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InteractiveAvatar: Real-Time Streaming Video Generation for Consistent and Intent-Aware Avatars

arXiv:2606.22905v1 Announce Type: new Abstract: Recent diffusion-based models have enabled realistic audio-driven avatar generation in real-time streaming. However, existing approaches struggle to mai

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Interest Entanglement: The Hidden Barrier to Blind Super-Resolution Optimization

DGX agent

arXiv:2606.22353v1 Announce Type: new Abstract: Fidelity and perceptual quality are two inherently competing and conflicting objectives in the image super-resolution (SR) task. Different loss function

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23 Jun 2026

Interleaved Speech Language Models Latently Work In Text

arXiv:2606.22473v1 Announce Type: cross Abstract: Speech language models (SLMs) have been extensively studied, with the common paradigm incorporating text data and pre-trained text LMs. A leading appr

Interpretable Kolmogorov-Arnold Network with Feature-Isolated Temporal Attention Mechanism for Electricity Load Forecasting

arXiv:2606.23425v1 Announce Type: new Abstract: Accurate electricity load forecasting is a crucial prerequisite for stable power system operations. While prevalent deep learning models present competi

Interpretable machine learning of halo gas density profiles: a sensitivity analysis of cosmological hydrodynamical simulations

arXiv:2512.09021v3 Announce Type: replace-cross Abstract: Stellar and AGN-driven feedback processes affect the distribution of gas on a wide range of scales, from within galaxies well into the interga

Interpretable Uncertainty Routing Separating Emotion Ambiguity from Distribution Shift in Facial Expression Recognition

arXiv:2606.22725v1 Announce Type: new Abstract: Facial expression recognition (FER) is inherently ambiguous: human annotators frequently disagree, and models deployed in real environments face distrib

Intrinsic Flow Matching on Quantum Pure-State Manifolds with Phase-Aligned Transport

arXiv:2606.21256v1 Announce Type: new Abstract: Quantum pure-state ensembles live on complex projective space, making flat Euclidean generative modeling geometrically mismatched. We introduce Intrinsi

Is Oracle Pruning the True Oracle?

arXiv:2412.00143v2 Announce Type: replace-cross Abstract: Oracle pruning, which selects unimportant weights by minimizing the pruned train loss, has served as the foundation for most neural network pr

Iterative Diffusion-Refined Neural Attenuation Fields for Multi-Source Stationary CT Reconstruction: NAF Meets Diffusion Model

arXiv:2511.14310v2 Announce Type: replace Abstract: Multi-source stationary computed tomography (CT) has recently attracted attention for its ability to achieve rapid image reconstruction, making it s

Jacobian-Adaptive Weighting for Stability: Enhancing Long-term Rollout of Neural Partial Differential Equation Solvers via Spatially-Adaptive Regularization

arXiv:2603.05538v3 Announce Type: replace Abstract: Data-driven surrogate models can significantly accelerate the simulation of continuous dynamical systems, yet the step-wise accumulation of errors d

Jacobian-Aware Posterior Sampling for Inverse Problems

arXiv:2511.18471v3 Announce Type: replace Abstract: Diffusion models provide powerful generative priors for solving inverse problems by sampling from a posterior distribution conditioned on corrupted

Kernel of Partition Paths: A Unified Representation for Tree Ensembles

arXiv:2606.18853v2 Announce Type: replace-cross Abstract: A recent line of work has reframed individual decision trees as linear models on engineered features associated with their splits, opening rou

Kiwano: A Cutting-Edge Open-Source Toolkit for Speaker Verification

arXiv:2606.22369v1 Announce Type: cross Abstract: In this paper, we present Kiwano, an open-source toolkit designed to advance research and evaluation for speaker verification. Kiwano provides a light

Kolmogorov-Arnold Reservoir Computing

arXiv:2606.19984v2 Announce Type: replace Abstract: Reservoir computing offers a lightweight framework for forecasting dynamical systems but may struggle to capture long-range dependencies due to limi

Ky Fan Norms and Beyond: Dual Norms and Combinations for Matrix Optimization

arXiv:2512.09678v2 Announce Type: replace-cross Abstract: In this article, we explore the use of various matrix norms for optimizing functions of weight matrices, a crucial problem in deep learning. M

