognitive architecture is proposed, in which the psyche is viewed as the operating system of a living or artificial subject, comprising a space of states, including the state of needs that determine the meaning of a subject's being in relation to stimuli from the external world, and intelligence as a decision-making system regarding actions in this world to satisfy these needs. Based on this concept, a computational formalization is proposed for creating artificial general intelligence systems for an agent through experiential learning in a state space that includes agent's needs, taking into account their biological or existential significance for the intelligent agent, along with agent's sensations and actions. Thus, the problem of constructing artificial general intelligence is formalized as a system for making optimal decisions in the space of specific agent needs under conditions of uncertainty, maximizing success in achieving goals, minimizing existential risks, and maximizing energy efficiency. A minimal experimental implementation of the model is presented.

            {
  "tldr": "This paper proposes a computational model of the human psyche aimed at creating artificial general intelligence (AGI). It introduces a cognitive architecture that integrates decision-making based on an agent's needs and experiences.",
  "whats_new": [
    "• Proposes a novel cognitive architecture for artificial agents based on the human psyche.",
    "• Introduces a 'space of needs' as a core component for decision-making.",
    "• Emphasizes energy efficiency and risk management in AGI development.",
    "• Presents a minimal experimental implementation to validate the model."
  ],
  "method": "The authors develop a cognitive architecture that models the psyche as an operating system for artificial agents. This architecture incorporates a space of states and needs, enabling decision-making through experiential learning and optimization under uncertainty.",
  "results": [
    "• The model formalizes AGI as a system for making optimal decisions in a state space reflecting agent needs.",
    "• An experimental implementation demonstrated the agent's ability to learn through reinforcement in a ping-pong game.",
    "• Positive reinforcement was found to enhance learning stability compared to negative reinforcement.",
    "• The architecture allows for dynamic decision-making based on the agent's internal and external states."
  ],
  "limitations": [
    "• The experimental implementation is minimal and may not fully represent complex real-world scenarios.",
    "• Further research is needed to enhance training stability and energy efficiency considerations.",
    "• The model's applicability to diverse environments remains to be tested."
  ],
  "why_useful": "This paper contributes to the understanding of AGI by providing a structured approach to modeling the psyche, which can enhance the development of intelligent systems. Its implications extend to various applications, including industrial automation and smart home technologies.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code & data available at https://github.com/aigents/aigentsjava/tree/master/src/main/java/net/webstructor/agi"
  }
}
            

arXiv Page | PDF

Published: 2026-03-16 17:46:28

Authors: Ludovic Van Waerbeke

Categories: hep-ph, astro-ph.CO

Abstract:
The nature of dark energy remains a central problem in cosmology. A compelling possibility is that dark matter is macroscopic, consisting of composite objects formed in the early Universe. We introduce the QCD-AQN framework, a well-motivated scenario in which dark matter is composed of dense aggregates of quarks and antiquarks matter stabilised by axion domain walls. The framework proposes a unified explanation for both dark matter and the observed matter-antimatter asymmetry. Particular emphasis is placed on existing observational constraints and on observational tests. Finally, we explore a possible QCD-based scenario for dark energy.

            {
  "tldr": "This paper introduces the QCD-AQN framework, proposing that dark matter consists of macroscopic aggregates of quark and antiquark matter, and explores its implications for dark energy and observational tests.",
  "whats_new": [
    "• Proposes a unified explanation for dark matter and matter-antimatter asymmetry.",
    "• Introduces the concept of Axion Quark Nuggets (AQNs) formed during the QCD phase transition.",
    "• Emphasizes multi-wavelength testability and observational constraints.",
    "• Explores a QCD-based scenario for dark energy."
  ],
  "method": "The paper reviews observational constraints on dark matter and discusses the theoretical foundations of the QCD-AQN model. It examines the structure and properties of AQNs, their cosmological behavior, and potential observational signatures across various astrophysical environments.",
  "results": [
    "• AQNs are predicted to have a mass range of approximately 10^-2 to 10^4 grams.",
    "• The mean AQN mass is constrained to be between 1 and 100 grams based on observational data.",
    "• AQNs contribute to observable signatures in the Galactic bulge and cosmic background radiation.",
    "• The framework predicts a correlation between AQN emissions and baryonic matter."
  ],
  "limitations": [
    "• The exact mass distribution of AQNs cannot be precisely computed from first principles.",
    "• The framework relies on several assumptions about QCD dynamics and axion behavior.",
    "• Observational tests are still needed to validate the predictions of the QCD-AQN model."
  ],
  "why_useful": "This paper is significant as it offers a novel perspective on dark matter, linking it to well-established QCD physics. Its implications for dark energy and the potential for observational tests make it relevant for ongoing research in cosmology and particle physics.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-03-16 17:45:28

Authors: Matej Martinc, Goran Dražič, Anton Kokalj, Katarina Žiberna, Janina Roknić, Matic Poberžnik, Sašo Džeroski, Andreja Benčan Golob

Categories: cond-mat.mtrl-sci, cs.CV

Abstract:
Four-dimensional scanning transmission electron microscopy (4D-STEM) provides rich, atomic-scale insights into materials structures. However, extracting specific physical properties - such as polarization directions essential for understanding functional properties of ferroelectrics - remains a significant challenge. In this study, we systematically benchmark multiple machine learning models, namely ResNet, VGG, a custom convolutional neural network, and PCA-informed k-Nearest Neighbors, to automate the detection of polarization directions from 4D-STEM diffraction patterns in ferroelectric potassium sodium niobate. While models trained on synthetic data achieve high accuracy on idealized synthetic diffraction patterns of equivalent thickness, the domain gap between simulation and experiment remains a critical barrier to real-world deployment. In this context, a custom made prototype representation training regime and PCA-based methods, combined with data augmentation and filtering, can better bridge this gap. Error analysis reveals periodic missclassification patterns, indicating that not all diffraction patterns carry enough information for a successful classification. Additionally, our qualitative analysis demonstrates that irregularities in the model's prediction patterns correlate with defects in the crystal structure, suggesting that supervised models could be used for detecting structural defects. These findings guide the development of robust, transferable machine learning tools for electron microscopy analysis.

