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Intend, Reflect, Refine: An Adaptive Multimodal Reflection Framework for Autonomous Driving

arXiv:2606.22913v1 Announce Type: new Abstract: Recent Vision-Language-Action (VLA) models have advanced end-to-end autonomous driving by incorporating reasoning for better interpretability and planni

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Intent-Handover: Grounding Language in Human-Usage Regions for Trustworthy Robot-to-Human Handovers

DGX agent

arXiv:2503.03579v2 Announce Type: replace-cross Abstract: Spoken instructions in robot-to-human handovers may specify either an object ('the cup') or an intended use ('pour water'); in both cases, suc

safetyarxiv-cs-lg

23 Jun 2026

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

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

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

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 for Predicting Startup Funding, Patenting, and Exits

arXiv:2510.09465v2 Announce Type: replace Abstract: This study develops an interpretable machine learning framework to forecast startup outcomes, including funding, patenting, and exit. A firm-quarter

applicationsarxiv-cs-lg

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 Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability

arXiv:2606.23177v1 Announce Type: new Abstract: Deep learning-based medical image segmentation models are trained using annotations that exhibit systematic bias and variability across raters. While pr

safetyarxiv-cs-cv

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

Inverse Problem for Partial Differential Equations with Jump Discontinuities in Coefficients by Two-stage Physics-Informed Deep Learning and Statistical Mixture Models

arXiv:2510.14656v2 Announce Type: replace-cross Abstract: This work proposes a two-stage physics-informed deep learning framework that combines neural-network-based sampling with statistical inference

Inverting the Bellman Equation: From Q-Values to World Models

arXiv:2606.21173v1 Announce Type: new Abstract: Model-based and model-free reinforcement learning are traditionally viewed as separate paradigms: instead of learning a model of the transition kernel P

safetyarxiv-cs-lg

IOI: Decoupling Kinematics and Physics for Interactive World Models

model-releasesarxiv-cs-ro

arXiv:2606.23296v1 Announce Type: new Abstract: Developing generalist embodied agents requires interactive environments providing visually realistic feedback and accurate action-conditioned dynamics.

IRumAI: Reinforcement Learning for Indian Rummy

arXiv:2606.21975v1 Announce Type: cross Abstract: Despite its massive player base and complex hidden-information dynamics, Indian Rummy has received no reinforcement learning attention. Existing agent

safetyarxiv-cs-lg

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

Is Our Benchmark Enough? An Analysis of Continual Learning for MLLMs

arXiv:2606.20961v1 Announce Type: new Abstract: Continual adaptation is essential for multimodal large language models (MLLMs) deployed across evolving domains, but the state-of-the-art MR-LoRA method

ISOPoT: Imaging Sonar Odometry by Point Tracking

arXiv:2606.23006v1 Announce Type: new Abstract: Reliable navigation in underwater environments remains a key challenge in marine robotics. In such scenarios, forward-looking sonars are a natural choic

applicationsarxiv-cs-ro

Iterative Convex Optimization with Control Barrier Functions for Obstacle Avoidance among Polytopes

arXiv:2603.05916v2 Announce Type: replace Abstract: Obstacle avoidance of polytopic obstacles by polytopic robots is a challenging problem in optimization-based control and trajectory planning. Many e

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

ITNet: A Learnable Integral Transform That Subsumes Convolution, Attention, and Recurrence

arXiv:2606.19538v2 Announce Type: replace-cross Abstract: Convolutional networks, recurrent networks, and transformers each encode different inductive biases -- locality, sequential memory, and conten

local-aiarxiv-cs-lg

It's Much Easier for Neural Networks to learn Game of Life Dynamics with the Right Activation Function: Polynomial Kolmogorov-Arnold Networks

arXiv:2606.23587v1 Announce Type: new Abstract: Previous work has found a gap between the scale of neural networks that reliably learn Conway's Game of Life, and minimal networks capable of representi

IViT: A Novel Interpretable Visual Transformer for Skin Disease Detection

arXiv:2606.22892v1 Announce Type: cross Abstract: The clinical diagnosis of skin diseases is susceptible to interference from inter-class similarity of skin lesions, and over-reliance on clinicians'ex

safetyarxiv-cs-cv

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

Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation

arXiv:2509.22307v2 Announce Type: replace Abstract: Lightweight 3D medical image segmentation remains constrained by a fundamental extit{``efficiency / robustness conflict''}, particularly when proces

local-aiarxiv-cs-cv

JPPD: Joint Prediction_Planning Diffusion with Differentiable Safety Guidance for Dynamic Obstacle Avoidance in Intelligent Transportation Systems

arXiv:2606.20686v1 Announce Type: new Abstract: Shared-space transportation operation requires low-speed autonomous platforms to navigate safely and efficiently among pedestrians, service robots, micr