Lane Change Intention Prediction of two distinct Populations using a Transformer

arXiv:2509.06529v2 Announce Type: replace Abstract: In complex traffic scenarios, intention prediction of surrounding vehicles can improve the strategy of automated driving functions. Existing work on

Leaderless Collective Motion in Affine Formation Control over the Complex Plane

arXiv:2604.05648v2 Announce Type: replace Abstract: We propose a method for the collective maneuvering of affine formations in the plane by modifying the original weights of the Laplacian matrix used

Learning Adaptive Dynamical Features via Multi-au Liquid-Mamba for All-in-one Image Restoration

arXiv:2606.22801v1 Announce Type: new Abstract: Image restoration aims to recover high-quality images from degraded observations. Recent Mamba-based image restoration models have demonstrated strong p

Learning-Based Modeling of Soft Robots via Cosserat Rod Theory

arXiv:2606.20958v1 Announce Type: new Abstract: Modeling soft robot dynamics is challenging due to their continuum structure and typically nonlinear dynamics. Creating models based on first-order prin

Learning Chern Numbers of Topological Insulators with Gauge Equivariant Neural Networks

arXiv:2502.15376v2 Announce Type: replace Abstract: Equivariant network architectures are a well-established tool for predicting invariant or equivariant quantities. However, almost all learning probl

Learning Cross-View Semantic Priors for Single-Reference Unseen Object Pose Estimation

arXiv:2606.22076v1 Announce Type: new Abstract: Single-reference unseen object 6D pose estimation reduces object onboarding by estimating poses of arbitrary novel objects from only one reference view.

Learning Entropy Signature for Image Representation and Classification

arXiv:2606.22634v1 Announce Type: new Abstract: Learning Entropy (LE) has recently been extended to image analysis through Spatial Learning Entropy Maps (SLEMs), which are two-dimensional LE distribut

Learning Expressive Random Feature Models via Parametrized Activations

arXiv:2411.19468v4 Announce Type: replace Abstract: The random feature (RF) method is a powerful kernel approximation technique, but it typically uses fixed activation functions, limiting its adaptabi

Learning Graphs through Continuous Information Entropy Fields

arXiv:2606.22895v1 Announce Type: new Abstract: Graph theory is inherently descriptive, capturing what relationships exist but not why they arise, because it treats edges as primitive constructs. This

Learning Stable Canonical Worlds for Novel View Synthesis and Beyond

arXiv:2606.23027v1 Announce Type: new Abstract: Feed-forward Gaussian splatting (FFGS) facilitates real-time novel view synthesis, yet current methods often remain tied to view-dependent predictions.

Learning thermodynamic master equations for open quantum systems

arXiv:2506.01882v3 Announce Type: replace-cross Abstract: The characterization of Hamiltonians and other components of open quantum dynamical systems plays a crucial role in quantum computing and othe

Learning with Multiple Correct Answers -- Regret Bounds under Different Feedback Models

arXiv:2602.09402v2 Announce Type: replace Abstract: We study the problem of learning with multiple correct answers, where each instance admits a set of valid labels. We primarily focus on the online s

Leveraging Similarities in Multi-Armed Bandits

arXiv:2606.23414v1 Announce Type: new Abstract: In many online learning and bandit problems, the actions we consider possess inherent similarities--for instance because they share latent traits, tags,

LIG: Layer-wise Integrated Gradients for Within-Layer Flow Analysis in Transformers

arXiv:2606.21564v1 Announce Type: new Abstract: Transformers achieve strong performance, but their internal computations remain opaque. We view each Transformer layer as a dynamic graph whose nodes ar

Lighting-Consistent Object Transfer Across Radiance Fields

arXiv:2606.22481v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) is widely used to capture and render real scenes. Compositing objects from one capture into another has applications in m

LightSTAR: Efficient Visual Document Retrieval via Lightweight Selection with Vision-Adaptive Refinement

arXiv:2606.23539v1 Announce Type: new Abstract: Visual document retrieval requires rapidly locating relevant pages from large multi-modal corpora in response to user queries. While recent methods powe

Lightweight 3D Feature Pretraining by Bayesian Inversion of 2D Foundation Models

arXiv:2606.21292v1 Announce Type: new Abstract: We present Casper3D, a lightweight probabilistic framework for converting noisy multi-view 2D foundation-model embeddings into a latent 3D semantic repr

