Search: “research”

Charting the Growth of Social-Physical HRI (spHRI): A Systematic Review Pipeline Augmented by Small Language Models

arXiv:2606.26382v1 Announce Type: cross Abstract: Social-physical human-robot interaction (spHRI) has grown rapidly across robotics, human-computer interaction, human-robot interaction, and haptics. Y

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

Clinical Harness for Governable Medical AI Skill Ecosystems

DGX agent

arXiv:2606.26494v1 Announce Type: new Abstract: Medical AI remains organized around isolated models, whereas clinical care requires accountable capabilities that persist across time. We propose clinic

researcharxiv-cs-ai

26 Jun 2026

Coarse-to-Fine: A Hybrid Self-Supervised Method for Non-rigid 3D Shape Matching

arXiv:2606.26557v1 Announce Type: new Abstract: Non-rigid 3D shape matching is a fundamental task in computer vision and graphics. In this paper, we propose a hybrid self-supervised method based on a

Code evolution for link prediction in complex networks

arXiv:2606.26132v1 Announce Type: cross Abstract: The problem of predicting links in complex networks appears in different disciplines and has led to a variety of ingenious human-designed methods. We

Compiler-Driven Approximation Tuning for Hyperdimensional Computing

arXiv:2606.26547v1 Announce Type: cross Abstract: As Moore's law reaches its physical and economic limits, domain-specific approaches are increasingly employed to accelerate machine learning workloads

Compositionality and the lexicon in evolutionary semantics

arXiv:2606.27228v1 Announce Type: new Abstract: Formal semantics has shown that sentence meanings arise by recursively composing lexical meanings, yet much of the literature on semantic universals mod

Computationally-efficient Graph Modeling with Refined Graph Random Features

arXiv:2510.07716v2 Announce Type: replace Abstract: We propose refined GRFs (GRFs++), a new class of Graph Random Features (GRFs) for efficient and accurate computations involving kernels defined on t

Computer Vision for MOBA Analytics: A Dataset and Baseline for Visibility Analysis in Dota 2

arXiv:2606.26970v1 Announce Type: new Abstract: Introduction: Most Multiplayer Online Battle Arena (MOBA) analytics studies rely on structured data, which does not directly capture what each team coul

Consistent Distributed Ranking of Generative Models via Kernel Distances

arXiv:2310.11714v5 Announce Type: replace Abstract: Ranking generative models based on the fidelity and diversity of their outputs is required to identify the best generator in a group of candidate ge

Controllable Egocentric Video Generation via Occlusion-Aware Sparse 3D Hand Joints

arXiv:2603.11755v2 Announce Type: replace Abstract: Controllable video generation for complex hand-object interactions is a critical step toward building visual world models. However, existing methods

DanceDuo: Bridging Human Movement and AI Choreography

arXiv:2606.26507v1 Announce Type: cross Abstract: In recent years, advancements in deep learning and generative models have revolutionized music-driven dance generation. This paper introduces a novel

Dataset Usage Inference without Shadow Models or Held-out Data

arXiv:2606.26257v1 Announce Type: new Abstract: How much of my data was used to train a machine learning model? Dataset Usage Inference (DUI) aims to answer this by estimating what fraction of a datas

Delegation and Verification Under AI

arXiv:2603.02961v2 Announce Type: replace-cross Abstract: As AI systems enter institutional workflows, workers must decide whether to delegate task execution to AI and how much effort to invest in ver

Detecting and Controlling Sycophancy with Cascading Linear Features

arXiv:2606.26155v1 Announce Type: new Abstract: Interpreting and controlling model behaviors through activation steering methods requires many pairs of contrastive samples that clearly exhibit desired

DinoLink: A Token-Centric Representation Compression Framework for Bandwidth-Constrained Collaborative V2X Perception

arXiv:2606.26398v1 Announce Type: new Abstract: High-precision remote perception is often hindered by the severe bandwidth constraints of Vehicle-to-Everything (V2X) networks. We propose extit{DinoLin

DMuon: Efficient Distributed Muon Training with Near-Adam Overhead

arXiv:2606.27153v1 Announce Type: cross Abstract: Matrix-orthogonalization-based optimizers, exemplified by Muon, have demonstrated strong convergence behavior across a wide range of modern deep learn