            {
  "tldr": "This study benchmarks various machine learning models for detecting polarization directions in ferroelectric materials using 4D-STEM data. It highlights the challenges of transferring models from synthetic to experimental data and proposes methods to bridge this gap.",
  "whats_new": [
    "• Systematic comparison of multiple machine learning architectures for polarization mapping.",
    "• Introduction of a prototype representation approach to improve model transferability.",
    "• Detailed error analysis revealing periodic misclassification patterns related to crystal defects.",
    "• Demonstration of PCA-based methods outperforming standard deep learning models in experimental settings."
  ],
  "method": "The study benchmarks ResNet, VGG, a custom CNN, and PCA-informed k-NN for polarization classification in 4D-STEM data. It employs synthetic datasets for training and evaluates model performance on experimental data, focusing on bridging the domain gap through data augmentation and filtering.",
  "results": [
    "• Models achieved over 99% accuracy on synthetic datasets with 20 nm thickness.",
    "• VGG (Reg) model reached over 40% accuracy on experimental datasets.",
    "• PCA-based methods correctly predicted majority classes for all experimental datasets in two out of three preprocessing configurations.",
    "• Significant domain gap observed, with models failing on thicker samples (50 nm)."
  ],
  "limitations": [
    "• Performance declines for thicker samples due to multiple scattering effects.",
    "• High variance in model predictions raises concerns about reliability.",
    "• Limited generalizability of models trained on synthetic data to real-world scenarios.",
    "• Defect detection capabilities remain uncertain due to preprocessing impacts."
  ],
  "why_useful": "This research is significant for advancing machine learning applications in materials science, particularly for characterizing ferroelectric materials. The findings can inform future developments in automated electron microscopy analysis and defect detection.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code for polarization classification available at https://github.com/matejMartinc/4D-STEM-polarization-mapping; data available at https://kt-cloud.ijs.si/index.php/s/yKqWe26tAF5tgC5."
  }
}
            

arXiv Page | PDF

Published: 2026-03-16 17:24:56

Authors: Yunling Chen, Dinh Tuan Huynh

Categories: math.CV, math.DG

Abstract:
We apply the technique of jet differentials to establish a Gauss curvature estimate for an open Riemann surface $M$, equipped with a conformal metric induced from a nonconstant holomorphic map that is highly ramified over a generic hypersurface of sufficiently high degree.

arXiv Page | PDF

Published: 2026-03-16 17:16:59

Authors: Haoze Zheng, Zihao Wang, Xianfeng Wu, Yajing Bai, Yexin Liu, Yun Li, Xiaogang Xu, Harry Yang

Categories: cs.CV

Abstract:
Single-image relighting is highly under-constrained: small illumination changes can produce large, nonlinear variations in shading, shadows, and specularities, while geometry and materials remain unobserved. Existing diffusion-based approaches either rely on intrinsic or G-buffer pipelines that require dense and fragile supervision, or operate purely in latent space without physical grounding, making fine-grained control of direction, intensity, and color unreliable. We observe that a full intrinsic decomposition is unnecessary and redundant for accurate relighting. Instead, sparse but physically meaningful cues, indicating where illumination should change and how materials should respond, are sufficient to guide a diffusion model. Based on this insight, we introduce LightCtrl that integrates physical priors at two levels: a few-shot latent proxy encoder that extracts compact material-geometry cues from limited PBR supervision, and a lighting-aware mask that identifies sensitive illumination regions and steers the denoiser toward shading relevant pixels. To compensate for scarce PBR data, we refine the proxy branch using a DPO-based objective that enforces physical consistency in the predicted cues. We also present ScaLight, a large-scale object-level dataset with systematically varied illumination and complete camera-light metadata, enabling physically consistent and controllable training. Across object and scene level benchmarks, our method achieves photometrically faithful relighting with accurate continuous control, surpassing prior diffusion and intrinsic-based baselines, including gains of up to +2.4 dB PSNR and 35% lower RMSE under controlled lighting shifts.

arXiv Page | PDF

Published: 2026-03-16 17:09:30

Authors: Davis Rempe, Mathis Petrovich, Ye Yuan, Haotian Zhang, Xue Bin Peng, Yifeng Jiang, Tingwu Wang, Umar Iqbal, David Minor, Michael de Ruyter, Jiefeng Li, Chen Tessler, Edy Lim, Eugene Jeong, Sam Wu, Ehsan Hassani, Michael Huang, Jin-Bey Yu, Chaeyeon Chung, Lina Song, Olivier Dionne, Jan Kautz, Simon Yuen, Sanja Fidler

Categories: cs.CV, cs.GR, cs.RO

Abstract:
High-quality human motion data is becoming increasingly important for applications in robotics, simulation, and entertainment. Recent generative models offer a potential data source, enabling human motion synthesis through intuitive inputs like text prompts or kinematic constraints on poses. However, the small scale of public mocap datasets has limited the motion quality, control accuracy, and generalization of these models. In this work, we introduce Kimodo, an expressive and controllable kinematic motion diffusion model trained on 700 hours of optical motion capture data. Our model generates high-quality motions while being easily controlled through text and a comprehensive suite of kinematic constraints including full-body keyframes, sparse joint positions/rotations, 2D waypoints, and dense 2D paths. This is enabled through a carefully designed motion representation and two-stage denoiser architecture that decomposes root and body prediction to minimize motion artifacts while allowing for flexible constraint conditioning. Experiments on the large-scale mocap dataset justify key design decisions and analyze how the scaling of dataset size and model size affect performance.

arXiv Page | PDF

Published: 2026-03-16 17:01:52

Authors: The LHAASO Collaboration

Categories: astro-ph.HE

Abstract:
Pulsar wind nebulae (PWNe) are bubbles of relativistic particles, powered by the rotational energy loss of the central pulsars. The Crab Nebula, powered by the Milky Way's most energetic pulsar, was discovered by the Large High Altitude Air Shower Observatory (LHAASO) as a PeV gamma-ray emitter, thereby establishing it as an extreme particle accelerator along with multiwavelength observations. Here we report LHAASO's detection of a point-like ultrahigh-energy (UHE, photon energy $E>100\,$TeV) gamma-ray source associated with the PWN powered by PSR~J1849-0001, a pulsar of spindown power 50 times lower than the Crab pulsar. The measured gamma-ray spectrum extends to PeV energies following a power-law distribution, with the PeV luminosity a few times higher than that of the Crab Nebula. Combined X-ray observations constrain the average magnetic field within the PWN to about $3μ\,$G, and reveal an extreme particle acceleration efficiency approaching or even exceeding unity. The result challenges the particle acceleration theory in PWN and implies non-ideal magnetohydrodynamics (MHD) conditions within the accelerator, potentially involving magnetic reconnection upstream of the termination shock.

arXiv Page | PDF

Published: 2026-03-16 16:53:08

Authors: Zhenheng Tang, Xiang Liu, Qian Wang, Eunsol Choi, Bo Li, Xiaowen Chu

Categories: cs.AI, cs.CY

Abstract:
As Large Language Models (LLMs) become more powerful and autonomous, they increasingly face conflicts and dilemmas in many scenarios. We first summarize and taxonomize these diverse conflicts. Then, we model the LLM's preferences to make different choices as a priority graph, where instructions and values are nodes, and the edges represent context-specific priorities determined by the model's output distribution. This graph reveals that a unified stable LLM alignment is very challenging, because the graph is neither static nor necessarily consistent in different contexts. Besides, it also reveals a potential vulnerability: priority hacking, where adversaries can craft deceptive contexts to manipulate the graph and bypass safety alignments. To counter this, we propose a runtime verification mechanism, enabling LLMs to query external sources to ground their context and resist manipulation. While this approach enhances robustness, we also acknowledge that many ethical and value dilemmas are philosophically irreducible, posing a long-term, open challenge for the future of AI alignment.

