research

A Universal Space of Brain Dynamics for Unveiling Cognitive Transitions and Individual Differences

arXiv:2605.02936v1 Announce Type: cross Abstract: Representing dynamical systems through data-driven universal spaces has proven effective; however, achieving this universality for human brain activit

Research

Actionable Real-Time Modeling of Surgical Team Dynamics via Time-Expanded Interaction Graphs

DGX agent

arXiv:2605.04169v1 Announce Type: cross Abstract: Surgical team performance arises from complex interactions between technical execution and non-technical skills, including communication and coordinat

researcharxiv-cs-lg

7 May 2026

Active Contact Sensing for Robust Robot-to-Human Object Handover

arXiv:2605.04610v1 Announce Type: new Abstract: Robot-to-human object handover is an essential skill for robot assistants, from serving drinks at home to passing surgical tools in the operating room.

researcharxiv-cs-ro

AdapShot: Adaptive Many-Shot In-Context Learning with Semantic-Aware KV Cache Reuse

arXiv:2605.03644v1 Announce Type: new Abstract: Many-Shot In-Context Learning (ICL) has emerged as a promising paradigm, leveraging extensive examples to unlock the reasoning potential of Large Langua

Adapt or Forget: Provable Tradeoffs Between Adam and SGD in Nonstationary Optimization

arXiv:2605.04269v1 Announce Type: cross Abstract: We provide a theoretical analysis of Adam under non-stationary stochastic objectives, separating two regimes: Euclidean tracking under adaptive strong

Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning

arXiv:2509.10784v3 Announce Type: replace-cross Abstract: Medical vision foundation models remain limited in downstream tasks, particularly volumetric medical image segmentation. While fine-tuning on

Adaptive Consensus in LLM Ensembles via Sequential Evidence Accumulation: Automatic Budget Identification and Calibrated Commit Signals

arXiv:2605.04236v1 Announce Type: new Abstract: Large Language Model ensembles improve reasoning accuracy up to a performance boundary; beyond it, additional deliberation degrades accuracy. Static-bud

Adaptive Inverted-Index Routing for Granular Mixtures-of-Experts

arXiv:2605.04952v1 Announce Type: new Abstract: Mixture-of-experts (MoE) models enable scalable transformer architectures by activating only a subset of experts per token. Recent evidence suggests tha

Adaptive Reorganization of Neural Pathways for Continual Learning with Spiking Neural Networks

arXiv:2309.09550v4 Announce Type: replace-cross Abstract: The human brain can self-organize rich and diverse sparse neural pathways to incrementally master hundreds of cognitive tasks. However, most e

Advancing Aesthetic Image Generation via Composition Transfer

arXiv:2605.04609v1 Announce Type: new Abstract: Composition is a cornerstone of visual aesthetics, influencing the appeal of an image. While its principles operate independently of specific content, i

Analogy between Boltzmann machines and Feynman path integrals

arXiv:2301.06217v1 Announce Type: cross Abstract: We provide a detailed exposition of the connections between Boltzmann machines commonly utilized in machine learning problems and the ideas already we

Are you with me? A Framework for Detecting Mental Model Discrepancies in Task-Based Team Dialogues

arXiv:2605.03149v1 Announce Type: new Abstract: Humans typically use natural language to update teammates on task states. Since not all updates are communicated, discrepancies arise between the team m

ArtiFixer: Enhancing and Extending 3D Reconstruction with Auto-Regressive Diffusion Models

arXiv:2603.00492v2 Announce Type: replace Abstract: Per-scene optimization methods such as 3D Gaussian Splatting provide state-of-the-art novel view synthesis quality but extrapolate poorly to under-o

Attention Sinks Induce Gradient Sinks: Massive Activations as Gradient Regulators in Transformers

arXiv:2603.17771v2 Announce Type: replace Abstract: Attention sinks and massive activations are recurring and closely related phenomena in Transformer models. Existing explanations have largely focuse

Average Attention Transformers and Arithmetic Circuits

arXiv:2605.04683v1 Announce Type: cross Abstract: We analyse the computational power of transformer encoders as sequence-to-sequence functions on vectors. We show that average hard attention can be us

Awaking Spatial Intelligence in Unified Multimodal Understanding and Generation

arXiv:2605.04128v1 Announce Type: cross Abstract: We present JoyAI-Image, a unified multimodal foundation model for visual understanding, text-to-image generation, and instruction-guided image editing

