Tootfinder

Hierarchical Reinforcement Learning and Value Optimization for Challenging Quadruped Locomotion
Jeremiah Coholich, Muhammad Ali Murtaza, Seth Hutchinson, Zsolt Kira
https://arxiv.org/abs/2506.20036

Hierarchical Reinforcement Learning and Value Optimization for Challenging Quadruped Locomotion
We propose a novel hierarchical reinforcement learning framework for quadruped locomotion over challenging terrain. Our approach incorporates a two-layer hierarchy in which a high-level policy (HLP) selects optimal goals for a low-level policy (LLP). The LLP is trained using an on-policy actor-critic RL algorithm and is given footstep placements as goals. We propose an HLP that does not require any additional training or environment samples and instead operates via an online optimization proces…

A Multimodal-Multitask Framework with Cross-modal Relation and Hierarchical Interactive Attention for Semantic Comprehension
Mohammad Zia Ur Rehman, Devraj Raghuvanshi, Umang Jain, Shubhi Bansal, Nagendra Kumar
https://arxiv.org/abs/2508.16300

A Multimodal-Multitask Framework with Cross-modal Relation and Hierarchical Interactive Attention for Semantic Comprehension
A major challenge in multimodal learning is the presence of noise within individual modalities. This noise inherently affects the resulting multimodal representations, especially when these representations are obtained through explicit interactions between different modalities. Moreover, the multimodal fusion techniques while aiming to achieve a strong joint representation, can neglect valuable discriminative information within the individual modalities. To this end, we propose a Multimodal-Mul…

LLM-as-classifier: Semi-Supervised, Iterative Framework for Hierarchical Text Classification using Large Language Models
Doohee You, Andy Parisi, Zach Vander Velden, Lara Dantas Inojosa
https://arxiv.org/abs/2508.16478

LLM-as-classifier: Semi-Supervised, Iterative Framework for Hierarchical Text Classification using Large Language Models
The advent of Large Language Models (LLMs) has provided unprecedented capabilities for analyzing unstructured text data. However, deploying these models as reliable, robust, and scalable classifiers in production environments presents significant methodological challenges. Standard fine-tuning approaches can be resource-intensive and often struggle with the dynamic nature of real-world data distributions, which is common in the industry. In this paper, we propose a comprehensive, semi-supervise…

Hierarchical Dimensionless Learning (Hi-{\pi}): A physics-data hybrid-driven approach for discovering dimensionless parameter combinations
Mingkun Xia, Haitao Lin, Weiwei Zhang
https://arxiv.org/abs/2507.18332

Hierarchical Dimensionless Learning (Hi-π): A physics-data hybrid-driven approach for discovering dimensionless parameter combinations
Dimensional analysis provides a universal framework for reducing physical complexity and reveal inherent laws. However, its application to high-dimensional systems still generates redundant dimensionless parameters, making it challenging to establish physically meaningful descriptions. Here, we introduce Hierarchical Dimensionless Learning (Hi-π), a physics-data hybrid-driven method that combines dimensional analysis and symbolic regression to automatically discover key dimensionless parameter…

Hierarchical Diffusion Framework for Pseudo-Healthy Brain MRI Inpainting with Enhanced 3D Consistency
Dou Hoon Kwark, Shirui Luo, Xiyue Zhu, Yudu Li, Zhi-Pei Liang, Volodymyr Kindratenko
https://arxiv.org/abs/2507.17911

Hierarchical Diffusion Framework for Pseudo-Healthy Brain MRI Inpainting with Enhanced 3D Consistency
Pseudo-healthy image inpainting is an essential preprocessing step for analyzing pathological brain MRI scans. Most current inpainting methods favor slice-wise 2D models for their high in-plane fidelity, but their independence across slices produces discontinuities in the volume. Fully 3D models alleviate this issue, but their high model capacity demands extensive training data for reliable, high-fidelity synthesis -- often impractical in medical settings. We address these limitations with a hi…

DualEquiNet: A Dual-Space Hierarchical Equivariant Network for Large Biomolecules
Junjie Xu, Jiahao Zhang, Mangal Prakash, Xiang Zhang, Suhang Wang
https://arxiv.org/abs/2506.19862

DualEquiNet: A Dual-Space Hierarchical Equivariant Network for Large Biomolecules
Geometric graph neural networks (GNNs) that respect E(3) symmetries have achieved strong performance on small molecule modeling, but they face scalability and expressiveness challenges when applied to large biomolecules such as RNA and proteins. These systems require models that can simultaneously capture fine-grained atomic interactions, long-range dependencies across spatially distant components, and biologically relevant hierarchical structure, such as atoms forming residues, which in turn f…

Hierarchical Reinforcement Learning Framework for Adaptive Walking Control Using General Value Functions of Lower-Limb Sensor Signals
Sonny T. Jones, Grange M. Simpson, Patrick M. Pilarski, Ashley N. Dalrymple
https://arxiv.org/abs/2507.16983

Hierarchical Reinforcement Learning Framework for Adaptive Walking Control Using General Value Functions of Lower-Limb Sensor Signals
Rehabilitation technology is a natural setting to study the shared learning and decision-making of human and machine agents. In this work, we explore the use of Hierarchical Reinforcement Learning (HRL) to develop adaptive control strategies for lower-limb exoskeletons, aiming to enhance mobility and autonomy for individuals with motor impairments. Inspired by prominent models of biological sensorimotor processing, our investigated HRL approach breaks down the complex task of exoskeleton contro…

Identifying Treatment Effect Heterogeneity with Bayesian Hierarchical Adjustable Random Partition in Adaptive Enrichment Trials
Xianglin Zhao, Shirin Golchi, Jean-Philippe Gouin, Kaberi Dasgupta
https://arxiv.org/abs/2508.16523

Identifying Treatment Effect Heterogeneity with Bayesian Hierarchical Adjustable Random Partition in Adaptive Enrichment Trials
Treatment effect heterogeneity refers to the systematic variation in treatment effects across subgroups. There is an increasing need for clinical trials that aim to investigate treatment effect heterogeneity and estimate subgroup-specific responses. While several statistical methods have been proposed to address this problem, existing partitioning-based methods often depend on auxiliary analysis, overlook model uncertainty, or impose inflexible borrowing strength. We propose the Bayesian Hierar…

Hierarchical Decision-Making for Autonomous Navigation: Integrating Deep Reinforcement Learning and Fuzzy Logic in Four-Wheel Independent Steering and Driving Systems
Yizhi Wang, Degang Xu, Yongfang Xie, Shuzhong Tan, Xianan Zhou, Peng Chen
https://arxiv.org/abs/2508.16574

Hierarchical Decision-Making for Autonomous Navigation: Integrating Deep Reinforcement Learning and Fuzzy Logic in Four-Wheel Independent Steering and Driving Systems
This paper presents a hierarchical decision-making framework for autonomous navigation in four-wheel independent steering and driving (4WISD) systems. The proposed approach integrates deep reinforcement learning (DRL) for high-level navigation with fuzzy logic for low-level control to ensure both task performance and physical feasibility. The DRL agent generates global motion commands, while the fuzzy logic controller enforces kinematic constraints to prevent mechanical strain and wheel slippag…

HARLF: Hierarchical Reinforcement Learning and Lightweight LLM-Driven Sentiment Integration for Financial Portfolio Optimization
Benjamin Coriat, Eric Benhamou
https://arxiv.org/abs/2507.18560

HARLF: Hierarchical Reinforcement Learning and Lightweight LLM-Driven Sentiment Integration for Financial Portfolio Optimization
This paper presents a novel hierarchical framework for portfolio optimization, integrating lightweight Large Language Models (LLMs) with Deep Reinforcement Learning (DRL) to combine sentiment signals from financial news with traditional market indicators. Our three-tier architecture employs base RL agents to process hybrid data, meta-agents to aggregate their decisions, and a super-agent to merge decisions based on market data and sentiment analysis. Evaluated on data from 2018 to 2024, after t…

Cross-Modal Prototype Augmentation and Dual-Grained Prompt Learning for Social Media Popularity Prediction
Ao Zhou, Mingsheng Tu, Luping Wang, Tenghao Sun, Zifeng Cheng, Yafeng Yin, Zhiwei Jiang, Qing Gu
https://arxiv.org/abs/2508.16147

Cross-Modal Prototype Augmentation and Dual-Grained Prompt Learning for Social Media Popularity Prediction
Social Media Popularity Prediction is a complex multimodal task that requires effective integration of images, text, and structured information. However, current approaches suffer from inadequate visual-textual alignment and fail to capture the inherent cross-content correlations and hierarchical patterns in social media data. To overcome these limitations, we establish a multi-class framework , introducing hierarchical prototypes for structural enhancement and contrastive learning for improved…

Implementation and Evaluation of Fast Raft for Hierarchical Consensus
Anton Melnychuk, Bryan SebaRaj
https://arxiv.org/abs/2506.17793 https://