arXiv Page | PDF

Published: 2026-03-16 16:47:45

Authors: David Števaňák, Marek Šuppa

Categories: cs.CL, cs.AI

Abstract:
Keyphrase extraction for morphologically rich, low-resource languages remains understudied, largely due to the scarcity of suitable evaluation datasets. We address this gap for Slovak by constructing a dataset of 227,432 scientific abstracts with author-assigned keyphrases -- scraped and systematically cleaned from the Slovak Central Register of Theses -- representing a 25-fold increase over the largest prior Slovak resource and approaching the scale of established English benchmarks such as KP20K. Using this dataset, we benchmark three unsupervised baselines (YAKE, TextRank, KeyBERT with SlovakBERT embeddings) and evaluate KeyLLM, an LLM-based extraction method using GPT-3.5-turbo. Unsupervised baselines achieve at most 11.6\% exact-match $F1@6$, with a large gap to partial matching (up to 51.5\%), reflecting the difficulty of matching inflected surface forms to author-assigned keyphrases. KeyLLM narrows this exact--partial gap, producing keyphrases closer to the canonical forms assigned by authors, while manual evaluation on 100 documents ($κ= 0.61$) confirms that KeyLLM captures relevant concepts that automated exact matching underestimates. Our analysis identifies morphological mismatch as the dominant failure mode for statistical methods -- a finding relevant to other inflected languages. The dataset (https://huggingface.co/datasets/NaiveNeuron/SlovKE) and evaluation code (https://github.com/NaiveNeuron/SlovKE) are publicly available.

arXiv Page | PDF

Published: 2026-03-16 16:47:43

Authors: Georgios Maragkopoulos, Aikaterini Mandilara, Ralntion Komini, Dimitris Syvridis

Categories: quant-ph

Abstract:
Multispectral satellite imagery poses significant challenges for deep learning models due to the high dimensionality of spectral data and the presence of structured correlations across channels. Recent work in quantum machine learning suggests that unitary evolutions and Hilbert-space embeddings can introduce useful inductive biases for learning. In this work, we show that several empirical advantages often attributed to quantum feature maps can be more precisely understood as consequences of geometric structure induced by unitary group actions and the associated quotient symmetries. Motivated by this observation, we introduce a fully classical pooling mechanism that maps latent features to complex projective space via a fixed-reference unitary action. This construction effectively collapses non-identifiable degrees of freedom, leading to a reduction in the dimensionality of the learned representations. Empirical results on multispectral satellite imagery show that incorporating this quantum-inspired pooling operation into a convolutional neural network improves optimization stability, accelerates convergence, and substantially reduces variance compared to standard pooling baselines. These results clarify the role of geometric structure in quantum-inspired architectures and demonstrate that their benefits can be reproduced through principled geometric inductive biases implemented entirely within classical deep learning models.

arXiv Page | PDF

Published: 2026-03-16 16:44:54

Authors: Xiyu Liu, Qingyi Si, Zhengxiao Liu, Chenxu Yang, Naibin Gu, Zheng Lin

Categories: cs.CL

Abstract:
While locate-then-edit knowledge editing efficiently updates knowledge encoded within Large Language Models (LLMs), a critical generalization failure mode emerges in the practical same-subject knowledge editing scenario: models fail to recall the updated knowledge when following user instructions, despite successfully recalling it in the original edited form. This paper identifies the geometric root of this generalization collapse as a fundamental conflict where the inner activation drifts induced by prompt variations exceed the model's geometric tolerance for generalization after editing. We attribute this instability to a dual pathology: (1) The joint optimization with orthogonal gradients collapses solutions into sharp minima with narrow stability, and (2) the standard covariance constraint paradoxically acts as a Covariance Trap that amplifies input perturbations. To resolve this, we introduce RoSE (Robust Same-subject Editing), which employs Isotropic Geometric Alignment to minimize representational deviation and Hierarchical Knowledge Integration to smooth the optimization landscape. Extensive experiments demonstrate that RoSE significantly improves instruction-following capabilities, laying the foundation for robust interactive parametric memory of LLM agents.

arXiv Page | PDF

Published: 2026-03-16 16:35:01

Authors: Nitin Priyadarshini Shankar, Soham Lahiri, Sheetal Kalyani, Saurav Prakash

Categories: cs.LG, cs.CV

Abstract:
Federated Learning (FL) preserves privacy by distributing training across devices. However, using DNNs is computationally intensive at the low-powered edge during inference. Edge deployment demands models that simultaneously optimize memory footprint and computational efficiency, a dilemma where conventional DNNs fail by exceeding resource limits. Traditional post-training binarization reduces model size but suffers from severe accuracy loss due to quantization errors. To address these challenges, we propose FedBNN, a rotation-aware binary neural network framework that learns binary representations directly during local training. By encoding each weight as a single bit $\{+1, -1\}$ instead of a $32$-bit float, FedBNN shrinks the model footprint, significantly reducing runtime (during inference) FLOPs and memory requirements in comparison to federated methods using real models. Evaluations across multiple benchmark datasets demonstrate that FedBNN significantly reduces resource consumption while performing similarly to existing federated methods using real-valued models.

arXiv Page | PDF

Published: 2026-03-16 16:30:12

Authors: J. R. Sauter, A. von Stauffenberg, G. Bourdarot, W. Brandner, F. Eisenhauer, L. Kreidberg, L. Labadie, S. Scheithauer, D. Trevascus, R. van Boekel

Categories: astro-ph.IM, astro-ph.EP

Abstract:
The implementation of the GRAVITY+ Adaptive Optics (GPAO) system at VLTI enables unprecedented sensitivity and stability in optical interferometry. This allows high-precision characterization of directly imaged exoplanets at medium spectral resolution, providing a new pathway for studying planetary atmospheres. We aim to quantify and characterize the short- and long-term stability of GRAVITY+ through a consecutive seven-hour observation of the bright and stable star beta Pictoris, providing a benchmark for future exoplanet observations. We developed SIMTERFERE, a data-driven simulation tool that reproduces GRAVITY+ on-star observations using ancillary instrument and telemetry data. By comparing the simulations with the measured coherent fluxes, we traced the origins of systematic flux variations and assessed their impact on exoplanet contrast measurements. We find that the approximately 10% variations are dominated by throughput changes driven by variable fiber coupling, which depends on wavefront stability, atmospheric dispersion, and residual fiber offsets. These variations appear as smooth continuum changes across wavelength and can be effectively mitigated using second-order polynomial corrections. After removing these instrumental effects, the remaining approximately 1% variations are almost purely of telluric origin, which we can reliably correct down to the photon-noise limit (0.1% precision) using a contrast spectrum approach with linear airmass interpolation. The GRAVITY+ inferometric instrument is highly stable: low-order continuum and telluric variations can be corrected with high precision, making it uniquely capable of high-fidelity characterization of directly imaged exoplanets.