Jury Duty: Calibration and Orientation Failures in MLLM-as-a-Judge Under Cultural Ambiguity

arXiv:2606.20676v1 Announce Type: new Abstract: MLLM-as-a-Judge is conventionally validated by agreement with human annotations, but this metric is undefined when the human pool is culturally heteroge

Kamera: Unified Position-Invariant Multimodal KV Cache for Training-Free Reuse

arXiv:2606.23581v1 Announce Type: cross Abstract: Multimodal agents repeatedly re-examine the same video frames, UI screenshots, and rendered artifacts as their context window slides and reasoning ite

Tutorials

Keep The Essentials: Efficient Reference Conditioned Generation via Token Dropping

arXiv:2606.23682v1 Announce Type: new Abstract: Reference-based diffusion models enable highly controllable image generation by leveraging elements from input images to guide prompt-driven synthesis.

tutorialsarxiv-cs-cv

Keeping the Evidence Chain: Semantic Evidence Allocation for Training-Free Token Pruning in Video Temporal Grounding

arXiv:2603.05663v2 Announce Type: replace Abstract: Video Temporal Grounding (VTG) localizes the temporal boundaries of query-relevant moments in long videos, making video-language-model prohibitively

local-aiarxiv-cs-cv

KEMO: Event-Driven Keyframe Memory for Long-Horizon Robot Manipulation with VLA Policies

arXiv:2606.23589v1 Announce Type: new Abstract: Long-horizon robot manipulation remains challenging because similar observations may occur at different execution stages, while the appropriate action d

applicationsarxiv-cs-ro

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

KITE: Decoupling Kinematics and Interaction for Zero-Shot Cross-Embodiment Manipulation

arXiv:2606.22113v1 Announce Type: new Abstract: Generalizing manipulation policies across robot embodiments remains difficult because standard policies entangle task reasoning with embodiment-specific

KiVi: Kinesthetic-Visuospatial Integration for Dynamic and Safe Egocentric Legged Locomotion

arXiv:2509.23650v3 Announce Type: replace Abstract: Vision-based locomotion has shown great promise in enabling legged robots to perceive and adapt to complex environments. However, visual information

applicationsarxiv-cs-ro

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

Koshur Pixel: a large-scale synthetic ocr dataset for kashmiri

arXiv:2606.23144v1 Announce Type: new Abstract: Optical Character Recognition (OCR) for low-resource languages is often constrained by the lack of annotated training data and the complexity of script-

applicationsarxiv-cs-cv

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

L-SR1: Learned Symmetric-Rank-One Preconditioning

arXiv:2508.12270v2 Announce Type: replace-cross Abstract: End-to-end deep learning has achieved impressive results but remains limited by its reliance on large labeled datasets, poor generalization to

applicationsarxiv-cs-cv

L20-Edu-135M: An Auditable Single-GPU Study of Data-Efficient Small Language Modeling

arXiv:2606.22189v1 Announce Type: new Abstract: Small language models are cheap to serve and feasible on local hardware, but strong public 135M-class systems are commonly trained with hundreds of bill

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

Large Language Model-Assisted Cleaning of Report-Derived Labels in a Large-Scale Chest CT Dataset

arXiv:2606.22382v1 Announce Type: cross Abstract: Purpose: To evaluate whether large language model (LLM)-assisted label cleaning can identify label-report discordance in CT-RATE, a large-scale public

LaST-HD: Learning Latent Physical Reasoning from Scalable Human Data for Robot Manipulation

arXiv:2606.23685v1 Announce Type: new Abstract: Human-hand demonstrations provide a direct and scalable source of physical interaction data for robot learning. While manual retargeting is indispensabl

Latent Goal Prediction from Language for Model-Based Planning

arXiv:2606.20627v1 Announce Type: cross Abstract: Planning with world models is bottlenecked by compounding prediction errors and the difficulty of defining optimizable goals. Visual targets provide p

local-aiarxiv-cs-lg

LAYUP: Asynchronous decentralized gradient descent with LAYer-wise UPdates

arXiv:2410.05985v4 Announce Type: replace Abstract: The increasing size of deep learning models has made distributed training across multiple devices essential. Synchronous, centralized methods incur

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

researcharxiv-cs-ro

Learned Controllers for Agile Quadrotors in Pursuit-Evasion Games

arXiv:2506.02849v3 Announce Type: replace-cross Abstract: In this letter we study 1v1 quadrotor pursuit-evasion, where a pursuer and an evader are trained via reinforcement learning (RL) by competing

safetyarxiv-cs-lg