Implementation and Evaluation of Fast Raft for Hierarchical Consensus
We present the first open-source implementation and evaluation of Fast Raft, a hierarchical consensus protocol designed for dynamic, distributed environments. Fast Raft reduces the number of message rounds needed to commit log entries compared to standard Raft by introducing a fast-track mechanism and reducing leader dependence. Our implementation uses gRPC and Kubernetes-based deployment across AWS availability zones. Experimental results demonstrate a throughput improvement and reduced commit…

Hierarchical Finite-Element Analysis of Multiscale Electromagnetic Problems via Sparse Operator-Adapted Wavelet Decomposition
F. \c{S}{\i}k, F. L. Teixeira, B. Shanker
https://arxiv.org/abs/2507.17989 …

Hierarchical Finite-Element Analysis of Multiscale Electromagnetic Problems via Sparse Operator-Adapted Wavelet Decomposition
In this paper, we present a finite element method (FEM) framework enhanced by an operator-adapted wavelet decomposition algorithm designed for the efficient analysis of multiscale electromagnetic problems. Usual adaptive FEM approaches, while capable of achieving the desired accuracy without requiring a complete re-meshing of the computational domain, inherently couple different resolution levels. This coupling requires recomputation of coarser-level solutions whenever finer details are added t…

Decentralized Consensus Inference-based Hierarchical Reinforcement Learning for Multi-Constrained UAV Pursuit-Evasion Game
Xiang Yuming, Li Sizhao, Li Rongpeng, Zhao Zhifeng, Zhang Honggang
https://arxiv.org/abs/2506.18126

Decentralized Consensus Inference-based Hierarchical Reinforcement Learning for Multi-Constrained UAV Pursuit-Evasion Game
Multiple quadrotor unmanned aerial vehicle (UAV) systems have garnered widespread research interest and fostered tremendous interesting applications, especially in multi-constrained pursuit-evasion games (MC-PEG). The Cooperative Evasion and Formation Coverage (CEFC) task, where the UAV swarm aims to maximize formation coverage across multiple target zones while collaboratively evading predators, belongs to one of the most challenging issues in MC-PEG, especially under communication-limited con…

Hierarchical friction memory leads to subdiffusive configurational dynamics of fast-folding proteins
Anton Klimek, Benjamin A. Dalton, Lucas Tepper, Roland R. Netz
https://arxiv.org/abs/2506.18823

Hierarchical friction memory leads to subdiffusive configurational dynamics of fast-folding proteins
Proteins often exhibit subdiffusive configurational dynamics. The origins of this subdiffusion are still unresolved. We investigate the impact of non-Markovian friction and the free energy landscape on the dynamics of fast-folding proteins in terms of the mean squared displacement (MSD) and the mean first-passage-time (MFPT) of the folding reaction coordinate. We find the friction memory kernel from published molecular dynamics (MD) simulations to be well-described by a hierarchical multi-expon…

Posterior Contraction for Sparse Neural Networks in Besov Spaces with Intrinsic Dimensionality
Kyeongwon Lee, Lizhen Lin, Jaewoo Park, Seonghyun Jeong
https://arxiv.org/abs/2506.19144

Posterior Contraction for Sparse Neural Networks in Besov Spaces with Intrinsic Dimensionality
This work establishes that sparse Bayesian neural networks achieve optimal posterior contraction rates over anisotropic Besov spaces and their hierarchical compositions. These structures reflect the intrinsic dimensionality of the underlying function, thereby mitigating the curse of dimensionality. Our analysis shows that Bayesian neural networks equipped with either sparse or continuous shrinkage priors attain the optimal rates which are dependent on the intrinsic dimension of the true structu…

BGM-HAN: A Hierarchical Attention Network for Accurate and Fair Decision Assessment on Semi-Structured Profiles
Junhua Liu, Roy Ka-Wei Lee, Kwan Hui Lim
https://arxiv.org/abs/2507.17472

BGM-HAN: A Hierarchical Attention Network for Accurate and Fair Decision Assessment on Semi-Structured Profiles
Human decision-making in high-stakes domains often relies on expertise and heuristics, but is vulnerable to hard-to-detect cognitive biases that threaten fairness and long-term outcomes. This work presents a novel approach to enhancing complex decision-making workflows through the integration of hierarchical learning alongside various enhancements. Focusing on university admissions as a representative high-stakes domain, we propose BGM-HAN, an enhanced Byte-Pair Encoded, Gated Multi-head Hierar…

New tools in hierarchical hyperbolicity: A survey
Alessandro Sisto
https://arxiv.org/abs/2507.17546 https://arxiv.org/pdf/2507.17546

New tools in hierarchical hyperbolicity: A survey
The aim of this short survey is to advertise various tools that have been developed to study hierarchically hyperbolic spaces (HHSs) in recent years, with particular emphasis on those that require little to no knowledge of the HHS machinery to be used.

LLM-Integrated Digital Twins for Hierarchical Resource Allocation in 6G Networks
Majumder Haider, Imtiaz Ahmed, Zoheb Hassan, Kamrul Hasan, H. Vincent Poor
https://arxiv.org/abs/2506.18293

LLM-Integrated Digital Twins for Hierarchical Resource Allocation in 6G Networks
Next-generation (NextG) wireless networks are expected to require intelligent, scalable, and context-aware radio resource management (RRM) to support ultra-dense deployments, diverse service requirements, and dynamic network conditions. Digital twins (DTs) offer a powerful tool for network management by creating high-fidelity virtual replicas that model real-time network behavior, while large language models (LLMs) enhance decision-making through their advanced generalization and contextual rea…

Accelerating Correlated Wave Function Calculations with Hierarchical Matrix Compression of the Two-Electron Integrals
Hongji Gao, Xiangmin Jiao, Benjamin G. Levine
https://arxiv.org/abs/2506.16576

Accelerating Correlated Wave Function Calculations with Hierarchical Matrix Compression of the Two-Electron Integrals
Leveraging matrix sparsity has proven a fruitful strategy for accelerating quantum chemical calculations. Here we present the hierarchical SOS-MP2 algorithm, which uses hierarchical matrix ($\mathcal{H}^{2}$) compression of the electron repulsion integral (ERI) tensor to reduce both time and space complexity. This approach is based on the atomic orbital Laplace transform MP2 calculations, leveraging the data sparsity of the ERI tensor and the element-wise sparsity of the energy-weighted density…

AutoMAT: A Hierarchical Framework for Autonomous Alloy Discovery
Penghui Yang, Chendong Zhao, Bijun Tang, Zhonghan Zhang, Xinrun Wang, Yanchen Deng, Yuhao Lu, Cuntai Guan, Zheng Liu, Bo An
https://arxiv.org/abs/2507.16005

AutoMAT: A Hierarchical Framework for Autonomous Alloy Discovery
Alloy discovery is central to advancing modern industry but remains hindered by the vastness of compositional design space and the costly validation. Here, we present AutoMAT, a hierarchical and autonomous framework grounded in and validated by experiments, which integrates large language models, automated CALPHAD-based simulations, and AI-driven search to accelerate alloy design. Spanning the entire pipeline from ideation to validation, AutoMAT achieves high efficiency, accuracy, and interpret…

Constraints on the extreme mass-ratio inspiral population from LISA data
Shashwat Singh, Christian E. A. Chapman-Bird, Christopher P L Berry, John Veitch
https://arxiv.org/abs/2508.16399

Constraints on the extreme mass-ratio inspiral population from LISA data
Gravitational waves from extreme mass-ratio inspirals (EMRIs), the inspirals of stellar-mass compact objects into massive black holes, are predicted to be observed by the Laser Interferometer Space Antenna (LISA). A sufficiently large number of EMRI observations will provide unique insights into the massive black hole population. We have developed a hierarchical Bayesian inference framework capable of constraining the parameters of the EMRI population, accounting for selection biases. We levera…

CIGaRS I: Combined simulation-based inference from SNae Ia and host photometry
Konstantin Karchev, Roberto Trotta, Raul Jimenez
https://arxiv.org/abs/2508.15899 https://

CIGaRS I: Combined simulation-based inference from SNae Ia and host photometry
Using type Ia supernovae (SNae Ia) as cosmological probes requires empirical corrections, which correlate with their host environment. We present a unified Bayesian hierarchical model designed to infer, from purely photometric observations, the intrinsic dependence of SN Ia brightness on progenitor properties (metallicity & age), the delay-time distribution (DTD) that governs their rate as a function of age, and cosmology, as well as the redshifts of all hosts. The model incorporates physics-ba…

Spinning into the Gap: Direct-Horizon Collapse as the Origin of GW231123 from End-to-End GRMHD Simulations
Ore Gottlieb, Brian D. Metzger, Danat Issa, Sean E. Li, Mathieu Renzo, Maximiliano Isi
https://arxiv.org/abs/2508.15887

Spinning into the Gap: Direct-Horizon Collapse as the Origin of GW231123 from End-to-End GRMHD Simulations
GW231123, the most massive binary black hole (BH) merger observed to date, involves component BHs with masses inside the pair-instability mass gap and unusually high spins. This challenges standard formation channels such as classical stellar evolution and hierarchical mergers. However, stellar rotation and magnetic fields, which have not been systematically incorporated in prior models, can strongly influence the BH properties. We present the first self-consistent simulations tracking a massiv…