arXiv Page | PDF

Published: 2026-03-16 16:17:26

Authors: Andrew Ritchhart, Sarah I. Allec, Pravalika Butreddy, Krista Kulesa, Qingpu Wang, Dan Thien Nguyen, Maxim Ziatdinov, Elias Nakouzi

Categories: cond-mat.mtrl-sci, cs.AI

Abstract:
We present a multi-agentic workflow for critical materials recovery that deploys a series of AI agents and automated instruments to recover critical materials from produced water and magnet leachates. This approach achieves selective precipitation from real-world feedstocks using simple chemicals, accelerating the development of efficient, adaptable, and scalable separations to a timeline of days, rather than months and years.

arXiv Page | PDF

Published: 2026-03-16 16:17:03

Authors: Alexander Sivitilli, Lucia Marchetti, Angus Comrie, P. Cilliers Pretorius, Thijs, van der Hulst, Fabio Vitello, D. J. Pisano, Ugo Becciani, A. Russell Taylor, Paolo Serra, Mayhew Steyn, Michaela van Zyl

Categories: astro-ph.IM, astro-ph.GA, cs.HC

Abstract:
As modern astronomy confronts unprecedented data volumes, automated pipelines and machine-learning techniques have become essential for processing and analysis. As these workflows grow more complex, astronomers also require input and inspection tools that can keep pace. To address challenges in navigating multidimensional datasets for quality control and scientific interpretation, we present the immersive Data Visualisation Interactive Explorer (iDaVIE), a virtual reality (VR) software suite developed in collaboration with the astronomy community. iDaVIE enables users to import and render large 3D data cubes within a VR environment, offering real-time tools for selection, cropping, catalogue overlays, and exporting results back into existing pipelines. Built on the Unity engine and SteamVR, the system uses custom plug-ins for efficient data parsing, downsampling, and statistical calculations. The software has already been integrated into workflows such as verifying HI data cubes from MeerKAT, ASKAP, and APERTIF, refining detection masks, and identifying new sources. Its intuitive interface aims to reduce the cognitive load associated with higher-dimensional data, allowing researchers to focus more directly on scientific goals. As an open-source, scalable, and adaptable platform, iDaVIE supports continued development and integration with other tools. Version 1.0 marks a significant milestone, with planned enhancements including subcube loading, advanced rendering modes, video-generation scripts, and collaborative capabilities. By pairing immersive visualisation with robust interaction tools, iDaVIE seeks to transform how researchers engage with complex datasets and enhance productivity in the era of big data.

arXiv Page | PDF

Published: 2026-03-16 14:42:11

Authors: Alessandro Zambon, Francesca Caruso, Riccardo Zecchina, Guido Tiana

Categories: cond-mat.dis-nn, cs.LG

Abstract:
Sampling the parameter space of artificial neural networks according to a Boltzmann distribution provides insight into the geometry of low-loss solutions and offers an alternative to conventional loss minimization for training. However, exact sampling methods such as hybrid Monte Carlo (hMC), while formally correct, become computationally prohibitive for realistic datasets because they require repeated evaluation of full-batch gradients. We introduce a pseudo-Langevin (pL) dynamics that enables efficient Boltzmann sampling of feed-forward neural networks trained with large datasets by using minibatches in a controlled manner. The method exploits the statistical properties of minibatch gradient noise and adjusts fictitious masses and friction coefficients to ensure that the induced stochastic process samples efficiently the desired equilibrium distribution. We validate numerically the approach by comparing its equilibrium statistics with those obtained from exact hMC sampling. Performance benchmarks demonstrate that, while hMC rapidly becomes inefficient as network size increases, the pL scheme maintains high computational diffusion and scales favorably to networks with over one million parameters. Finally, we show that sampling at intermediate temperatures yields optimal generalization performance, comparable to SGD, without requiring a validation set or early stopping procedure. These results establish controlled minibatch Langevin dynamics as a practical and scalable tool for exploring and exploiting the solution space of large neural networks.

arXiv Page | PDF

Published: 2026-03-16 14:39:11

Authors: Jingpu Cheng, Qianxiao Li, Ting Lin, Zuowei Shen

Categories: cs.LG, math.DS

Abstract:
We investigate the dependence of the approximation capacity of deep residual networks on its depth in a continuous dynamical systems setting. This can be formulated as the general problem of quantifying the minimal time-horizon required to approximate a diffeomorphism by flows driven by a given family $\mathcal F$ of vector fields. We show that this minimal time can be identified as a geodesic distance on a sub-Finsler manifold of diffeomorphisms, where the local geometry is characterised by a variational principle involving $\mathcal F$. This connects the learning efficiency of target relationships to their compatibility with the learning architectural choice. Further, the results suggest that the key approximation mechanism in deep learning, namely the approximation of functions by composition or dynamics, differs in a fundamental way from linear approximation theory, where linear spaces and norm-based rate estimates are replaced by manifolds and geodesic distances.

arXiv Page | PDF

Published: 2026-03-16 14:38:41

Authors: Masahiko G. Yamada

Categories: cond-mat.str-el

Abstract:
In the series of papers Motivic GUT Part I: Grand Unified Theory of Topological Order, Motivic GUT Part II: Grand Unified Theory of Symmetry-Protected Topological Order, and Motivic GUT Part III: Grand Unified Theory of Symmetry-Enriched Topological Order, we propose a unified framework for gapped topological phases based on the Grothendieck-Kitaev-Lurie motivic yoga. In the spirit of Grothendieck's rising sea, we argue that the classification problem can only be properly addressed after identifying the correct higher-categorical ambient space in which its full richness appears. In this first part, we propose a unified definition of gapped topological order in spatial dimension $d$ in terms of unitary fusion $(\infty,d)$-categorical data, considered up to Morita equivalence. For $d=2$, this framework recovers unitary modular tensor categories. For $d>2$, it naturally leads to genuinely higher-categorical structures. This suggests a Copernican turn in the theory of topological phases: many existing classification schemes should be reinterpreted as lower-categorical shadow realizations of intrinsically $\infty$-categorical objects.

arXiv Page | PDF

Published: 2026-03-16 14:37:46

Authors: Huayan Geng, Yangjun Zeng, Yiwei Qiu

Categories: math.OC, eess.SY

Abstract:
Renewable power-to-ammonia (ReP2A), which uses hydrogen produced from renewable electricity as feedstock, is a promising pathway for decarbonizing the energy, transportation, and chemical sectors. However, variability in renewable generation causes fluctuations in hydrogen supply and ammonia production, leading to revenue instability for both ReP2A producers and conventional fossil-based gray ammonia (GA) producers in the market. Existing studies mainly rely on engineering measures, such as production scheduling, to manage this risk, but their effectiveness is constrained by physical system limits. To address this challenge, this paper proposes a financial instrument termed \emph{renewable ammonia futures} and integrates it with production decisions to hedge ammonia output risk. Production and trading models are developed for both ReP2A and GA producers, with conditional value-at-risk (CVaR) used to represent risk preferences under uncertainty. A game-theoretic framework is established in which the two producers interact in coupled ammonia spot and futures markets, and a Nash bargaining mechanism coordinates their production and trading strategies. Case studies based on a real-world system show that introducing renewable ammonia futures increases the CVaR utilities of ReP2A and GA producers by 5.103% and 10.14%, respectively, improving profit stability under renewable uncertainty. Sensitivity analysis further confirms the effectiveness of the mechanism under different levels of renewable variability and capacity configurations.