DnA: Denoising Attention for Visual Tasks

arXiv:2606.27372v1 Announce Type: new Abstract: The softmax activation in multihead attention (MHA) is the de facto standard for attention-based models in visual perception tasks. However, standard so

Does Aurora Encode Atmospheric Structure? Latent Regime Analysis and Attribution

arXiv:2606.26361v1 Announce Type: new Abstract: ML foundation models are able to emulate atmospheric dynamics accurately and efficiently but operate as opaque ``black boxes''. We investigate the inter

DroidBreaker: Practical and Functional Problem-Space Attacks on Machine-Learning Android Malware Detectors

arXiv:2606.26707v1 Announce Type: cross Abstract: Adversarial APKs are Android applications modified in the problem space to evade machine-learning malware detectors. In this work, we first show that,

Dynamic-dLLM: Dynamic Cache-Budget and Adaptive Parallel Decoding for Training-Free Acceleration of Diffusion LLM

arXiv:2606.26120v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) offer a promising alternative to autoregressive models, excelling in text generation tasks due to their bidirect

Efficient foundation decoders for fault-tolerant quantum computing

arXiv:2606.27119v1 Announce Type: cross Abstract: Foundation decoders, a class of high-capacity neural decoders, are leading candidates for fault-tolerant quantum computing, with accurate and efficien

Efficient learning of bosonic Gaussian unitaries

arXiv:2510.05531v2 Announce Type: replace-cross Abstract: Bosonic Gaussian unitaries are fundamental building blocks of central continuous-variable quantum technologies such as quantum-optic interfero

Einstein World Models

arXiv:2606.26969v1 Announce Type: new Abstract: Does intelligence require the ability to reason about phenomena beyond direct experience? It is natural to suspect that some complex thought cannot be c

Embedding Foundation Model Predictions in Discrete-Choice Models with Structural Guarantees

arXiv:2606.26432v1 Announce Type: new Abstract: Tabular foundation models achieve strong accuracy on choice prediction tasks, but their predictions often violate the economic logic those tasks require

Enabling self-supervised learned primal dual with Noise2Inverse

arXiv:2606.26991v1 Announce Type: cross Abstract: X-ray computed tomography reconstruction is an ill-posed inverse problem, particularly in low-dose and sparse-angle settings where measurements are no

Erase-then-Delta Attention: Decoupling Erase and Write Addresses in Delta-Rule Linear Attention

arXiv:2606.26560v1 Announce Type: new Abstract: Delta-rule linear attention improves recurrent memory updates by correcting what is already stored at the current write address before writing new conte

Event-Aware Instructed Assistant for Referring Video Segmentation

arXiv:2606.26994v1 Announce Type: cross Abstract: Existing referring video segmentation methods often treat a video as a single event consisting of multiple images, overlooking the fact that a video t

Exact and Deterministic Patch Descriptor Retrieval via Hierarchical Normalization

arXiv:2606.27280v1 Announce Type: new Abstract: We present a patch descriptor retrieval method that returns the exact nearest neighbour -- provably identical to exhaustive full-vector search -- while

Explainable Ensemble-Based Machine Learning Models for Detecting the Presence of Cirrhosis in Hepatitis C Patients

arXiv:2606.26561v1 Announce Type: new Abstract: Hepatitis C is a liver infection caused by a virus, which results in mild to severe inflammation of the liver. Over many years, hepatitis C gradually da

Exploring the Intrinsic Geometry of Diffusion Models with Constrained Inverse Kinematics

arXiv:2606.26408v1 Announce Type: new Abstract: Recent studies suggest that diffusion models can recover geometric structure in the data manifolds they are trained on, yet the supporting evidence has

researcharxiv-cs-ro

Extracting Neural Materials from Multi-view Images

arXiv:2606.26715v1 Announce Type: new Abstract: Neural materials can represent complex specular reflections and scattering effects in a compact, universal basis. However, acquiring and authoring such

FBK's Long-form SpeechLLMs for IWSLT 2026 Instruction Following

arXiv:2606.26819v1 Announce Type: new Abstract: This paper describes our submission to the IWSLT 2026 Instruction Following shared task. SpeechLLMs are developed for both short-form and long-form spee