Collusion-Resilient Hierarchical Secure Aggregation with Heterogeneous Security Constraints
Zhou Li, Xiang Zhang, Jiawen Lv, Jihao Fan, Haiqiang Chen, Giuseppe Caire
https://arxiv.org/abs/2507.14768

Collusion-Resilient Hierarchical Secure Aggregation with Heterogeneous Security Constraints
Motivated by federated learning (FL), secure aggregation (SA) aims to securely compute, as efficiently as possible, the sum of a set of inputs distributed across many users. To understand the impact of network topology, hierarchical secure aggregation (HSA) investigated the communication and secret key generation efficiency in a 3-layer relay network, where clusters of users are connected to the aggregation server through an intermediate layer of relays. Due to the pre-aggregation of the messag…

DuetGen: Music Driven Two-Person Dance Generation via Hierarchical Masked Modeling
Anindita Ghosh, Bing Zhou, Rishabh Dabral, Jian Wang, Vladislav Golyanik, Christian Theobalt, Philipp Slusallek, Chuan Guo
https://arxiv.org/abs/2506.18680

DuetGen: Music Driven Two-Person Dance Generation via Hierarchical Masked Modeling
We present DuetGen, a novel framework for generating interactive two-person dances from music. The key challenge of this task lies in the inherent complexities of two-person dance interactions, where the partners need to synchronize both with each other and with the music. Inspired by the recent advances in motion synthesis, we propose a two-stage solution: encoding two-person motions into discrete tokens and then generating these tokens from music. To effectively capture intricate interactions…

Connected Theorems: A Graph-Based Approach to Evaluating Mathematical Results
Haocheng Ju, Tianyi Xu, Bin Dong
https://arxiv.org/abs/2508.17596 https://arx…

Connected Theorems: A Graph-Based Approach to Evaluating Mathematical Results
The evaluation of mathematical results plays a central role in assessing researchers' contributions and shaping the direction of the field. Currently, such evaluations rely primarily on human judgment, whether through journal peer review or committees at research institutions. To complement these traditional processes, we propose a data-driven approach. We construct a hierarchical graph linking theorems, papers, and fields to capture their citation relationships. We then introduce a PageRank-st…

Fractality of Wireless Mesh Networks: Dimensional Effects on Network Performance
Marat Zaidyn, Sayat Akhtanov, Dana Turlykozhayeva, Symbat Temesheva, Almat Akhmetali, Alisher Skabylov, Nurzhan Ussipov
https://arxiv.org/abs/2506.19366

Fractality of Wireless Mesh Networks: Dimensional Effects on Network Performance
Wireless mesh networks (WMNs) depend on the spatial distribution of nodes, which directly influences connectivity, routing efficiency, and overall network performance. Conventional models typically assume uniform or random node placement, which inadequately represent the complex, hierarchical spatial patterns observed in practical deployments. In this study, we present a novel algorithm that constructs WMN topologies with tunable fractal dimensions, allowing precise control over spatial self-si…

Multimodal Recurrent Ensembles for Predicting Brain Responses to Naturalistic Movies (Algonauts 2025)
Semih Eren, Deniz Kucukahmetler, Nico Scherf
https://arxiv.org/abs/2507.17897

Multimodal Recurrent Ensembles for Predicting Brain Responses to Naturalistic Movies (Algonauts 2025)
Accurately predicting distributed cortical responses to naturalistic stimuli requires models that integrate visual, auditory and semantic information over time. We present a hierarchical multimodal recurrent ensemble that maps pretrained video, audio, and language embeddings to fMRI time series recorded while four subjects watched almost 80 hours of movies provided by the Algonauts 2025 challenge. Modality-specific bidirectional RNNs encode temporal dynamics; their hidden states are fused and p…

CogniQ-H: A Soft Hierarchical Reinforcement Learning Paradigm for Automated Data Preparation
Jing Chang, Chang Liu, Jinbin Huang, Rui Mao, Jianbin Qin
https://arxiv.org/abs/2507.13710

CogniQ-H: A Soft Hierarchical Reinforcement Learning Paradigm for Automated Data Preparation
Data preparation is a foundational yet notoriously challenging component of the machine learning lifecycle, characterized by a vast combinatorial search space of potential operator sequences. While reinforcement learning (RL) offers a promising direction, existing approaches are inefficient as they fail to capture the structured, hierarchical nature of the problem. We argue that Hierarchical Reinforcement Learning (HRL), a paradigm that has been successful in other domains, provides a conceptua…

Efficient Strategy Synthesis for MDPs via Hierarchical Block Decomposition
Alexandros Evangelidis, Gricel V\'azquez, Simos Gerasimou
https://arxiv.org/abs/2506.17792

Efficient Strategy Synthesis for MDPs via Hierarchical Block Decomposition
Software-intensive systems, such as software product lines and robotics, utilise Markov decision processes (MDPs) to capture uncertainty and analyse sequential decision-making problems. Despite the usefulness of conventional policy synthesis methods, they fail to scale to large state spaces. Our approach addresses this issue and accelerates policy synthesis in large MDPs by dynamically refining the MDP and iteratively selecting the most fragile MDP regions for refinement. This iterative procedu…

Crosslisted article(s) found for cs.MM. https://arxiv.org/list/cs.MM/new
[1/1]:
- Hierarchical Vision-Language Reasoning for Multimodal Multiple-Choice Question Answering
Zhou, Gu, Sun, Chen, Tu, Cheng, Yin, Jiang, Gu

Pyramid Hierarchical Masked Diffusion Model for Imaging Synthesis
Xiaojiao Xiao, Qinmin Vivian Hu, Guanghui Wang
https://arxiv.org/abs/2507.16579 https://

Pyramid Hierarchical Masked Diffusion Model for Imaging Synthesis
Medical image synthesis plays a crucial role in clinical workflows, addressing the common issue of missing imaging modalities due to factors such as extended scan times, scan corruption, artifacts, patient motion, and intolerance to contrast agents. The paper presents a novel image synthesis network, the Pyramid Hierarchical Masked Diffusion Model (PHMDiff), which employs a multi-scale hierarchical approach for more detailed control over synthesizing high-quality images across different resolut…

Modelling longitudinal polytomous animal data using Bayesian hierarchical models
Maria Let\'icia Salvador, Gabriel Rodrigues Palma, Mariana Coelly Modesto Santos Tavares, Iran Jose Oliveira Silva, Idemauro Antonio Rodrigues de Lara
https://arxiv.org/abs/2507.17004

Modelling longitudinal polytomous animal data using Bayesian hierarchical models
The analysis of longitudinal categorical data can be complex and unfeasible due to the number of parameters involved, characterised by overparameterisation leading to model non-convergence, in addition to problems related to sample size and the presence or absence of overdispersion. In this context, we introduce Bayesian hierarchical models as an alternative methodology to classical statistical techniques for analysing nominal polytomous data in longitudinal studies. The theoretical foundation …

Hierarchical Maximum Likelihood Estimation for Time-Resolved NMR Data
Lennart H. Bosch, Pernille R. Jensen, Nico Striegler, Thomas Unden, Jochen Scharpf, Usman Qureshi, Philipp Neumann, Martin Gierse, John W. Blanchard, Stephan Knecht, Jochen Scheuer, Ilai Schwart, Martin B. Plenio
https://arxiv.org/abs/2508.14902

Hierarchical Maximum Likelihood Estimation for Time-Resolved NMR Data
Metabolic monitoring and reaction rate estimation using hyperpolarized NMR technology requires accurate quantitative analysis of multidimensional data scenarios. Currently, this analysis is often performed in a two-stage procedure, which is prone to errors in uncertainty propagation and estimation. We propose an approach derived from a Bayesian hierarchical model that intrinsically propagates uncertainties and operates on the full data to maximize the precision at minimal uncertainty. In an ana…

Arbitrage Tactics in the Local Markets via Hierarchical Multi-agent Reinforcement Learning
Haoyang Zhang, Mina Montazeri, Philipp Heer, Koen Kok, Nikolaos G. Paterakis
https://arxiv.org/abs/2507.16479

Arbitrage Tactics in the Local Markets via Hierarchical Multi-agent Reinforcement Learning
Strategic bidding tactics employed by prosumers in local markets, including the Local Electricity Market (LEM) and Local Flexibility Market (LFM), have attracted significant attention due to their potential to enhance economic benefits for market participants through optimized energy management and bidding. While existing research has explored strategic bidding in a single market with multi-agent reinforcement learning (MARL) algorithms, arbitrage opportunities across local markets remain unexp…

Statistical conservation laws for scalar model problems: Hierarchical evolution equations
Qian Huang, Christian Rohde
https://arxiv.org/abs/2508.15359 https://

Statistical conservation laws for scalar model problems: Hierarchical evolution equations
The probability density functions (PDFs) for the solution of the incompressible Navier-Stokes equation can be represented by a hierarchy of linear equations. This article develops new hierarchical evolution equations for PDFs of a scalar conservation law with random initial data as a model problem. Two frameworks are developed, including multi-point PDFs and single-point higher-order derivative PDFs. These hierarchies capture statistical correlations and guide closure strategies.