arXiv Page | PDF

Published: 2026-03-16 14:36:43

Authors: Hui Zhang, Jiayu Liu

Categories: cs.IR

Abstract:
Recommender systems (RS) play a core role in various domains, including business analytics, helping users and companies make appropriate decisions. To optimize service quality, related technologies focus on constructing user profiles by analyzing users' historical behavior information. This paper considers four analytical scenarios to evaluate user profiling capabilities under different information conditions. A generic user attribute analysis framework named RAPI is proposed, which infers users' personal characteristics by exploiting easily accessible recommendation lists. Specifically, a surrogate recommendation model is established to simulate the original model, leveraging content embedding from a pre-trained BERT model to obtain item embeddings. A sample augmentation module generates extended recommendation lists by considering similarity between model outputs and item embeddings. Finally, an adaptive weight classification model assigns dynamic weights to facilitate user characteristic inference. Experiments on four collections show that RAPI achieves inference accuracy of 0.764 and 0.6477, respectively.

arXiv Page | PDF

Published: 2026-03-16 14:36:31

Authors: Saswata Roy, Owen C. Wetherbee, Valla Fatemi

Categories: quant-ph, physics.app-ph

Abstract:
Bosonic codes offer hardware-efficient approaches to logical qubit construction and hosted the first demonstration of beyond-break even logical quantum memory.However, such accomplishments were done for idling information, and realization of fault-tolerant logical operations remains a critical bottleneck for universal quantum computation in scaled systems. Error-transparent (ET) gates offer an avenue to resolve this issue, but experimental demonstrations have been limited to phase gates. Here, we introduce a framework based on dynamic encoding subspaces that enables simple linear drives to accomplish universal gates that are error semi-transparent (EsT) to oscillator photon loss. With an EsT logical gate set of {X, H, T}, we observe a five-fold reduction in infidelity conditioned on photon loss, demonstrate extended active-manipulation lifetimes with quantum error correction, and construct a composite EsT non-Clifford operation using a sequence of eight gates from the set. Our approach is compatible with methods for detectable ancilla errors, offering an approach to error-mitigated universal control of bosonic logical qubits with the standard quantum control toolkit.

arXiv Page | PDF

Published: 2026-03-16 14:36:31

Authors: Tuomas Uusnäkki, Miika Rasola, Vasilii Vadimov, Priyank Singh, Ahmad Darwish, Mikko Möttönen

Categories: quant-ph

Abstract:
Quantum heat engines provide attractive means in quantum thermodynamics for studying the fundamentals of the flow of heat and work. Previous experimental implementations of heat engines operating at the level of a few excitation quanta have utilized external driving, which has made the observation of the produced work challenging. Conversely, autonomous quantum heat engines only require a flow of heat to operate and generate work. However, autonomous quantum heat engines have not yet been experimentally demonstrated in any system despite numerous theoretical investigations. Here, we experimentally realize an autonomous quantum heat engine based on superconducting circuits. We construct the engine circuit implementing an approximate Otto cycle by coupling two superconducting resonators with a superconducting quantum interference device, and coupling this system to spectrally filtered hot and cold reservoirs. By varying the experimental conditions, we observe coherent microwave power generation arising from the internal dynamics of the system driven only by the thermal reservoirs. Our results validate previous theoretical predictions for this circuit and pave the way for detailed studies of quantum effects in heat engines and for using heat-generated coherent microwaves in circuit quantum electrodynamics.

arXiv Page | PDF

Published: 2026-03-16 14:35:52

Authors: Qinke Ni, Huan Liao, Dekun Chen, Yuxiang Wang, Zhizheng Wu

Categories: cs.SD, cs.AI, eess.AS

Abstract:
While recent text-to-speech (TTS) systems increasingly integrate nonverbal vocalizations (NVs), their evaluations lack standardized metrics and reliable ground-truth references. To bridge this gap, we propose NV-Bench, the first benchmark grounded in a functional taxonomy that treats NVs as communicative acts rather than acoustic artifacts. NV-Bench comprises 1,651 multi-lingual, in-the-wild utterances with paired human reference audio, balanced across 14 NV categories. We introduce a dual-dimensional evaluation protocol: (1) Instruction Alignment, utilizing the proposed paralinguistic character error rate (PCER) to assess controllability, (2) Acoustic Fidelity, measuring the distributional gap to real recordings to assess acoustic realism. We evaluate diverse TTS models and develop two baselines. Experimental results demonstrate a strong correlation between our objective metrics and human perception, establishing NV-Bench as a standardized evaluation framework.

arXiv Page | PDF

Published: 2026-03-16 14:33:42

Authors: Edward A. Tocco, Benjamin D. Dudson, Ian G. Abel, Ben Zhu

Categories: physics.plasm-ph

Abstract:
In this paper, we investigate the effects of rapid rotation (Mach number M ~ 1) on plasma fluid stability, focusing specifically on Kelvin-Helmholtz (KH) and interchange instabilities - including both magnetic-curvature-driven (CDI) and rotation-driven (RDI) interchanges. Building on previous studies of shear flow stabilization, we utilize a drift- reduced fluid approach rather than standard magnetohydrodynamics to capture finite Larmor-radius effects. To achieve this, the drift-reduced equations were modified to include the centrifugal force and implemented in hermes-3 (Dudson et al. 2024), an extension to the BOUT++ (Dudson et al. 2009) framework. Because plasma rotation both drives the RDI and provides stabilizing shear flow, we find that the global plasma stability is sensitive to background profile characteristics. We identify three distinct regimes of RDI behavior and establish a simple criterion based on the density and velocity profiles to predict RDI susceptibility. This approach is similar to recent local gyrokinetic studies of shear flow that compared instability growth rates to shearing rates (Ivanov et al. 2025). Finally, by examining cases where the plasma is both interchange- and KH-unstable, we find that global KH modes make the plasma less resistant to RDI.

