research

Compliant Sphere Lattice Contact: Distributed Contact Modeling for Sphere-Based Robot Representations

arXiv:2608.00263v1 Announce Type: new Abstract: Contact planning in robotics requires models that are both computationally efficient and physically accurate. Sphere-based robot representations satisfy

Research

Computational Approaches to Understanding Large Language Model Impact on Writing and Information Ecosystems

DGX agent

arXiv:2506.17467v2 Announce Type: replace Abstract: Large language models (LLMs) have shown significant potential to change how we write, communicate, and create, leading to rapid adoption across soci

researcharxiv-cs-cl

4 Aug 2026

Conformalized Large Language Models under Configuration Shift

arXiv:2608.01460v1 Announce Type: new Abstract: Conformal prediction (CP) is a distribution-free framework for uncertainty quantification that has recently been adapted to large language models (LLMs)

Constructing Parallel Multidimensional Chromatic Lexicons for Corpus-Assisted Analysis of Russian and English Texts

arXiv:2608.01752v1 Announce Type: new Abstract: This article addresses the relative scarcity of research tools for the corpus-assisted linguistic analysis of colour terms in literary texts. It describ

Context-Aware Mixture of Domain Experts for Bodily Expression of Emotion in the Wild

arXiv:2608.02331v1 Announce Type: new Abstract: The same body posture can convey entirely different emotions depending on its surrounding context, yet most methods for recognising bodily emotions trea

Contextual Semantic Relevance Tracks fMRI BOLD Responses During Naturalistic Speech Comprehension

arXiv:2607.15856v2 Announce Type: replace Abstract: Naturalistic language comprehension requires listeners to process both local probabilistic expectations and contextual semantic relations. This stud

CopyCat: Improving Fine-Grained Subject Consistency in Subject-to-Image Models within Seconds

arXiv:2608.00674v1 Announce Type: new Abstract: Recent subject-to-image models have achieved impressive progress in personalized image generation, yet they still struggle to preserve fine-grained subj

CoRe-GNN: Multilevel Message passing on Coarsened graphs

arXiv:2608.02128v1 Announce Type: new Abstract: Training Graph Neural Networks on large graphs is challenged by the memory cost of storing all node representations across layers. We show that several

CountTRuCoLa: Rule Learning for Interpretable Temporal Knowledge Graph Forecasting

arXiv:2509.09474v2 Announce Type: replace Abstract: We address the task of temporal knowledge graph forecasting with an inherently interpretable method based on symbolic rules. Motivated by recent wor

Credit the Right Box: Marginal Contribution Assignment for Structured Visual Perception

arXiv:2608.01055v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) are increasingly expected to solve structured perception tasks that require visual recognition, language-to-obj

Cross-Task Dissociation in Frontier Vision-Language Model Theory of Mind

arXiv:2608.00261v1 Announce Type: new Abstract: Do frontier vision-language models present a coherent Theory-of-Mind (ToM) profile across tasks, matching the same human reference group, or does that p

Cultural Awareness is Represented but Not Decoded: Tracing Mythological Knowledge across 18 Open-Source LLMs

arXiv:2608.02486v1 Announce Type: new Abstract: Open-source LLMs reliably name Zeus, Jupiter, and Thor, but recover their counterparts in less-represented traditions like Finnish, Slavic, Egyptian, or

D^2-4DGS: Dual-Depth Guided Sparse-Camera 4D Gaussian Splatting

arXiv:2608.01588v1 Announce Type: new Abstract: Dynamic 4D Gaussian Splatting has emerged as an efficient representation for dynamic novel view synthesis through explicit scene modeling and real-time

DART: Decoded Attention over Recurrent States for Efficient Long-Context Sequence Modeling

arXiv:2608.02032v1 Announce Type: new Abstract: Modern language models are built primarily from Transformers, recurrent models, and their hybrid architectures. Transformers rely on token-level attenti

Decisions over Sequences: Computability and Choice

arXiv:2203.00070v3 Announce Type: replace-cross Abstract: We develop a framework to study situations where decision makers face alternatives sequentially. Within this framework, we focus on endogenous

Decoding Children's Gait Behavior

arXiv:2608.00371v1 Announce Type: new Abstract: We introduce a new problem domain for human action recognition: the fine-grained analysis of children's gait behaviors from standard RGB video. We speci