MinD: Unified Visual Imagination and Control via Hierarchical World Models
Xiaowei Chi, Kuangzhi Ge, Jiaming Liu, Siyuan Zhou, Peidong Jia, Zichen He, Yuzhen Liu, Tingguang Li, Lei Han, Sirui Han, Shanghang Zhang, Yike Guo
https://arxiv.org/abs/2506.18897

MinD: Unified Visual Imagination and Control via Hierarchical World Models
Video generation models (VGMs) offer a promising pathway for unified world modeling in robotics by integrating simulation, prediction, and manipulation. However, their practical application remains limited due to (1) slowgeneration speed, which limits real-time interaction, and (2) poor consistency between imagined videos and executable actions. To address these challenges, we propose Manipulate in Dream (MinD), a hierarchical diffusion-based world model framework that employs a dual-system des…

Hybrid Annotation for Propaganda Detection: Integrating LLM Pre-Annotations with Human Intelligence
Ariana Sahitaj, Premtim Sahitaj, Veronika Solopova, Jiaao Li, Sebastian M\"oller, Vera Schmitt
https://arxiv.org/abs/2507.18343

Hybrid Annotation for Propaganda Detection: Integrating LLM Pre-Annotations with Human Intelligence
Propaganda detection on social media remains challenging due to task complexity and limited high-quality labeled data. This paper introduces a novel framework that combines human expertise with Large Language Model (LLM) assistance to improve both annotation consistency and scalability. We propose a hierarchical taxonomy that organizes 14 fine-grained propaganda techniques into three broader categories, conduct a human annotation study on the HQP dataset that reveals low inter-annotator agreeme…

HEAS: Hierarchical Evolutionary Agent Simulation Framework for Cross-Scale Modeling and Multi-Objective Search
Ruiyu Zhang, Lin Nie, Xin Zhao
https://arxiv.org/abs/2508.15555 ht…

HEAS: Hierarchical Evolutionary Agent Simulation Framework for Cross-Scale Modeling and Multi-Objective Search
Hierarchical Evolutionary Agent Simulation (HEAS) is a Python framework that unifies layered agent-based modeling with evolutionary optimization and tournament evaluation in a single, reproducible workflow. HEAS represents models as hierarchies of lightweight processes ("streams") scheduled in deterministic layers that read and write a shared context, making cross-scale couplings explicit and auditable. A compact API and CLI-simulate, optimize, evaluate-expose single- and multi-objective evolut…

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data
Jer Shyuan Ng, Aditya Pribadi Kalapaaking, Xiaoyu Xia, Dusit Niyato, Ibrahim Khalil, Iqbal Gondal
https://arxiv.org/abs/2506.18259

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data
In recent years, Federated Learning (FL) has emerged as a widely adopted privacy-preserving distributed training approach, attracting significant interest from both academia and industry. Research efforts have been dedicated to improving different aspects of FL, such as algorithm improvement, resource allocation, and client selection, to enable its deployment in distributed edge networks for practical applications. One of the reasons for the poor FL model performance is due to the worker dropou…

H-QuEST: Accelerating Query-by-Example Spoken Term Detection with Hierarchical Indexing
Akanksha Singh, Yi-Ping Phoebe Chen, Vipul Arora
https://arxiv.org/abs/2506.16751

H-QuEST: Accelerating Query-by-Example Spoken Term Detection with Hierarchical Indexing
Query-by-example spoken term detection (QbE-STD) searches for matching words or phrases in an audio dataset using a sample spoken query. When annotated data is limited or unavailable, QbE-STD is often done using template matching methods like dynamic time warping (DTW), which are computationally expensive and do not scale well. To address this, we propose H-QuEST (Hierarchical Query-by-Example Spoken Term Detection), a novel framework that accelerates spoken term retrieval by utilizing Term Fre…

Fully-Dynamic Parallel Algorithms for Single-Linkage Clustering
Quinten De Man, Laxman Dhulipala, Kishen N Gowda
https://arxiv.org/abs/2506.18384 https://

Fully-Dynamic Parallel Algorithms for Single-Linkage Clustering
Single-linkage clustering is a popular form of hierarchical agglomerative clustering (HAC) where the distance between two clusters is defined as the minimum distance between any pair of points across the two clusters. In single-linkage HAC, the output is typically the single-linkage dendrogram (SLD), which is the binary tree representing the hierarchy of clusters formed by iteratively contracting the two closest clusters. In the dynamic setting, prior work has only studied maintaining a minimum…

Investigating Mobility in Spatial Biodiversity Models through Recurrence Quantification Analysis
Matheus Palmero, Matheus Bongestab
https://arxiv.org/abs/2507.18595 https://

Investigating Mobility in Spatial Biodiversity Models through Recurrence Quantification Analysis
Recurrence plots and their associated quantifiers provide a robust framework for detecting and characterising complex patterns in non-linear time-series. In this paper, we employ recurrence quantification analysis to investigate the dynamics of the cyclic, non-hierarchical May-Leonard model, also referred to as rock--paper--scissors systems, that describes competitive interactions among three species. A crucial control parameter in these systems is the species' mobility $m$, which governs the s…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[7/7]:
- Hierarchical Decision-Making for Autonomous Navigation: Integrating Deep Reinforcement Learning a...
Yizhi Wang, Degang Xu, Yongfang Xie, Shuzhong Tan, Xianan Zhou, Peng Chen

Toward Multi-Functional LAWNs with ISAC: Opportunities, Challenges, and the Road Ahead
Jun Wu, Weijie Yuan, Xiaoqi Zhang, Yaohuan Yu, Yuanhao Cui, Fan Liu, Geng Sun, Jiacheng Wang, Dusit Niyato, Dong In Kim
https://arxiv.org/abs/2508.17354

Toward Multi-Functional LAWNs with ISAC: Opportunities, Challenges, and the Road Ahead
Integrated sensing and communication (ISAC) has been envisioned as a foundational technology for future low-altitude wireless networks (LAWNs), enabling real-time environmental perception and data exchange across aerial-ground systems. In this article, we first explore the roles of ISAC in LAWNs from both node-level and network-level perspectives. We highlight the performance gains achieved through hierarchical integration and cooperation, wherein key design trade-offs are demonstrated. Apart f…

BoSS: Beyond-Semantic Speech
Qing Wang, Zehan Li, Hang Lv, Hongjie Chen, Yaodong Song, Jian Kang, Jie Lian, Jie Li, Yongxiang Li, Zhongjiang He, Xuelong Li
https://arxiv.org/abs/2507.17563

BoSS: Beyond-Semantic Speech
Human communication involves more than explicit semantics, with implicit signals and contextual cues playing a critical role in shaping meaning. However, modern speech technologies, such as Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) often fail to capture these beyond-semantic dimensions. To better characterize and benchmark the progression of speech intelligence, we introduce Spoken Interaction System Capability Levels (L1-L5), a hierarchical framework illustrated the evolution…

MAHL: Multi-Agent LLM-Guided Hierarchical Chiplet Design with Adaptive Debugging
Jinwei Tang (Katie), Jiayin Qin (Katie), Nuo Xu (Katie), Pragnya Sudershan Nalla (Katie), Yu Cao (Katie), Yang (Katie), Zhao, Caiwen Ding
https://arxiv.org/abs/2508.14053

MAHL: Multi-Agent LLM-Guided Hierarchical Chiplet Design with Adaptive Debugging
As program workloads (e.g., AI) increase in size and algorithmic complexity, the primary challenge lies in their high dimensionality, encompassing computing cores, array sizes, and memory hierarchies. To overcome these obstacles, innovative approaches are required. Agile chip design has already benefited from machine learning integration at various stages, including logic synthesis, placement, and routing. With Large Language Models (LLMs) recently demonstrating impressive proficiency in Hardwa…

GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface
Urchade Zaratiana, Gil Pasternak, Oliver Boyd, George Hurn-Maloney, Ash Lewis
https://arxiv.org/abs/2507.18546

GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface
Information extraction (IE) is fundamental to numerous NLP applications, yet existing solutions often require specialized models for different tasks or rely on computationally expensive large language models. We present GLiNER2, a unified framework that enhances the original GLiNER architecture to support named entity recognition, text classification, and hierarchical structured data extraction within a single efficient model. Built pretrained transformer encoder architecture, GLiNER2 maintains…

Hierarchical Graph Information Bottleneck for Multi-Behavior Recommendation
Hengyu Zhang, Chunxu Shen, Xiangguo Sun, Jie Tan, Yanchao Tan, Yu Rong, Hong Cheng, Lingling Yi
https://arxiv.org/abs/2507.15395

Hierarchical Graph Information Bottleneck for Multi-Behavior Recommendation
In real-world recommendation scenarios, users typically engage with platforms through multiple types of behavioral interactions. Multi-behavior recommendation algorithms aim to leverage various auxiliary user behaviors to enhance prediction for target behaviors of primary interest (e.g., buy), thereby overcoming performance limitations caused by data sparsity in target behavior records. Current state-of-the-art approaches typically employ hierarchical design following either cascading (e.g., vi…