arXiv Page | PDF

Published: 2026-03-16 14:28:51

Authors: Ren Jian Lim, Rushi Dai

Categories: cs.AI, cs.HC, cs.MA

Abstract:
In architectural interior design, miscommunication frequently arises as clients lack design knowledge, while designers struggle to explain complex spatial relationships, leading to delayed timelines and financial losses. Recent advancements in generative layout tools narrow the gap by automating 3D visualizations. However, prevailing methodologies exhibit limitations: rule-based systems implement hard-coded spatial constraints that restrict participatory engagement, while data-driven models rely on extensive training datasets. Recent large language models (LLMs) bridge this gap by enabling intuitive reasoning about spatial relationships through natural language. This research presents an LLM-based, multimodal, multi-agent framework that dynamically converts natural language descriptions and imagery into 3D designs. Specialized agents (Reference, Spatial, Interactive, Grader), operating via prompt guidelines, collaboratively address core challenges: the agent system enables real-time user interaction for iterative spatial refinement, while Retrieval-Augmented Generation (RAG) reduces data dependency without requiring task-specific model training. This framework accurately interprets spatial intent and generates optimized 3D indoor design, improving productivity, and encouraging nondesigner participation. Evaluations across diverse floor plans and user questionnaires demonstrate effectiveness. An independent LLM evaluator consistently rated participatory layouts higher in user intent alignment, aesthetic coherence, functionality, and circulation. Questionnaire results indicated 77% satisfaction and a clear preference over traditional design software. These findings suggest the framework enhances user-centric communication and fosters more inclusive, effective, and resilient design processes. Project page: https://rsigktyper.github.io/AICodesign/

arXiv Page | PDF

Published: 2026-03-16 14:25:40

Authors: James Cheshire, Yann Issartel

Categories: stat.ML, cs.LG

Abstract:
Active seriation aims at recovering an unknown ordering of $n$ items by adaptively querying pairwise similarities. The observations are noisy measurements of entries of an underlying $n$ x $n$ permuted Robinson matrix, whose permutation encodes the latent ordering. The framework allows the algorithm to start with partial information on the latent ordering, including seriation from scratch as a special case. We propose an active seriation algorithm that provably recovers the latent ordering with high probability. Under a uniform separation condition on the similarity matrix, optimal performance guarantees are established, both in terms of the probability of error and the number of observations required for successful recovery.

arXiv Page | PDF

Published: 2026-03-16 14:20:56

Authors: Bochen Liu

Categories: math.CA, math.AP, math.CO

Abstract:
Suppose $E, F$ are Borel sets in the plane, $\dim_{\mathcal{H}} E>1$, $\dim_{\mathcal{H}} E+\dim_{\mathcal{H}} F>2$, and $F$ has equal Hausdorff and packing dimension. We prove that there exists $y\in F$ such that the pinned distance set $$Δ_y(E):=\{|x-y|:x\in E\}$$ has positive Lebesgue measure. In particular, it settles the regular case of the distance set problem in the plane. The main ingredients of the proof consist of a multi-scale Good-Bad decomposition and a multi-scale Mizohata-Takeuchi-type estimate with arbitrary small power-loss.

arXiv Page | PDF

Published: 2026-03-16 14:19:57

Authors: Jagadis Prasad Nayak, Smrutirekha Sahoo, Shreya Barman, Gopi Nath Daptary

Categories: cond-mat.mes-hall, cond-mat.mtrl-sci

Abstract:
We report a detailed investigation of low-frequency resistance fluctuations (1/f noise) in chemical vapor deposition (CVD) grown graphene. Systematic measurements reveal that the magnitude of 1/f noise in CVD-grown graphene is significantly higher by several orders of magnitude than that typically observed in exfoliated single-crystal graphene. This enhancement is attributed to structural imperfections such as grain boundaries and defect states within the polycrystalline film. Detailed analysis of the temperature dependence of the noise demonstrates that the resistance fluctuations arise from thermally activated dynamics of localized defects. These results provide key insights into the microscopic mechanism of noise in scalable graphene films and highlight the role of defect engineering in optimizing graphene for large-scale electronic applications. Our findings establish low-frequency noise as a sensitive probe of microscopic disorder in CVD graphene, providing a practical pathway for assessing material quality in scalable electronic technologies.

arXiv Page | PDF

Published: 2026-03-16 14:01:47

Authors: Heinz Siedentop

Categories: math-ph

Abstract:
We show that Csanyi's and Arias' energy functional of the reduced one-particle density matrix is bounded from below by the Müller functional and bounded from above by the Hartree-Fock functional. We use this fact to derive an asymptotic expansion of the ground state energy of this functional which agrees with the quantum energy to third order.

arXiv Page | PDF

Published: 2026-03-16 13:57:38

Authors: Giuseppe Samo, Paola Merlo

Categories: cs.CL, cs.DB

Abstract:
Large language models (LLMs) have shown remarkable performance across various sentence-based linguistic phenomena, yet their ability to capture cross-sentence paradigmatic patterns, such as verb alternations, remains underexplored. In this work, we present curated paradigm-based datasets for four languages, designed to probe systematic cross-sentence knowledge of verb alternations (change-of-state and object-drop constructions in English, German and Italian, and Hebrew binyanim). The datasets comprise thousands of the Blackbird Language Matrices (BLMs) problems. The BLM task -- an RPM/ARC-like task devised specifically for language -- is a controlled linguistic puzzle where models must select the sentence that completes a pattern according to syntactic and semantic rules. We introduce three types of templates varying in complexity and apply linguistically-informed data augmentation strategies across synthetic and natural data. We provide simple baseline performance results across English, Italian, German, and Hebrew, that demonstrate the diagnostic usefulness of the datasets.

arXiv Page | PDF

Published: 2026-03-16 13:47:08

Authors: Dickens Kwesiga, Angshuman Guin, Khaled Abdelghany, Michael Hunter

Categories: cs.LG

Abstract:
Reinforcement learning (RL) has attracted increasing interest for adaptive traffic signal control due to its model-free ability to learn control policies directly from interaction with the traffic environment. However, several challenges remain before RL-based signal control can be considered ready for field deployment. Many existing studies rely on simplified signal timing structures, robustness of trained models under varying traffic demand conditions remains insufficiently evaluated, and runtime efficiency continues to pose challenges when training RL algorithms in traffic microscopic simulation environments. This study formulates an RL-based signal control algorithm capable of representing a full eight-phase ring-barrier configuration consistent with field signal controllers. The algorithm is trained and evaluated under varying traffic demand conditions and benchmarked against state-of-the-practice actuated signal control (ASC). To assess robustness, experiments are conducted across multiple traffic volumes and origin-destination (O-D) demand patterns with varying levels of structural similarity. To improve training efficiency, a distributed asynchronous training architecture is implemented that enables parallel simulation across multiple computing nodes. Results from a case study intersection show that the proposed RL-based signal control significantly outperforms optimized ASC, reducing average delay by 11-32% across movements. A model trained on a single O-D pattern generalizes well to similar unseen demand patterns but degrades under substantially different demand conditions. In contrast, a model trained on diverse O-D patterns demonstrates strong robustness, consistently outperforming ASC even under highly dissimilar unseen demand scenarios.

arXiv Page | PDF

Published: 2026-03-16 13:39:43

Authors: N. Huélamo, I. de Gregorio-Monsalvo, Aina Palau, C. Carrasco-González, A. Ribas, H. Bouy, R. Pandey, D. Barrado, N. Otten, V. D. Ivanov, M. F. Sterzik, M. Dunham, L. A. Zapata, E. Pantin, E. Macías