Deep Learning-Based Estimation of Ground Reaction Forces in Parkinsonian Gait Using an Optimized Set of IMU Data

arXiv:2608.02408v1 Announce Type: new Abstract: Accurate gait analysis in Parkinson's disease (PD) typically relies on laboratory-based systems to capture biomechanical data, such as ground reaction f

DeepConvContext: A Multi-Scale Approach to Timeseries Classification in Human Activity Recognition

arXiv:2505.20894v2 Announce Type: replace Abstract: Despite recognized limitations in modeling long-range temporal dependencies, Human Activity Recognition (HAR) has traditionally relied on a sliding

DeGS: A Scalable 3DGS Architecture via Decoupled Workload Parsing and Reorganization

arXiv:2608.02099v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) has emerged as a leading technique for real-time novel view synthesis, yet existing 3DGS accelerators suffer from poor ar

Deja Cue: Localizing States in Object Histories via Vocabulary-Relative Coordinates

arXiv:2608.02044v1 Announce Type: new Abstract: Tracking links observations of the same object through visual change, yet cannot by itself determine when the object is empty or filled, intact or cut.

Detecting Nonproperness of Likelihood Equations

arXiv:2608.01976v1 Announce Type: cross Abstract: Given an algebraic statistical model, a challenging problem is classifying the data according to the number of positive critical points of the likelih

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation

arXiv:2608.01343v1 Announce Type: new Abstract: The emergence of transformer-based deep learning models has brought unprecedented performance across various domains, particularly in natural language p

Diagnosing Under-Development of Irreversible Processes in Video Generation

arXiv:2608.00617v1 Announce Type: new Abstract: Many physical attributes are irreversible: ice melts but does not re-freeze, paper chars but does not un-burn. Do video generators respect this? We show

Differentiable Lifting for Topological Neural Networks

arXiv:2608.01160v1 Announce Type: new Abstract: Topological neural networks (TNNs) enable leveraging high-order structures on graphs (e.g., cycles and cliques) to boost the expressive power of message

Discriminative Axis, Not Data Volume: What a Contrastive Corpus Teaches an Audio Embedding

arXiv:2608.01560v1 Announce Type: new Abstract: Scaling the corpus is the default remedy when a contrastive representation lacks an attribute. We report a case where it does nothing, and identify what

Distill What RGB Can Recover: Privileged 3D Evidence for RGB-Only Vision-Language Models

arXiv:2608.00110v1 Announce Type: new Abstract: 3D scene understanding requires reasoning about entity existence, spatial layout, and object relations, yet RGB images alone often provide insufficient

Distilling Drifting Transformers with Representation Autoencoders

arXiv:2606.15553v2 Announce Type: replace Abstract: Despite the significant training acceleration and promising performance, Representation Autoencoders (RAEs) are mainly criticized for poor distillat

Distributional Matching for Vector Quantization: A Unified Theoretical and Empirical Framework

arXiv:2607.15933v2 Announce Type: replace Abstract: The effectiveness of modern visual representation learning and autoregressive models critically depends on vector quantization (VQ), which discretiz

Do Neural Networks Really Beat the Curse of Dimensionality? A Bit-Complexity View

arXiv:2608.01357v1 Announce Type: new Abstract: Traditional approximation theory measures convergence rates in terms of the number of parameters or degrees of freedom. However, practical computation o

DODA: A Database of Datasets for Aesthetics Research

arXiv:2608.00089v1 Announce Type: new Abstract: With rapid growth in the fields of empirical and computational aesthetics we have seen a vast increase in large image datasets annotated for aesthetics.

Does Accuracy Equal Evidence? Reasoning Faithfulness under KV Cache Compression

arXiv:2608.01631v1 Announce Type: new Abstract: KV cache compression is commonly evaluated by final-answer accuracy, implicitly assuming that preserving the answer also preserves the reasoning that su

Dominant Arm Identification with Mixing and Recycling Observed Samples

arXiv:2608.01545v1 Announce Type: cross Abstract: We study the problem of identifying the dominant arm in multi-armed bandits, where the objective is to find the action with the highest probability of

DreamTraj: Generating 6-DoF Object Trajectories by Reading Unrendered Video Diffusion Latents

arXiv:2608.00486v1 Announce Type: new Abstract: Accurate prediction of object trajectories during manipulation is essential for closing the perception-action loop. Progress is limited on two fronts: a