Modeling Finite Deformations in Alloying Electrodes -- A Closer Look at Cracks and Pores During Phase Transformation
Delin Zhang, Yu-Cheng Lai, Kodi Thurber, Kai Smith, Johanna N. Weker, Sarah Tolbert, Ananya Renuka Balakrishna
https://arxiv.org/abs/2506.19982

Modeling Finite Deformations in Alloying Electrodes -- A Closer Look at Cracks and Pores During Phase Transformation
Nanostructured electrodes with voids or interconnected pores accommodate large volume changes, shorten ion diffusion pathways, and enhance the structural reversibility of alloying electrodes. While these nanoporous features improve the performance of architected electrodes over bulk electrodes, they also act as geometric irregularities that localize and concentrate internal stresses. In this work, we investigate the hierarchical interplay between phase boundaries and nanoporous features at the …

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus
Yifan Gao, Jiaxi Sheng, Wenbin Wu, Haoyue Li, Yaoxian Dong, Chaoyang Ge, Feng Yuan, Xin Gao
https://arxiv.org/abs/2506.18404

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus
Foundation models for volumetric medical image segmentation have emerged as powerful tools in clinical workflows, enabling radiologists to delineate regions of interest through intuitive clicks. While these models demonstrate promising capabilities in segmenting previously unseen anatomical structures, their performance is strongly influenced by prompt quality. In clinical settings, radiologists often provide suboptimal prompts, which affects segmentation reliability and accuracy. To address th…

Hierarchical constraints on gravitational waves from horizonless compact objects
Rajrupa Mondal, Julian Westerweck, Yotam Sherf, Collin D. Capano
https://arxiv.org/abs/2506.17215 …

Hierarchical constraints on gravitational waves from horizonless compact objects
We use the data of several promising gravitational wave observations to obtain increasingly stringent bounds on near-horizon deviations of their sources from the Kerr geometry. A range of horizonless compact objects proposed as alternatives to black holes of general relativity would possess a modified gravitational wave emission after the merger. Modelling these objects by introducing reflection of gravitational waves near the horizon, we can measure deviations from Kerr in terms of a single ad…

Spinning and random quotients preserve hierarchical hyperbolicity
Carolyn Abbott, Daniel Berlyne, Giorgio Mangioni, Thomas Ng, Alexander J. Rasmussen
https://arxiv.org/abs/2507.16677

Spinning and random quotients preserve hierarchical hyperbolicity
In this paper we consider random quotients of hierarchically hyperbolic groups, obtained by taking the quotient of the group by the n-th steps of a finite family of independent random walks. We show that a random quotient of a hierarchically hyperbolic group is again hierarchically hyperbolic asymptotically almost surely. The same techniques also yield that a random quotient of a non-elementary hyperbolic group (relative to a finite collection of peripheral subgroups) is asymptotically almost s…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[7/7]:
- Hierarchical Decision-Making for Autonomous Navigation: Integrating Deep Reinforcement Learning a...
Yizhi Wang, Degang Xu, Yongfang Xie, Shuzhong Tan, Xianan Zhou, Peng Chen

Continuous Indexed Points for Multivariate Volume Visualization
Liang Zhou, Xinyi Gou, Daniel Weiskopf
https://arxiv.org/abs/2506.19400 https://

Continuous Indexed Points for Multivariate Volume Visualization
We introduce continuous indexed points for improved multivariate volume visualization. Indexed points represent linear structures in parallel coordinates and can be used to encode local correlation of multivariate (including multifield, multifaceted, and multiattribute) volume data. First, we perform local linear fitting in the spatial neighborhood of each volume sample using principal component analysis, accelerated by hierarchical spatial data structures. This local linear information is then…

Dora: A Controller Provisioning Strategy in Hierarchical Domain-based Satellite Networks
Qiyuan Peng, Qi Zhang, Yue Gao, Kun Qiu
https://arxiv.org/abs/2507.14512

Dora: A Controller Provisioning Strategy in Hierarchical Domain-based Satellite Networks
The rapid proliferation of satellite constellations in Space-Air-Ground Integrated Networks (SAGIN) presents significant challenges for network management. Conventional flat network architectures struggle with synchronization and data transmission across massive distributed nodes. In response, hierarchical domain-based satellite network architectures have emerged as a scalable solution, highlighting the critical importance of controller provisioning strategies. However, existing network managem…

Spatial Policy: Guiding Visuomotor Robotic Manipulation with Spatial-Aware Modeling and Reasoning
Yijun Liu, Yuwei Liu, Yuan Meng, Jieheng Zhang, Yuwei Zhou, Ye Li, Jiacheng Jiang, Kangye Ji, Shijia Ge, Zhi Wang, Wenwu Zhu
https://arxiv.org/abs/2508.15874

Spatial Policy: Guiding Visuomotor Robotic Manipulation with Spatial-Aware Modeling and Reasoning
Vision-centric hierarchical embodied models have demonstrated strong potential for long-horizon robotic control. However, existing methods lack spatial awareness capabilities, limiting their effectiveness in bridging visual plans to actionable control in complex environments. To address this problem, we propose Spatial Policy (SP), a unified spatial-aware visuomotor robotic manipulation framework via explicit spatial modeling and reasoning. Specifically, we first design a spatial-conditioned em…

HierAdaptMR: Cross-Center Cardiac MRI Reconstruction with Hierarchical Feature Adapters
Ruru Xu, Ilkay Oksuz
https://arxiv.org/abs/2508.13026 https://arxiv…

HierAdaptMR: Cross-Center Cardiac MRI Reconstruction with Hierarchical Feature Adapters
Deep learning-based cardiac MRI reconstruction faces significant domain shift challenges when deployed across multiple clinical centers with heterogeneous scanner configurations and imaging protocols. We propose HierAdaptMR, a hierarchical feature adaptation framework that addresses multi-level domain variations through parameter-efficient adapters. Our method employs Protocol-Level Adapters for sequence-specific characteristics and Center-Level Adapters for scanner-dependent variations, built …

The Role of Excitatory Parvalbumin-positive Neurons in the Tectofugal Pathway of Pigeon (Columba livia) Hierarchical Visual Processing
Shan Lu, Xiaoteng Zhang, Yueyang Cang, Shihao Pan, Yanyan Peng, Xinwei Li, Shaoju Zeng, Yingjie Zhu, Li Shi
https://arxiv.org/abs/2507.15486

The Role of Excitatory Parvalbumin-positive Neurons in the Tectofugal Pathway of Pigeon (Columba livia) Hierarchical Visual Processing
The visual systems of birds and mammals exhibit remarkable organizational similarities: the dorsal ventricular ridge (DVR) demonstrates a columnar microcircuitry that parallels the cortical architecture observed in mammals. However, the specific neuronal subtypes involved and their functional roles in pigeon hierarchical visual processing remain unclear. This study investigates the role of excitatory parvalbumin (PV+) neurons within the Ento-MVL (entoallium-mesopallium venterolaterale) circuit …

CBF-AFA: Chunk-Based Multi-SSL Fusion for Automatic Fluency Assessment
Papa S\'ega Wade, Mihai Andries, Ioannis Kanellos, Thierry Moudenc
https://arxiv.org/abs/2506.20243

CBF-AFA: Chunk-Based Multi-SSL Fusion for Automatic Fluency Assessment
Automatic fluency assessment (AFA) remains challenging, particularly in capturing speech rhythm, pauses, and disfluencies in non-native speakers. We introduce a chunk-based approach integrating self-supervised learning (SSL) models (Wav2Vec2, HuBERT, and WavLM) selected for their complementary strengths in phonetic, prosodic, and noisy speech modeling, with a hierarchical CNN-BiLSTM framework. Speech is segmented into breath-group chunks using Silero voice activity detection (Silero-VAD), enabl…

HERAKLES: Hierarchical Skill Compilation for Open-ended LLM Agents
Thomas Carta, Cl\'ement Romac, Loris Gaven, Pierre-Yves Oudeyer, Olivier Sigaud, Sylvain Lamprier
https://arxiv.org/abs/2508.14751

HERAKLES: Hierarchical Skill Compilation for Open-ended LLM Agents
Open-ended AI agents need to be able to learn efficiently goals of increasing complexity, abstraction and heterogeneity over their lifetime. Beyond sampling efficiently their own goals, autotelic agents specifically need to be able to keep the growing complexity of goals under control, limiting the associated growth in sample and computational complexity. To adress this challenge, recent approaches have leveraged hierarchical reinforcement learning (HRL) and language, capitalizing on its compos…

Convergence Rate of Efficient MCMC with Ancillarity-Sufficiency Interweaving Strategy for Panel Data Models
Makoto Nakakita, Tomoki Toyabe, Teruo Nakatsuma, Takahiro Hoshino
https://arxiv.org/abs/2507.18404

Convergence Rate of Efficient MCMC with Ancillarity-Sufficiency Interweaving Strategy for Panel Data Models
Improving Markov chain Monte Carlo algorithm efficiency is essential for enhancing computational speed and inferential accuracy in Bayesian analysis. These improvements can be effectively achieved using the ancillarity-sufficiency interweaving strategy (ASIS), an effective means of achieving such gains. Herein, we provide the first rigorous theoretical justification for applying ASIS in Bayesian hierarchical panel data models. Asymptotic analysis demonstrated that when the product of prior vari…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[4/4]:
- Graph-Assisted Stitching for Offline Hierarchical Reinforcement Learning
Seungho Baek, Taegeon Park, Jongchan Park, Seungjun Oh, Yusung Kim