Categories: astro-ph.SR, astro-ph.GA

Abstract:
Very low-luminosity objects in nearby star-forming regions have been identified as promising proto-brown dwarf candidates. The study of their multiplicity can shed light on the dominant formation mechanism of these substellar objects. We aim at studying the multiplicity of the very low luminosity object IRAM 04191+1522. To do so, we have obtained 0.89mm ALMA observations with a very extended configuration, achieving an angular resolution of ~0.04 arcsec (6 au at 140 pc). We have complemented our data with new VLA observations, and ALMA archival data at 1.3mm. As a result, we resolve IRAM04191+1522 into a close binary candidate for the first time. The binary is detected in the ALMA continuum data with a projected separation of ~80 mas, or 11 au at a distance of 140 pc. The two sources are oriented in the East-West direction, with the eastern component being brighter and more extended than the western one, which is marginally resolved. The analysis of C18O(2-1) archival data reveals gaseous material in rotation around the binary, presumably from a circumbinary disk with ~27 au of radius centered on the faintest ALMA component. A fit of the position-velocity diagram allows us to estimate a total dynamical mass for the system of 50+-40 MJup. Therefore, we classify IRAM04191 as a tight proto-brown dwarf binary candidate. The VLA data reveals the detection of a single object closer to the western ALMA source, and with a spectral index consistent with a radio jet.

arXiv Page | PDF

Published: 2026-03-16 13:36:38

Authors: Kim Ouan, Noémie Moreau, Katarzyna Bozek

Categories: cs.CV

Abstract:
The tortuosity of corneal nerve fibers are used as indication for different diseases. Current state-of-the-art methods for grading the tortuosity heavily rely on expensive segmentation maps of these nerve fibers. In this paper, we demonstrate that self-supervised pretrained features from ImageNet are transferable to the domain of in vivo confocal microscopy. We show that DINO should not be disregarded as a deep learning model for medical imaging, although it was superseded by two later versions. After careful fine-tuning, DINO improves upon the state-of-the-art in terms of accuracy (84,25%) and sensitivity (77,97%). Our fine-tuned model focuses on the key morphological elements in grading without the use of segmentation maps.

arXiv Page | PDF

Published: 2026-03-16 13:24:34

Authors: Yves Colin de Verdière, Charlotte Dietze, Emmanuel Trélat

Categories: math.SP

Abstract:
We consider a compact smooth manifold $X$ of dimension $n+1$ with boundary $M=\partial X$. In a collar neighborhood of $M$, we assume that the metric has the form $g=u^{-α}\bar g$, where $u$ is a boundary defining function, $α\in C^1(M;[0,2))$ and $\bar g$ is a $C^1$ Riemannian metric up to $M$. Since $α<2$, the boundary lies at finite $g$-distance and $(X,g)$ is a singular metric space. We study the Weyl asymptotics of the Friedrichs Laplacian $\triangle\_g$ when the degeneracy exponent $α$ varies along $M$. If the maximum $α\_{\mathrm{max}}$ of $α$ on $M$ is strictly larger than the critical value $α\_c=\frac{2}{n+1}$, then we prove that the points where $α$ is close to $α\_{\mathrm{max}}$ govern the leading term in the Weyl asymptotics. If $α\_{\mathrm{max}}\leqα\_c$, then the leading term is governed by the truncated volume $\vol\_g(\{\dist(\cdot,M)>λ^{-1/2}\})$. When the maximum set of $α$ is Morse-Bott, we compute the associated constants and the logarithmic corrections. To the best of our knowledge, this is the first Weyl law in this setting with a boundary-dependent degeneracy exponent. The results highlight a sharp transition at $α\_c$ between a boundary-dominated non-classical regime and a truncated-volume regime.

arXiv Page | PDF

Published: 2026-03-16 13:23:34

Authors: Miguel Díaz-Rodríguez, Vicente Mata, Angel Valera, Alvaro Page

Categories: cs.RO

Abstract:
The identification of dynamic parameters in mechanical systems is important for improving model-based control as well as for performing realistic dynamic simulations. Generally, when identification techniques are applied only a subset of so-called base parameters can be identified. More even, some of these parameters cannot be identified properly given that they have a small contribution to the robot dynamics and hence in the presence of noise in measurements and discrepancy in modeling, their quality of being identifiable decreases. For this reason, a strategy for dynamic parameter identification of fully parallel robots in terms of a subset called relevant parameters is put forward. The objective of the proposed methodology is to start from a full dynamic model, then simplification concerning the geometry of each link and, the symmetry due to legs of fully parallel robots, are carried out. After that, the identification is done by Weighted Least Squares. Then, with statistical considerations the model is reduced until the physical feasibility conditions are met. The application of the propose strategy has been experimentally tested on two difierent configurations of actual 3-DOF parallel robots. The response of the inverse and forward dynamics of the identified models agrees with experiments. In order to evaluate the forward dynamics response, an approach for obtaining the forward dynamics in terms of the relevant parameters is also proposed.

arXiv Page | PDF

Published: 2026-03-16 13:21:46

Authors: Laurent Lellouch, Alessandro Lupo, Mattias Sjö, Pierre Vanhove

Categories: hep-ph, hep-lat, hep-th

Abstract:
This work presents a detailed account of the Feynman integrals required for the three-loop hadronic vacuum polarization calculation performed in arXiv:2511.12885. We explain how to compute each of the three-loop integrals, and outline the mathematical framework underlying their evaluation. This culminates in a practical numerical implementation that enables fast and accurate evaluation of these integrals for arbitrary complex values of the photon virtuality.

arXiv Page | PDF

Published: 2026-03-16 13:19:54

Authors: Shuo Shao, Chao Qi, Jincheng Dai

Categories: cs.IT

Abstract:
In this paper we study the nonasymptotic bounds of a special Joint Source-Channel Coding system with hierarchical source, where an observable source and an unobservable indirect source are required to be reconstructed. Namely, we focus on the achievable and converse bounds of the excess distortion probability in the finite blocklength regime. The main challenge arises from the hierarchical source structure, which requires simultaneous reconstruction of both sources. This setup demands a coding scheme which satisfy the demand of encoding both source for the achievability bound, and a method to characterize the joint excess-distortion probability of two correlated events for the converse bound.