AxMoE: Characterizing the Impact of Approximate Multipliers on Mixture-of-Experts DNN Architectures

arXiv:2605.04754v1 Announce Type: new Abstract: Deep neural network (DNN) inference at the edge demands simultaneous improvements in accuracy, computational efficiency, and energy consumption. Approxi

Beyond Exponential Decay: Rethinking Error Accumulation in Large Language Models

arXiv:2505.24187v2 Announce Type: replace Abstract: The prevailing assumption of an exponential decay in large language model (LLM) reliability with sequence length, predicated on independent per-toke

Beyond Rigid Geometries: The Spline-Pullback Metric for Universal Diffeomorphic SPD Representation Learning

arXiv:2605.04406v1 Announce Type: new Abstract: The integration of Symmetric Positive Definite (SPD) matrices into deep learning has historically relied on fixed algebraic Riemannian metrics. Analogou

Beyond Variance: Prompt-Efficient RLVR via Rare-Event Amplification and Bidirectional Pairing

arXiv:2602.03452v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) is effective for training large language models on deterministic outcome reasoning tasks. Prio

Bilinear Mamba-Koopman Neural MPC for Varying Dynamics

arXiv:2605.04793v1 Announce Type: new Abstract: Koopman-based neural MPC models generate time-varying dynamics from historical data, but preserve convexity by enforcing that the system operator is ind

BOOOM: Loss-Function-Agnostic Black-Box Optimization over Orthonormal Manifolds for Machine Learning and Statistical Inference

arXiv:2605.04087v1 Announce Type: cross Abstract: Optimization over the Stiefel manifold St(p,d), the set of p imes d column-orthonormal matrices, is fundamental in statistics, machine learning, and s

Bridging Input Feature Spaces Towards Graph Foundation Models

arXiv:2605.04834v1 Announce Type: new Abstract: Unlike vision and language domains, graph learning lacks a shared input space, as input features differ across graph datasets not only in semantics, but

Bridging Modalities: Joint Synthesis and Registration Framework for Aligning Diffusion MRI with T1-Weighted Images

arXiv:2601.11689v2 Announce Type: replace-cross Abstract: Multimodal image registration between diffusion MRI (dMRI) and T1-weighted (T1w) MRI images is a critical step for aligning diffusion-weighted

Building informative materials datasets beyond targeted objectives

arXiv:2605.05104v1 Announce Type: cross Abstract: Materials science data collection can be expensive, making the reuse and long-term utility of datasets critical important for future discovery campaig

Calibrating Tabular Anomaly Detection via Optimal Transport

arXiv:2602.06810v2 Announce Type: replace Abstract: Tabular anomaly detection (TAD) remains challenging due to the heterogeneity of tabular data: features lack natural relationships, vary widely in di

Can AI Help You Get Over Your Breakup? One Session with a Belief-Reframing Chatbot Shows Sustained Distress Reduction

arXiv:2605.03261v1 Announce Type: cross Abstract: Romantic breakups are among the most common and intense sources of psychological distress. We evaluated *overit*, a single-session AI chatbot that use

Capabilities of Auto-encoders and Principal Component Analysis of the Reduction of Microstructural Images; Application on the Acceleration of Phase-Field Simulations

arXiv:2605.04229v1 Announce Type: new Abstract: In this work, a data-driven framework based on Phase-Field simulations data is proposed to highlight the capabilities of neural networks to ensure accur

CAR: Query-Guided Confidence-Aware Reranking for Retrieval-Augmented Generation

arXiv:2605.04495v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) depends on document ranking to provide useful evidence for generation, but conventional reranking methods mainly op

Cardiovascular disease classification using radiomics and geometric features from cardiac CT

arXiv:2506.22226v2 Announce Type: replace-cross Abstract: Automatic detection and classification of Cardiovascular disease (CVD) from Computed Tomography (CT) images play an important part in facilita

CAST: Mitigating Object Hallucination in Large Vision-Language Models via Caption-Guided Visual Attention Steering

arXiv:2605.04641v1 Announce Type: new Abstract: Although Large Vision-Language Models (LVLMs) have demonstrated remarkable performance on downstream tasks, they frequently produce contents that deviat