Dynamic UAV-based search operations using probabilistic diffusion modeling of Man Overboard incident victims

arXiv:2608.02093v1 Announce Type: new Abstract: More than 70% of the people that fell overboard cruise ships in the period 2010-2019 lost their lives. This paper presents a strategy for reliably predi

Ego2Robot: Scalable Robot Data Synthesis from Egocentric Human Data

arXiv:2608.02580v1 Announce Type: new Abstract: Learning generalizable robot manipulation policies requires large-scale and diverse demonstration data. Egocentric human manipulation videos offer rich

EHR2Path: Comprehensive Pathway-Level Modeling of Longitudinal Patient Trajectories from Multimodal Electronic Health Records

arXiv:2506.04831v3 Announce Type: replace-cross Abstract: Forecasting how a patient's condition is likely to evolve, including possible deterioration, recovery, treatment needs, and care transitions,

Eigenvalues as a Metric for Memory Dynamics in Sequence Models

arXiv:2510.09379v2 Announce Type: replace Abstract: While softmax attention drives state-of-the-art performance in sequence modeling, its quadratic complexity motivates linear alternatives such as sta

ELECTRIC: Evidential Learning-Enhanced CT Reconstruction via Iterative Correction

arXiv:2608.00060v1 Announce Type: new Abstract: Here we introduce ELECTRIC (Evidential Learning-Enhanced CT Reconstruction via Iterative Correction), a physics-guided Bayesian formulation. An evidenti

Embodied Passive Aeroacoustic Perception Enables Relative Sensing and Pursuit Between Aerial Robots

arXiv:2608.00401v1 Announce Type: new Abstract: Aerial robots generate structured aeroacoustic fields during flight, yet these signals have been underexplored as a source of onboard relative perceptio

EmoScene: A Dual-space Dataset for Controllable Affective Image Generation

arXiv:2604.00933v2 Announce Type: replace Abstract: Text-to-image diffusion models achieve high visual fidelity, yet fine-grained affective control remains difficult because textual emotion cues often

Empirical investigation of 3D CT Foundation Models and Unsupervised Adaptation for Head and Neck Cancer Recurrence Prediction

arXiv:2608.00071v1 Announce Type: new Abstract: The rapid emergence of 3D CT foundation models has opened new avenues for predictive modeling from CT imaging, offering a compelling alternative to trad

Enhancing Large Language Model Reasoning with Reward Models: An Analytical Survey

arXiv:2510.01925v3 Announce Type: replace Abstract: Reward models (RMs) play a critical role in enhancing the reasoning performance of LLMs. For example, they can provide training signals to finetune

Enriched text-guided variational multimodal knowledge distillation network (VMD) for automated diagnosis of plaque vulnerability in 3D carotid artery MRI

arXiv:2509.11924v2 Announce Type: cross Abstract: Multimodal learning has attracted much attention in recent years due to its ability to effectively utilize data features from a variety of different m

Environmental resilience via morphological diversity within machines

arXiv:2608.02395v1 Announce Type: new Abstract: Organisms contain diverse, sensorimotor parts across size scales and rapidly adapt to new environments, while machines contain only inert materials at s

EOVSAM: Efficient Open-Vocabulary Segmentation with SAM 3 in One Pass

arXiv:2608.02284v1 Announce Type: new Abstract: Open-vocabulary segmentation identifies and segments objects from arbitrary textual descriptions. SAM 3 supports noun-phrase-guided segmentation and ach

Estimating SSIM from MSE for DCT-Based Compressed Images

arXiv:2608.02549v1 Announce Type: cross Abstract: Efficient and perceptually meaningful quality assessment is a fundamental requirement for image and video processing, compression, and streaming syste

Estimating Treatment Effects in Networks under Unknown Exposure Mappings

arXiv:2510.21457v2 Announce Type: replace Abstract: Estimating heterogeneous treatment effects in network settings is complicated by interference, meaning that the outcome of an instance can be influe

Explainable Hybrid Feature Selection for Intrusion Detection in Internet of Medical Things Environments

arXiv:2608.00869v1 Announce Type: cross Abstract: Internet of Medical Things (IoMT) networks are hard to protect: devices are heterogeneous, computing resources are scarce, and traffic must be analyze