Hierarchical Black Hole Mergers in AGN Disks: Tracing Massive Black Hole Growth Across Cosmic Time
Maria Paola Vaccaro
https://arxiv.org/abs/2508.15337 https://

Hierarchical Black Hole Mergers in AGN Disks: Tracing Massive Black Hole Growth Across Cosmic Time
The dense and gaseous environments of active galactic nuclei (AGNs) can catalyze repeated mergers of stellar-mass black holes (BHs), potentially explaining the high-mass tail of binary black hole (BBH) mergers observed by LIGO-Virgo-KAGRA (LVK). We present a semi-analytical population synthesis framework that captures key physical processes in AGN disks, including gas capture, migration, binary pair-up, gas hardening, and dynamical binary-single interactions. Our simulations show that AGN disks…

Cooperative Suppression Strategy for Dual Thermal Transport Channels in Crystalline Materials
Yu Wu, Ying Chen, Shuming Zeng, Hao Zhang, Liujiang Zhou, Chenhan Liu, Su-Huai Wei
https://arxiv.org/abs/2508.17318

Cooperative Suppression Strategy for Dual Thermal Transport Channels in Crystalline Materials
We propose a novel design principle for achieving ultralow thermal conductivity in crystalline materials via a "heavy-light and soft-stiff" structural motif. By combining heavy and light atomic species with soft and stiff bonding networks, both particle-like ($κ_p$) and wave-like ($κ_c$) phonon transport channels are concurrently suppressed. First-principles calculations show that this architecture induces a hierarchical phonon spectrum: soft-bonded heavy atoms generate dense low-frequency mo…

MATER: Multi-level Acoustic and Textual Emotion Representation for Interpretable Speech Emotion Recognition
Hyo Jin Jon, Longbin Jin, Hyuntaek Jung, Hyunseo Kim, Donghun Min, Eun Yi Kim
https://arxiv.org/abs/2506.19887

MATER: Multi-level Acoustic and Textual Emotion Representation for Interpretable Speech Emotion Recognition
This paper presents our contributions to the Speech Emotion Recognition in Naturalistic Conditions (SERNC) Challenge, where we address categorical emotion recognition and emotional attribute prediction. To handle the complexities of natural speech, including intra- and inter-subject variability, we propose Multi-level Acoustic-Textual Emotion Representation (MATER), a novel hierarchical framework that integrates acoustic and textual features at the word, utterance, and embedding levels. By fusi…

SHINE: A Scalable HNSW Index in Disaggregated Memory
Manuel Widmoser, Daniel Kocher, Nikolaus Augsten
https://arxiv.org/abs/2507.17647 https://

SHINE: A Scalable HNSW Index in Disaggregated Memory
Approximate nearest neighbor (ANN) search is a fundamental problem in computer science for which in-memory graph-based methods, such as Hierarchical Navigable Small World (HNSW), perform exceptionally well. To scale beyond billions of high-dimensional vectors, the index must be distributed. The disaggregated memory architecture physically separates compute and memory into two distinct hardware units and has become popular in modern data centers. Both units are connected via RDMA networks that a…

MOHAF: A Multi-Objective Hierarchical Auction Framework for Scalable and Fair Resource Allocation in IoT Ecosystems
Kushagra Agrawal, Polat Goktas, Anjan Bandopadhyay, Debolina Ghosh, Junali Jasmine Jena, Mahendra Kumar Gourisaria
https://arxiv.org/abs/2508.14830

MOHAF: A Multi-Objective Hierarchical Auction Framework for Scalable and Fair Resource Allocation in IoT Ecosystems
The rapid growth of Internet of Things (IoT) ecosystems has intensified the challenge of efficiently allocating heterogeneous resources in highly dynamic, distributed environments. Conventional centralized mechanisms and single-objective auction models, focusing solely on metrics such as cost minimization or revenue maximization, struggle to deliver balanced system performance. This paper proposes the Multi-Objective Hierarchical Auction Framework (MOHAF), a distributed resource allocation mech…

ECHO: Frequency-aware Hierarchical Encoding for Variable-length Signal
Yucong Zhang, Juan Liu, Ming Li
https://arxiv.org/abs/2508.14689 https://arxiv.org/p…

ECHO: Frequency-aware Hierarchical Encoding for Variable-length Signal
Pre-trained foundation models have demonstrated remarkable success in vision and language, yet their potential for general machine signal modeling-covering acoustic, vibration, and other industrial sensor data-remains under-explored. Existing approach using sub-band-based encoders has achieved competitive results but are limited by fixed input lengths, and the absence of explicit frequency positional encoding. In this work, we propose a novel foundation model that integrates an advanced band-sp…

Music-Aligned Holistic 3D Dance Generation via Hierarchical Motion Modeling
Xiaojie Li, Ronghui Li, Shukai Fang, Shuzhao Xie, Xiaoyang Guo, Jiaqing Zhou, Junkun Peng, Zhi Wang
https://arxiv.org/abs/2507.14915

Music-Aligned Holistic 3D Dance Generation via Hierarchical Motion Modeling
Well-coordinated, music-aligned holistic dance enhances emotional expressiveness and audience engagement. However, generating such dances remains challenging due to the scarcity of holistic 3D dance datasets, the difficulty of achieving cross-modal alignment between music and dance, and the complexity of modeling interdependent motion across the body, hands, and face. To address these challenges, we introduce SoulDance, a high-precision music-dance paired dataset captured via professional motio…

A Hierarchical Surrogate Model for Efficient Multi-Task Parameter Learning in Closed-Loop Contro
Sebastian Hirt, Lukas Theiner, Maik Pfefferkorn, Rolf Findeisen
https://arxiv.org/abs/2508.12738

A Hierarchical Surrogate Model for Efficient Multi-Task Parameter Learning in Closed-Loop Contro
Many control problems require repeated tuning and adaptation of controllers across distinct closed-loop tasks, where data efficiency and adaptability are critical. We propose a hierarchical Bayesian optimization (BO) framework that is tailored to efficient controller parameter learning in sequential decision-making and control scenarios for distinct tasks. Instead of treating the closed-loop cost as a black-box, our method exploits structural knowledge of the underlying problem, consisting of a…

Replaced article(s) found for q-bio.QM. https://arxiv.org/list/q-bio.QM/new
[1/1]:
- Unveiling Molecular Moieties through Hierarchical Grad-CAM Graph Explainability
Salvatore Contino, Paolo Sortino, Maria Rita Gulotta, Ugo Perricone, Roberto Pirrone

Hierarchical Graph Feature Enhancement with Adaptive Frequency Modulation for Visual Recognition
Feiyue Zhao, Zhichao Zhang
https://arxiv.org/abs/2508.11497 https://

Hierarchical Graph Feature Enhancement with Adaptive Frequency Modulation for Visual Recognition
Convolutional neural networks (CNNs) have demonstrated strong performance in visual recognition tasks, but their inherent reliance on regular grid structures limits their capacity to model complex topological relationships and non-local semantics within images. To address this limita tion, we propose the hierarchical graph feature enhancement (HGFE), a novel framework that integrates graph-based rea soning into CNNs to enhance both structural awareness and feature representation. HG…

Hierarchical Rectified Flow Matching with Mini-Batch Couplings
Yichi Zhang, Yici Yan, Alex Schwing, Zhizhen Zhao
https://arxiv.org/abs/2507.13350 https://

Hierarchical Rectified Flow Matching with Mini-Batch Couplings
Flow matching has emerged as a compelling generative modeling approach that is widely used across domains. To generate data via a flow matching model, an ordinary differential equation (ODE) is numerically solved via forward integration of the modeled velocity field. To better capture the multi-modality that is inherent in typical velocity fields, hierarchical flow matching was recently introduced. It uses a hierarchy of ODEs that are numerically integrated when generating data. This hierarchy …

Robust Symbolic Reasoning for Visual Narratives via Hierarchical and Semantically Normalized Knowledge Graphs
Yi-Chun Chen
https://arxiv.org/abs/2508.14941 https://

Robust Symbolic Reasoning for Visual Narratives via Hierarchical and Semantically Normalized Knowledge Graphs
Understanding visual narratives such as comics requires structured representations that capture events, characters, and their relations across multiple levels of story organization. However, symbolic narrative graphs often suffer from inconsistency and redundancy, where similar actions or events are labeled differently across annotations or contexts. Such variance limits the effectiveness of reasoning and generalization. This paper introduces a semantic normalization framework for hierarchica…

CHADET: Cross-Hierarchical-Attention for Depth-Completion Using Unsupervised Lightweight Transformer
Kevin Christiansen Marsim, Jinwoo Jeon, Yeeun Kim, Myeongwoo Jeong, Hyun Myung
https://arxiv.org/abs/2507.15189