Finding Stationary Points by Comparisons

arXiv:2606.27082v1 Announce Type: new Abstract: We study the problem of finding stationary points of non-convex functions when access to the objective is provided only through a comparison oracle that

Focusing on What Matters: Saliency-Harnessing Accurate Routing for Diffusion MoE

arXiv:2606.26938v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures have emerged as a powerful paradigm for scaling diffusion models in visual generation. Recent advancements have f

FracEvent: Event-Camera Simulation via Fractional-Relaxation Pixel Dynamics

arXiv:2606.26636v1 Announce Type: new Abstract: Event cameras asynchronously report brightness changes with microsecond-level temporal resolution, but real event data remain difficult to collect at sc

From Vajrayana Tara to Bengali Baul: A Computational Study of Lexical Transmission Across Buddhist, Shakta, and Vaishnava Traditions in Bengal

arXiv:2606.26803v1 Announce Type: new Abstract: We present a computational corpus study of vocabulary relationships across eight tradition layers of Bengali and Sanskrit devotional literature spanning

fTNN: a tensor neural network for fractional PDEs

arXiv:2606.27140v1 Announce Type: new Abstract: We develop the fTNN, a deterministic tensor neural network subspace method for problems involving the fractional Laplacian on bounded domains, taking th

Full spectrum Unlearnable Examples via Spectral Equalization

arXiv:2606.26719v1 Announce Type: new Abstract: Unlearnable examples (UEs) protect training data by injecting imperceptible perturbations so that models fail to extract exploitable representations. In

Generative Models on Analog Hardware with Dynamics

arXiv:2606.27294v1 Announce Type: cross Abstract: Analog hardware platforms such as coupled oscillators and Analog Ising Machines naturally solve differential equations at a fraction of the energy cos

Geometric Gradient Rectification for Safe Open-Set Semi-Supervised Learning

arXiv:2606.26973v1 Announce Type: new Abstract: Open-set semi-supervised learning aims to leverage unlabeled data that may contain out-of-distribution outliers while maintaining performance on in-dist

Geometry-Aware MCTS for Extremal Problems in Combinatorial Geometry

arXiv:2606.26399v1 Announce Type: new Abstract: We study certain extremal problems in combinatorial geometry that ask about configurations of points in an n imes n grid that satisfy strict, global geo

Geometry-Aware Superpixel Graph Transformer with Metadata for Skin Lesion Classification

arXiv:2606.20390v2 Announce Type: replace Abstract: Automated skin cancer classification from dermoscopic images remains challenging due to heterogeneous lesion structure, strong intra-class variabili

Gradient Testing and Estimation by Comparisons

arXiv:2405.11454v3 Announce Type: replace Abstract: We study gradient testing and gradient estimation of smooth functions using only a comparison oracle that, given two points, indicates which one has

Heavy-Ball Q-Learning with Residual Weighting Correction

arXiv:2606.27112v1 Announce Type: cross Abstract: This paper proposes a corrected heavy-ball Q-learning method for reinforcement learning (RL) and establishes its convergence. It also identifies condi

Huracan: A skillful end-to-end data-driven system for ensemble data assimilation and weather prediction

arXiv:2508.18486v2 Announce Type: replace-cross Abstract: Over the past few years, machine learning-based data-driven weather prediction has been transforming operational weather forecasting by provid

Interpreting 'Interpretability' and Explaining 'Explainability' in Machine Learning in Physics

arXiv:2606.26228v1 Announce Type: cross Abstract: We review the concepts of interpretability and explainability as they apply to machine learning in physics. We define interpretability as concerning t

Intracranial Aneurysm Classification and Segmentation via Tri-Axial ROI and Multi-Task Learning

arXiv:2606.26706v1 Announce Type: new Abstract: Intracranial aneurysms are often asymptomatic until rupture, which carries high mortality. Rupture risk assessment and treatment planning depend on both

Inverse Design of Compact and Wideband Inverted Doherty Power Amplifiers Using Deep Learning

arXiv:2606.27002v1 Announce Type: cross Abstract: This paper presents a deep learning-assisted methodology for the inverse synthesis of a compact, wideband inverted Doherty power amplifier (PA). Convo