Centrality-Based Pruning for Efficient Echo State Networks

arXiv:2603.20684v2 Announce Type: replace Abstract: Echo State Networks (ESNs) are a reservoir computing framework widely used for nonlinear time-series prediction. However, despite their effectivenes

Combining Abstract Argumentation and Machine Learning for Efficiently Analyzing Low-Level Process Event Streams

arXiv:2505.05880v2 Announce Type: replace-cross Abstract: Monitoring and analyzing process traces is a critical task for modern companies and organizations. In scenarios where there is a gap between t

Computing Thiele Rules on Interval Elections and their Generalizations

arXiv:2605.03067v1 Announce Type: new Abstract: Approval-based committee voting has received significant attention in the social choice community. Among the studied rules, Thiele rules, and especially

Conditional outlier detection for clinical alerting

arXiv:2605.05124v1 Announce Type: new Abstract: We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management actions using past patient cases stored in an electr

Consensus Entropy: Harnessing Multi-VLM Agreement for Self-Verifying and Self-Improving OCR

arXiv:2504.11101v4 Announce Type: replace Abstract: Optical Character Recognition (OCR) is fundamental to Vision-Language Models (VLMs) and high-quality data generation for LLM training. Yet, despite

Contact Matrix: Enhancing Dance Motion Synthesis with Precise Interaction Modeling

arXiv:2605.04662v1 Announce Type: new Abstract: Generating realistic reactive motions, in which one person reacts to the fixed motions of others, is challenging due to strict interaction constraints a

Contextual Memory-Enhanced Source Coding for Low-SNR Communications

arXiv:2605.04400v1 Announce Type: cross Abstract: While Separate Source-Channel Coding (SSCC) retains the practical benefits of modular system design, its effectiveness in noisy text transmission is f

Continual Distillation of Teachers from Different Domains

arXiv:2605.04059v1 Announce Type: cross Abstract: Deep learning models continue to scale, with some requiring more storage than many large-scale datasets. Thus, we introduce a new paradigm: Continual

Continual Knowledge Updating in LLM Systems: Learning Through Multi-Timescale Memory Dynamics

arXiv:2605.05097v1 Announce Type: cross Abstract: LLMs are trained once, then deployed into a world that never stops changing. External memory compensates for this, but most systems manage it explicit

Critical Windows of Complexity Control: When Transformers Decide to Reason or Memorize

arXiv:2605.04396v1 Announce Type: new Abstract: Recent work has shown that Transformers' compositional generalization is governed by complexity control, initialization scale and weight decay, which st

Cross-Dataset Linkage of Brain MRI using Image Similarity Measures

arXiv:2602.10043v2 Announce Type: replace Abstract: Head magnetic resonance imaging (MRI) data are routinely collected and shared for research under strict regulatory frameworks that require the remov

Cross-Tokenizer Likelihood Scoring Algorithms for Language Model Distillation

arXiv:2512.14954v2 Announce Type: replace Abstract: Computing next-token likelihood ratios between two language models (LMs) is a standard task in training paradigms such as knowledge distillation. Si

Dataset-Driven Channel Masks in Transformers for Multivariate Time Series

arXiv:2410.23222v3 Announce Type: replace Abstract: Recent advancements in foundation models have been successfully extended to the time series (TS) domain, facilitated by the emergence of large-scale

Deco: Extending Personal Physical Objects into Pervasive AI Companion through a Dual-Embodiment Framework

arXiv:2605.03882v1 Announce Type: cross Abstract: Individuals frequently form deep attachments to physical objects (e.g., plush toys) that usually cannot sense or respond to their emotions. While AI c

Deepfake Audio Detection Using Self-supervised Fusion Representations

arXiv:2605.03420v1 Announce Type: cross Abstract: This paper describes a submission to the Environment-Aware Speech and Sound Deepfake Detection Challenge (ESDD2) 2026, which addresses component-level

DeFed-GMM-DaDiL: A Decentralized Federated Framework for Domain Adaptation

arXiv:2605.04324v1 Announce Type: new Abstract: Decentralized multi-source domain adaptation seeks to transfer knowledge from multiple heterogeneous and related source domains to an unlabeled target d

Demystifying Manifold Constraints in LLM Pre-training

arXiv:2605.04418v1 Announce Type: new Abstract: The empirical success of large language model (LLM) pre-training relies heavily on heuristic stabilization techniques, such as explicit normalization la