Lightweight Neural Framework for Robust 3D Volume and Surface Estimation from Multi-View Images

arXiv:2606.23653v1 Announce Type: new Abstract: Accurate volume and surface area estimation is critical for diverse applications, from marine ecology to medical diagnostics. However, existing methods

LK Jam: System Architecture and Implementation of a Real-Time Human-AI Interactive Music Generation System using Role-Aware GRU

arXiv:2606.21018v1 Announce Type: cross Abstract: As artificial intelligence advances into the era of Embodied AI, live musical interaction urgently needs to break free from the limitations of offline

LSTM Variants for Chaotic Dynamical Systems: An Empirical Study on the Lorenz Attractor

arXiv:2606.22662v1 Announce Type: new Abstract: Forecasting chaotic dynamical systems such as the Lorenz attractor is notoriously difficult: small numerical errors are amplified exponentially over lon

LVQAC: Lattice Vector Quantization Coupled with Spatially Adaptive Companding for Efficient Learned Image Compression

arXiv:2304.12319v2 Announce Type: replace-cross Abstract: Recently, numerous end-to-end optimized image compression neural networks have been developed and proved themselves as leaders in rate-distort

Machine Learning Classification of Cryopathy Syndromes: A Comprehensive Comparative Study

arXiv:2606.20874v1 Announce Type: new Abstract: Cryopathy syndromes are difficult to classify because laboratory patterns often overlap across diagnostic categories, while some diagnoses are rare. Thi

Manipulider: A Multi-Engine Buoyancy-Controlled Robot for Thrusterless Underwater Gliding and Manipulation

arXiv:2606.21461v1 Announce Type: new Abstract: The Manipulider is a buoyancy-actuated underwater robot that enables thrusterless, glide-like locomotion and attitude-based manipulation, while providin

Maps are the primary force that shapes the territory they picture.

researchfrancois-chollet--x

This post likely explores the concept that maps don't merely represent territory passively, but actively influence how we perceive, organize, and interact with physical space—a reversal of the traditi

Market efficiency is a myth. Markets are made of people, who are irrational both individually and in aggregate. Maybe if human irrationality…

researchfrancois-chollet--x

Market efficiency is a myth. Markets are made of people, who are irrational both individually and in aggregate. Maybe if human irrationality were completely random, it might average out to a rational

MaRS: Robust Out-of-Distribution Detection via Mahalanobis Residual Scoring

arXiv:2606.22649v1 Announce Type: new Abstract: Foundation models provide highly descriptive representations for medical images, yet their reliability degrades under distribution shifts arising from c

Massive Activations Are Architecturally Robust: A Controlled Scratch/Commitment Residual Stream Test

arXiv:2606.20743v1 Announce Type: new Abstract: Trained transformers reliably develop massive activations, a small number of hidden dimensions whose magnitude is far above the median and which concent

MeGAS: Thermomechanical Dynamic Gaussian Splatting for Thermophysical Scene Editing

arXiv:2606.23455v1 Announce Type: new Abstract: Recent advances integrate physically grounded Newtonian dynamics with neural rendering frameworks, narrowing the gap between photorealistic scene recons

Membrane-based Acoustic Microrobots

arXiv:2606.21047v1 Announce Type: new Abstract: Acoustic microrobots have emerged as a promising frontier for targeted drug delivery and minimally invasive medicine due to their high-power density and

Memory Is No Longer a Bottleneck: Memory-Efficient Graph Filtering for Scalable Collaborative Filtering

arXiv:2606.21540v1 Announce Type: new Abstract: Graph convolutional networks (GCNs) have demonstrated significant success in capturing complex user-item relationships for collaborative filtering (CF).

MeshFlow: Mesh Generation with Equivariant Flow Matching

arXiv:2606.23489v1 Announce Type: cross Abstract: Meshes are among the most common 3D scene representations, but directly generating meshes is challenging because the representation contains important

Metric-Dependent Annotation Saturation for Learning from Label Distributions

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When annotators disagree on a label, the disagreement itself carries signal—and the number of annotators needed to capture it depends on the evaluation metric. We fine-tune NLI models on label distrib

MILE: A Mechanically Isomorphic Exoskeleton Data Collection System with Fingertip Visuotactile Sensing for Dexterous Manipulation

arXiv:2512.00324v3 Announce Type: replace-cross Abstract: Imitation learning provides a promising approach to dexterous hand manipulation, but its effectiveness is limited by the lack of large-scale,