arXiv Page | PDF

Published: 2026-03-16 13:15:28

Authors: Florian Cougoulic, Piotr Korcyl, Tomasz Stebel

Categories: hep-ph, nucl-th

Abstract:
We systematically derive expressions for ten small-$x$ Transverse Momentum Dependent (TMD) distributions in the Gaussian approximation, three in the quark-gluon sector and seven in the gluon-gluon sector; for the general $SU(N_c)$ gauge group. The derived formulae depend on the logarithm of the dipole amplitude. Using the McLerran-Venugopalan model for the initial condition, we simulate the dipole amplitude as well as estimate all ten TMD distributions for $N_c \in \{2,3,4,5\}$. We compare these explicit numerical results with the derived expressions and find very good agreement for all studied values of $N_c$. Consequently, we study the scaling with $N_c$ of the TMD distributions. Thanks to that, we are able to derive the large-$N_c$ limit of the Gaussian approximation results and show that they agree with expressions obtained in the mean-field approximation. By comparing with our numerical results, we are able to demonstrate the size, origin, and significance of subleading-$N_c$ corrections. This work sets the stage for a systematic study of subleading corrections induced by the JIMWLK evolution in the rapidity evolution of the TMD distributions. Interestingly, we find an exact sum rule that relates all seven gluon-gluon TMD operators at $N_c = 3$ and arbitrary $x$.

arXiv Page | PDF

Published: 2026-03-16 13:14:54

Authors: Mohammad Asif Habibi, Xavier Costa-Pérez, Hans D. Schotten

Categories: cs.NI

Abstract:
In this paper, we propose an rApp, named SliceMapper, to optimize the mapping process of the open centralized unit (O-CU) and open distributed unit (O-DU) of an open radio access network (O-RAN) slice subnet onto the underlying open cloud (O-Cloud) sites in sixth-generation (6G) O-RAN. To accomplish this, we first design a system model for SliceMapper and introduce its mathematical framework. Next, we formulate the mapping process addressed by SliceMapper as a sequential decision-making optimization problem. To solve this problem, we implement both on-policy and off-policy variants of the Q-learning algorithm, employing tabular representation as well as function approximation methods for each variant. To evaluate the effectiveness of these approaches, we conduct a series of simulations under various scenarios. We proceed further by performing a comparative analysis of all four variants. The results demonstrate that the on-policy function approximation method outperforms the alternative approaches in terms of stability and lower standard deviation across all random seeds. However, the on-policy and off-policy tabular representation methods achieve higher average rewards, with values of 5.42 and 5.12, respectively. Finally, we conclude the paper and introduce several directions for future research.

arXiv Page | PDF

Published: 2026-03-16 13:10:46

Authors: Bocheng Zhu, Volker Springel, Feng Yuan

Categories: astro-ph.GA

Abstract:
Active galactic nuclei (AGN) feedback plays a significant role in many aspects of galaxy formation and evolution and has become a key ingredient in cosmological simulations. However, the subgrid models of AGN feedback in cosmological simulations such as IllustrisTNG (hereafter TNG) often overlook recent progress in the small-scale modelling of black hole (BH) accretion and AGN physics. In this study, we improve on this by incorporating central aspects of the MACER model, a framework that treats AGN physics in greater detail, into the TNG feedback implementation. Specifically, we adopt MACER-prescriptions for feedback output for high and low accretion rates in a new model while the estimation of the accretion rate remains unchanged. We test this updated scenario both for idealized elliptical galaxies and for a cosmological box. Compared to the original TNG model, the MACER-based simulation leads to a higher star formation rate (SFR) and BH accretion rate in ellipticals, yielding a gas density profile in better agreement with observations. In the cosmological simulations, the time evolution of the SFR density, galaxy stellar mass function at $z=0$, and $M_{\star}-M_{\rm BH}$ relation at $M_{\star}>10^{10.5}\,{\rm M_{\odot}}$ are similar in both models. The MACER model better reproduces low-mass BHs in low-mass galaxies, and yields milder quenching in massive galaxies, although this is accompanied by the absence of a pronounced colour bimodality. Still, the similarity of the outcomes underlines the self-regulated nature of BH feedback: for different feedback energetics, the accretion rate tends to adjust such that a similar total AGN feedback energy is released.

arXiv Page | PDF

Published: 2026-03-16 13:09:57

Authors: Mohammad Soleymani, Bruno Clerckx, Robert Schober, Lajos Hanzo

Categories: eess.SP

Abstract:
We simultaneously minimize the latency and improve energy efficiency (EE) of the multi-user multiple-input multiple-output (MU-MIMO) rate splitting multiple access (RSMA) downlink, aided by a reconfigurable intelligent surface (RIS). Our results show that RSMA improves the EE and may reduce the delay to 13\% of that of spatial division multiple access (SDMA). Moreover, RIS and RSMA support each other synergistically, while an RIS operating without RSMA provides limited benefits in terms of latency and cannot effectively mitigate interference. {Furthermore, increasing the RIS size amplifies the gains of RSMA more significantly than those of SDMA, without altering the fundamental EE-latency trade-offs.} Results also show that latency increases with more stringent reliability requirements, and RSMA yields more significant gains under such conditions, making it eminently suitable for energy-efficient ultra-reliable low-latency communication (URLLC) scenarios.

arXiv Page | PDF

Published: 2026-03-16 13:07:10

Authors: Benjamin Luke, Sudip Shiwakoti, Shayan Nadeem, Andrei Alexandru, Walter Wilcox, Frank X. Lee

Categories: hep-lat

Abstract:
Understanding a hadron's electric and magnetic polarizabilities allows one to access internal structural information. Traditionally, the external field two-point function method has been used to calculate polarizabilities. However, recent work has demonstrated the effectiveness of using four-point functions for computing polarizabilities of charged and neutral hadrons. Our previous study on the electric polarizability of the charged pion used a quenched Wilson action on a lattice with pion mass from 1100 MeV to 370 MeV. In this work, we employ a number of improvements, including a dynamical action (nHYP), smaller pion masses (220 MeV and 315 MeV), and a variable lattice size in order to extrapolate to infinite volume. Preliminary results are presented.

arXiv Page | PDF

Published: 2026-03-16 13:05:27

Authors: Shlomo Segal

Categories: cond-mat.stat-mech, physics.bio-ph

Abstract:
The emergence of life from inanimate matter presents a thermodynamic challenge: the Second Law of Thermodynamics dictates a global trend towards disorder, yet life constitutes localized pockets of profound organization. This paper presents a formal physical framework for abiogenesis grounded in the statistical physics of non-equilibrium systems. We transition from the established connection between dissipation and process probability (e.g., Crooks Fluctuation Theorem) to a large-deviation framework for the likelihood of system histories. This formalism reveals a probabilistic bias towards histories with greater integrated dissipation. We then demonstrate how this bias leads to the selection of heredity. The core of our argument is a rigorous mathematical proposition showing that while simple autocatalysis leads to an exponential increase in dissipation, template-directed replication, via its capacity for mutation and adaptation (a process from which we derive an effective adaptation rate, alpha), unlocks a super-exponential growth pathway. This translates to a doubly-exponential amplification in the relative probability of its emergence over time, constituting an asymptotically dominant physical bias for its selection. This framework delineates a hierarchical transition from simple dissipative structures to information-bearing replicators, whose stability is contingent upon exceeding critical thresholds of fidelity, kinetic efficiency, and resource supply. We conclude by proposing a refined, quantitative, and falsifiable experiment, defining a precise mathematical signature for identifying the onset of evolutionary processes in synthetic chemical systems.

arXiv Page | PDF