CHADET: Cross-Hierarchical-Attention for Depth-Completion Using Unsupervised Lightweight Transformer
Depth information which specifies the distance between objects and current position of the robot is essential for many robot tasks such as navigation. Recently, researchers have proposed depth completion frameworks to provide dense depth maps that offer comprehensive information about the surrounding environment. However, existing methods show significant trade-offs between computational efficiency and accuracy during inference. The substantial memory and computational requirements make them un…

EndoControlMag: Robust Endoscopic Vascular Motion Magnification with Periodic Reference Resetting and Hierarchical Tissue-aware Dual-Mask Contro
An Wanga, Rulin Zhou, Mengya Xu, Yiru Ye, Longfei Gou, Yiting Chang, Hao Chen, Chwee Ming Lim, Jiankun Wang, Hongliang Ren
https://arxiv.org/abs/2507.15292

EndoControlMag: Robust Endoscopic Vascular Motion Magnification with Periodic Reference Resetting and Hierarchical Tissue-aware Dual-Mask Contro
Visualizing subtle vascular motions in endoscopic surgery is crucial for surgical precision and decision-making, yet remains challenging due to the complex and dynamic nature of surgical scenes. To address this, we introduce EndoControlMag, a training-free, Lagrangian-based framework with mask-conditioned vascular motion magnification tailored to endoscopic environments. Our approach features two key modules: a Periodic Reference Resetting (PRR) scheme that divides videos into short overlapping…

GW231123: a product of successive mergers from $\sim 10 $ stellar-mass black holes
Yin-Jie Li, Shan-Peng Tang, Ling-Qin Xue, Yi-Zhong Fan
https://arxiv.org/abs/2507.17551 https:…

GW231123: a product of successive mergers from $\sim 10 $ stellar-mass black holes
GW231123 is an exceptionally massive binary black hole (BBH) merger with unusually high component spins. Such extreme properties challenge conventional stellar evolution models predicting a black hole mass gap due to pair-instability supernovae. We analyze GW231123 using population-informed priors on BH mass and spin distributions to test possible formation scenarios: first-generation stellar collapse, hierarchical (multi-generation) mergers, and primordial origin. Our analysis strongly prefers…

A Theoretical Framework for Virtual Power Plant Integration with Gigawatt-Scale AI Data Centers: Multi-Timescale Control and Stability Analysis
Ali Peivandizadeh
https://arxiv.org/abs/2506.17284

A Theoretical Framework for Virtual Power Plant Integration with Gigawatt-Scale AI Data Centers: Multi-Timescale Control and Stability Analysis
The explosive growth of artificial intelligence has created gigawatt-scale data centers that fundamentally challenge power system operation, exhibiting power fluctuations exceeding 500 MW within seconds and millisecond-scale variations of 50-75% of thermal design power. This paper presents a comprehensive theoretical framework that reconceptualizes Virtual Power Plants (VPPs) to accommodate these extreme dynamics through a four-layer hierarchical control architecture operating across timescales…

A Hierarchical Sharded Blockchain Balancing Performance and Availability
Yongrae Jo, Chanik Park
https://arxiv.org/abs/2508.14457 https://arxiv.org/pdf/250…

A Hierarchical Sharded Blockchain Balancing Performance and Availability
Blockchain networks offer decentralization, transparency, and immutability for managing critical data but encounter scalability problems as the number of network members and transaction issuers grows. Sharding is considered a promising solution to enhance blockchain scalability. However, most existing blockchain sharding techniques prioritize performance at the cost of availability (e.g., a failure in a few servers holding a shard leads to data unavailability). In this paper, we propose PyloCha…

Sequential Confirmatory Factor Analysis: A Novel Approach to Latent Variable Measurement
Zachary Esses Johnson
https://arxiv.org/abs/2508.15611 https://arx…

Sequential Confirmatory Factor Analysis: A Novel Approach to Latent Variable Measurement
Factor score estimation in small sample sizes often encounters parameter bias and convergence failures when constructing hierarchical national/sub-national indices. This paper proposes a novel method for hierarchical factor analysis called "sequential Confirmatory Factor Analysis". Instead of estimating multiple levels of factors at the same time, this approach calculates factor scores sequentially from the lowest to highest levels. This sequential estimation keeps the original sample size in e…

DEPTH: Hallucination-Free Relation Extraction via Dependency-Aware Sentence Simplification and Two-tiered Hierarchical Refinement
Yupei Yang, Fan Feng, Lin Yang, Wanxi Deng, Lin Qu, Biwei Huang, Shikui Tu, Lei Xu
https://arxiv.org/abs/2508.14391

DEPTH: Hallucination-Free Relation Extraction via Dependency-Aware Sentence Simplification and Two-tiered Hierarchical Refinement
Relation extraction enables the construction of structured knowledge for many downstream applications. While large language models (LLMs) have shown great promise in this domain, most existing methods concentrate on relation classification, which predicts the semantic relation type between a related entity pair. However, we observe that LLMs often struggle to reliably determine whether a relation exists, especially in cases involving complex sentence structures or intricate semantics, which lea…

Unified Hierarchical MPC in Task Executing for Modular Manipulators across Diverse Morphologies
Maolin Lei, Edoardo Romiti, Arturo Laurenzi, Cheng Zhou, Wanli Xing, Liang Lu, Nikos G. Tsagarakis
https://arxiv.org/abs/2508.13513

Unified Hierarchical MPC in Task Executing for Modular Manipulators across Diverse Morphologies
This work proposes a unified Hierarchical Model Predictive Control (H-MPC) for modular manipulators across various morphologies, as the controller can adapt to different configurations to execute the given task without extensive parameter tuning in the controller. The H-MPC divides the control process into two levels: a high-level MPC and a low-level MPC. The high-level MPC predicts future states and provides trajectory information, while the low-level MPC refines control actions by updating th…

Hierarchical Vision-Language Retrieval of Educational Metaverse Content in Agriculture
Ali Abdari, Alex Falcon, Giuseppe Serra
https://arxiv.org/abs/2508.13713 https://

Hierarchical Vision-Language Retrieval of Educational Metaverse Content in Agriculture
Every day, a large amount of educational content is uploaded online across different areas, including agriculture and gardening. When these videos or materials are grouped meaningfully, they can make learning easier and more effective. One promising way to organize and enrich such content is through the Metaverse, which allows users to explore educational experiences in an interactive and immersive environment. However, searching for relevant Metaverse scenarios and finding those matching users…

Hierarchical knowledge guided fault intensity diagnosis of complex industrial systems
Yu Sha, Shuiping Gou, Bo Liu, Johannes Faber, Ningtao Liu, Stefan Schramm, Horst Stoecker, Thomas Steckenreiter, Domagoj Vnucec, Nadine Wetzstein, Andreas Widl, Kai Zhou
https://arxiv.org/abs/2508.12375

Hierarchical knowledge guided fault intensity diagnosis of complex industrial systems
Fault intensity diagnosis (FID) plays a pivotal role in monitoring and maintaining mechanical devices within complex industrial systems. As current FID methods are based on chain of thought without considering dependencies among target classes. To capture and explore dependencies, we propose a hierarchical knowledge guided fault intensity diagnosis framework (HKG) inspired by the tree of thought, which is amenable to any representation learning methods. The HKG uses graph convolutional networks…

Efficient Temporal Tokenization for Mobility Prediction with Large Language Models
Haoyu He, Haozheng Luo, Yan Chen, Qi R. Wang
https://arxiv.org/abs/2507.14017

Efficient Temporal Tokenization for Mobility Prediction with Large Language Models
We introduce RHYTHM (Reasoning with Hierarchical Temporal Tokenization for Human Mobility), a framework that leverages large language models (LLMs) as spatio-temporal predictors and trajectory reasoners. RHYTHM partitions trajectories into daily segments encoded as discrete tokens with hierarchical attention, capturing both daily and weekly dependencies while substantially reducing the sequence length. Token representations are enriched with pre-computed prompt embeddings via a frozen LLM, enha…

Reflective VLM Planning for Dual-Arm Desktop Cleaning: Bridging Open-Vocabulary Perception and Precise Manipulation
Yufan Liu, Yi Wu, Gweneth Ge, Haoliang Cheng, Rui Liu
https://arxiv.org/abs/2506.17328

Reflective VLM Planning for Dual-Arm Desktop Cleaning: Bridging Open-Vocabulary Perception and Precise Manipulation
Desktop cleaning demands open-vocabulary recognition and precise manipulation for heterogeneous debris. We propose a hierarchical framework integrating reflective Vision-Language Model (VLM) planning with dual-arm execution via structured scene representation. Grounded-SAM2 facilitates open-vocabulary detection, while a memory-augmented VLM generates, critiques, and revises manipulation sequences. These sequences are converted into parametric trajectories for five primitives executed by coordin…

Compressed Video Super-Resolution based on Hierarchical Encoding
Yuxuan Jiang, Siyue Teng, Qiang Zhu, Chen Feng, Chengxi Zeng, Fan Zhang, Shuyuan Zhu, Bing Zeng, David Bull
https://arxiv.org/abs/2506.14381

