Tootfinder

Hierarchy or Heterarchy? A Theory of Long-Range Connections for the Sensorimotor Brain
Jeff Hawkins (Thousand Brains Project), Niels Leadholm (Thousand Brains Project), Viviane Clay (Thousand Brains Project)
https://arxiv.org/abs/2507.05888

Hierarchy or Heterarchy? A Theory of Long-Range Connections for the Sensorimotor Brain
In the traditional understanding of the neocortex, sensory information flows up a hierarchy of regions, with each level processing increasingly complex features. Information also flows down the hierarchy via a different set of connections. Although the hierarchical model has significant support, many anatomical connections do not conform to the standard hierarchical interpretation. In addition, hierarchically arranged regions sometimes respond in parallel, not sequentially as would occur in a h…

Multi-Step Prediction and Control of Hierarchical Emotion Distribution in Text-to-Speech Synthesis
Sho Inoue, Kun Zhou, Shuai Wang, Haizhou Li
https://arxiv.org/abs/2507.04598

Multi-Step Prediction and Control of Hierarchical Emotion Distribution in Text-to-Speech Synthesis
We investigate hierarchical emotion distribution (ED) for achieving multi-level quantitative control of emotion rendering in text-to-speech synthesis (TTS). We introduce a novel multi-step hierarchical ED prediction module that quantifies emotion variance at the utterance, word, and phoneme levels. By predicting emotion variance in a multi-step manner, we leverage global emotional context to refine local emotional variations, thereby capturing the intrinsic hierarchical structure of speech emot…

Hi3DEval: Advancing 3D Generation Evaluation with Hierarchical Validity
Yuhan Zhang, Long Zhuo, Ziyang Chu, Tong Wu, Zhibing Li, Liang Pan, Dahua Lin, Ziwei Liu
https://arxiv.org/abs/2508.05609

Hi3DEval: Advancing 3D Generation Evaluation with Hierarchical Validity
Despite rapid advances in 3D content generation, quality assessment for the generated 3D assets remains challenging. Existing methods mainly rely on image-based metrics and operate solely at the object level, limiting their ability to capture spatial coherence, material authenticity, and high-fidelity local details. 1) To address these challenges, we introduce Hi3DEval, a hierarchical evaluation framework tailored for 3D generative content. It combines both object-level and part-level evaluatio…

H-Net : Hierarchical Dynamic Chunking for Tokenizer-Free Language Modelling in Morphologically-Rich Languages
Mehrdad Zakershahrak, Samira Ghodratnama
https://arxiv.org/abs/2508.05628

H-Net++: Hierarchical Dynamic Chunking for Tokenizer-Free Language Modelling in Morphologically-Rich Languages
Byte-level language models eliminate fragile tokenizers but face computational challenges in morphologically-rich languages (MRLs), where words span many bytes. We propose H-NET++, a hierarchical dynamic-chunking model that learns linguistically-informed segmentation through end-to-end training. Key innovations include: (1) a lightweight Transformer context-mixer (1.9M parameters) for cross-chunk attention, (2) a two-level latent hyper-prior for document-level consistency, (3) specialized handl…

Hierarchical Reduced-Order Model Predictive Control for Robust Locomotion on Humanoid Robots
Adrian B. Ghansah, Sergio A. Esteban, Aaron D. Ames
https://arxiv.org/abs/2509.04722

Hierarchical Reduced-Order Model Predictive Control for Robust Locomotion on Humanoid Robots
As humanoid robots enter real-world environments, ensuring robust locomotion across diverse environments is crucial. This paper presents a computationally efficient hierarchical control framework for humanoid robot locomotion based on reduced-order models -- enabling versatile step planning and incorporating arm and torso dynamics to better stabilize the walking. At the high level, we use the step-to-step dynamics of the ALIP model to simultaneously optimize over step periods, step lengths, and…

Learning-Augmented Hierarchical Clustering
Vladimir Braverman, Jon C. Ergun, Chen Wang, Samson Zhou
https://arxiv.org/abs/2506.05495 https://

Learning-Augmented Hierarchical Clustering
Hierarchical clustering (HC) is an important data analysis technique in which the goal is to recursively partition a dataset into a tree-like structure while grouping together similar data points at each level of granularity. Unfortunately, for many of the proposed HC objectives, there exist strong barriers to approximation algorithms with the hardness of approximation. Thus, we consider the problem of hierarchical clustering given auxiliary information from natural oracles. Specifically, we fo…

Hierarchical Subtraction with Neural Density Estimators as a General Solution to Overlapping Gravitational Wave Signals
Qian Hu
https://arxiv.org/abs/2507.05209

Hierarchical Subtraction with Neural Density Estimators as a General Solution to Overlapping Gravitational Wave Signals
Overlapping gravitational wave (GW) signals are expected in the third-generation (3G) GW detectors, leading to one of the major challenges in GW data analysis. Inference of overlapping GW sources is complicated - it has been reported that hierarchical inference with signal subtraction may amplify errors, while joint estimation, though more accurate, is computationally expensive. However, in this work, we show that hierarchical subtraction can achieve accurate results with a sufficient number of…

Hierarchical community detection via maximum entropy partitions and the renormalization group
Jorge Martinez Armas
https://arxiv.org/abs/2508.04034 https://

Hierarchical community detection via maximum entropy partitions and the renormalization group
Identifying meaningful structure across multiple scales remains a central challenge in network science. We introduce Hierarchical Clustering Entropy (HCE), a general and model-agnostic framework for detecting informative levels in hierarchical community structures. Unlike existing approaches, HCE operates directly on dendrograms without relying on edge-level statistics. It selects resolution levels that maximize a principled trade-off between the entropy of the community size distribution and t…

There are good days in software development and then there are days like these when your brain melts onto your desk and your soul shrivels after hours of video conference calls, communications proofreading, and, the horrors of all horrors, bash and powershell scripting. #npm

From MAS to MARS: Coordination Failures and Reasoning Trade-offs in Hierarchical Multi-Agent Robotic Systems within a Healthcare Scenario
Yuanchen Bai, Zijian Ding, Shaoyue Wen, Xiang Chang, Angelique Taylor
https://arxiv.org/abs/2508.04691

From MAS to MARS: Coordination Failures and Reasoning Trade-offs in Hierarchical Multi-Agent Robotic Systems within a Healthcare Scenario
Multi-agent robotic systems (MARS) build upon multi-agent systems by integrating physical and task-related constraints, increasing the complexity of action execution and agent coordination. However, despite the availability of advanced multi-agent frameworks, their real-world deployment on robots remains limited, hindering the advancement of MARS research in practice. To bridge this gap, we conducted two studies to investigate performance trade-offs of hierarchical multi-agent frameworks in a s…