Compressed Video Super-Resolution based on Hierarchical Encoding
This paper presents a general-purpose video super-resolution (VSR) method, dubbed VSR-HE, specifically designed to enhance the perceptual quality of compressed content. Targeting scenarios characterized by heavy compression, the method upscales low-resolution videos by a ratio of four, from 180p to 720p or from 270p to 1080p. VSR-HE adopts hierarchical encoding transformer blocks and has been sophisticatedly optimized to eliminate a wide range of compression artifacts commonly introduced by H.2…

HieraRS: A Hierarchical Segmentation Paradigm for Remote Sensing Enabling Multi-Granularity Interpretation and Cross-Domain Transfer
Tianlong Ai, Tianzhu Liu, Haochen Jiang, Yanfeng Gu
https://arxiv.org/abs/2507.08741

HieraRS: A Hierarchical Segmentation Paradigm for Remote Sensing Enabling Multi-Granularity Interpretation and Cross-Domain Transfer
Hierarchical land cover and land use (LCLU) classification aims to assign pixel-wise labels with multiple levels of semantic granularity to remote sensing (RS) imagery. However, existing deep learning-based methods face two major challenges: 1) They predominantly adopt a flat classification paradigm, which limits their ability to generate end-to-end multi-granularity hierarchical predictions aligned with tree-structured hierarchies used in practice. 2) Most cross-domain studies focus on perform…

Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning
Martin Klissarov, Akhil Bagaria, Ziyan Luo, George Konidaris, Doina Precup, Marlos C. Machado
https://arxiv.org/abs/2506.14045

Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning
Developing agents capable of exploring, planning and learning in complex open-ended environments is a grand challenge in artificial intelligence (AI). Hierarchical reinforcement learning (HRL) offers a promising solution to this challenge by discovering and exploiting the temporal structure within a stream of experience. The strong appeal of the HRL framework has led to a rich and diverse body of literature attempting to discover a useful structure. However, it is still not clear how one might …

EEG-MedRAG: Enhancing EEG-based Clinical Decision-Making via Hierarchical Hypergraph Retrieval-Augmented Generation
Yi Wang, Haoran Luo, Lu Meng
https://arxiv.org/abs/2508.13735

EEG-MedRAG: Enhancing EEG-based Clinical Decision-Making via Hierarchical Hypergraph Retrieval-Augmented Generation
With the widespread application of electroencephalography (EEG) in neuroscience and clinical practice, efficiently retrieving and semantically interpreting large-scale, multi-source, heterogeneous EEG data has become a pressing challenge. We propose EEG-MedRAG, a three-layer hypergraph-based retrieval-augmented generation framework that unifies EEG domain knowledge, individual patient cases, and a large-scale repository into a traversable n-ary relational hypergraph, enabling joint semantic-tem…

MHSNet:An MoE-based Hierarchical Semantic Representation Network for Accurate Duplicate Resume Detection with Large Language Model
Yu Li, Zulong Chen, Wenjian Xu, Hong Wen, Yipeng Yu, Man Lung Yiu, Yuyu Yin
https://arxiv.org/abs/2508.13676

MHSNet:An MoE-based Hierarchical Semantic Representation Network for Accurate Duplicate Resume Detection with Large Language Model
To maintain the company's talent pool, recruiters need to continuously search for resumes from third-party websites (e.g., LinkedIn, Indeed). However, fetched resumes are often incomplete and inaccurate. To improve the quality of third-party resumes and enrich the company's talent pool, it is essential to conduct duplication detection between the fetched resumes and those already in the company's talent pool. Such duplication detection is challenging due to the semantic complexity, structural h…

HUG-VAS: A Hierarchical NURBS-Based Generative Model for Aortic Geometry Synthesis and Controllable Editing
Pan Du, Mingqi Xu, Xiaozhi Zhu, Jian-xun Wang
https://arxiv.org/abs/2507.11474

HUG-VAS: A Hierarchical NURBS-Based Generative Model for Aortic Geometry Synthesis and Controllable Editing
Accurate characterization of vascular geometry is essential for cardiovascular diagnosis and treatment planning. Traditional statistical shape modeling (SSM) methods rely on linear assumptions, limiting their expressivity and scalability to complex topologies such as multi-branch vascular structures. We introduce HUG-VAS, a Hierarchical NURBS Generative model for Vascular geometry Synthesis, which integrates NURBS surface parameterization with diffusion-based generative modeling to synthesize r…

HCOMC: A Hierarchical Cooperative On-Ramp Merging Control Framework in Mixed Traffic Environment on Two-Lane Highways
Tianyi Wang, Yangyang Wang, Jie Pan, Junfeng Jiao, Christian Claudel
https://arxiv.org/abs/2507.11621

HCOMC: A Hierarchical Cooperative On-Ramp Merging Control Framework in Mixed Traffic Environment on Two-Lane Highways
Highway on-ramp merging areas are common bottlenecks to traffic congestion and accidents. Currently, a cooperative control strategy based on connected and automated vehicles (CAVs) is a fundamental solution to this problem. While CAVs are not fully widespread, it is necessary to propose a hierarchical cooperative on-ramp merging control (HCOMC) framework for heterogeneous traffic flow on two-lane highways to address this gap. This paper extends longitudinal car-following models based on the int…

Hierarchical Fine-grained Preference Optimization for Physically Plausible Video Generation
Harold Haodong Chen, Haojian Huang, Qifeng Chen, Harry Yang, Ser-Nam Lim
https://arxiv.org/abs/2508.10858

Hierarchical Fine-grained Preference Optimization for Physically Plausible Video Generation
Recent advancements in video generation have enabled the creation of high-quality, visually compelling videos. However, generating videos that adhere to the laws of physics remains a critical challenge for applications requiring realism and accuracy. In this work, we propose PhysHPO, a novel framework for Hierarchical Cross-Modal Direct Preference Optimization, to tackle this challenge by enabling fine-grained preference alignment for physically plausible video generation. PhysHPO optimizes vid…

LeanRAG: Knowledge-Graph-Based Generation with Semantic Aggregation and Hierarchical Retrieval
Yaoze Zhang, Rong Wu, Pinlong Cai, Xiaoman Wang, Guohang Yan, Song Mao, Ding Wang, Botian Shi
https://arxiv.org/abs/2508.10391

LeanRAG: Knowledge-Graph-Based Generation with Semantic Aggregation and Hierarchical Retrieval
Retrieval-Augmented Generation (RAG) plays a crucial role in grounding Large Language Models by leveraging external knowledge, whereas the effectiveness is often compromised by the retrieval of contextually flawed or incomplete information. To address this, knowledge graph-based RAG methods have evolved towards hierarchical structures, organizing knowledge into multi-level summaries. However, these approaches still suffer from two critical, unaddressed challenges: high-level conceptual summarie…

EquiContact: A Hierarchical SE(3) Vision-to-Force Equivariant Policy for Spatially Generalizable Contact-rich Tasks
Joohwan Seo, Arvind Kruthiventy, Soomi Lee, Megan Teng, Xiang Zhang, Seoyeon Choi, Jongeun Choi, Roberto Horowitz
https://arxiv.org/abs/2507.10961

EquiContact: A Hierarchical SE(3) Vision-to-Force Equivariant Policy for Spatially Generalizable Contact-rich Tasks
This paper presents a framework for learning vision-based robotic policies for contact-rich manipulation tasks that generalize spatially across task configurations. We focus on achieving robust spatial generalization of the policy for the peg-in-hole (PiH) task trained from a small number of demonstrations. We propose EquiContact, a hierarchical policy composed of a high-level vision planner (Diffusion Equivariant Descriptor Field, Diff-EDF) and a novel low-level compliant visuomotor policy (Ge…

Hierarchical Deep Feature Fusion and Ensemble Learning for Enhanced Brain Tumor MRI Classification
Zahid Ullah, Jihie Kim
https://arxiv.org/abs/2506.12363 …

Hierarchical Deep Feature Fusion and Ensemble Learning for Enhanced Brain Tumor MRI Classification
Accurate brain tumor classification is crucial in medical imaging to ensure reliable diagnosis and effective treatment planning. This study introduces a novel double ensembling framework that synergistically combines pre-trained deep learning (DL) models for feature extraction with optimized machine learning (ML) classifiers for robust classification. The framework incorporates comprehensive preprocessing and data augmentation of brain magnetic resonance images (MRI), followed by deep feature e…

Acting and Planning with Hierarchical Operational Models on a Mobile Robot: A Study with RAE UPOM
Oscar Lima, Marc Vinci, Sunandita Patra, Sebastian Stock, Joachim Hertzberg, Martin Atzmueller, Malik Ghallab, Dana Nau, Paolo Traverso
https://arxiv.org/abs/2507.11345

Acting and Planning with Hierarchical Operational Models on a Mobile Robot: A Study with RAE+UPOM
Robotic task execution faces challenges due to the inconsistency between symbolic planner models and the rich control structures actually running on the robot. In this paper, we present the first physical deployment of an integrated actor-planner system that shares hierarchical operational models for both acting and planning, interleaving the Reactive Acting Engine (RAE) with an anytime UCT-like Monte Carlo planner (UPOM). We implement RAE+UPOM on a mobile manipulator in a real-world deployment…