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HO-Flow: Generalizable Hand-Object Interaction Generation with Latent Flow Matching

arXiv:2604.10836v1 Announce Type: new Abstract: Generating realistic 3D hand-object interactions (HOI) is a fundamental challenge in computer vision and robotics, requiring both temporal coherence and

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Tutorials

How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networks

DGX agent

arXiv:2507.07067v3 Announce Type: replace-cross Abstract: Training effective artificial intelligence models for telecommunications is challenging due to the scarcity of deployment-specific data. Real

tutorialsarxiv-cs-lg

14 Apr 2026

How to Design a Compact High-Throughput Video Camera?

arXiv:2604.10619v1 Announce Type: new Abstract: High throughput video acquisition is a challenging problem and has been drawing increasing attention. Existing high throughput imaging systems splice hu

How to Spin an Object: First, Get the Shape Right

arXiv:2412.10273v3 Announce Type: replace Abstract: Image-to-3D models increasingly rely on hierarchical generation to disentangle geometry and texture. However, the design choices underlying these tw

Improving the Reasoning of Multi-Image Grounding in MLLMs via Reinforcement Learning

arXiv:2507.00748v3 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) perform well in single-image visual grounding but struggle with real-world tasks that demand cross-image re

INCRT: An Incremental Transformer That Determines Its Own Architecture

arXiv:2604.10703v1 Announce Type: new Abstract: Transformer architectures are designed by trial and error: the number of attention heads, the depth, and the head size are fixed before training begins,

Interpretable Alzheimer's Diagnosis via Multimodal Fusion of Regional Brain Experts

arXiv:2512.10966v2 Announce Type: replace-cross Abstract: Accurate and early diagnosis of Alzheimer's disease (AD) is critical for effective intervention and requires integrating complementary informa

KL Divergence Between Gaussians: A Step-by-Step Derivation for the Variational Autoencoder Objective

arXiv:2604.11744v1 Announce Type: new Abstract: Kullback-Leibler (KL) divergence is a fundamental concept in information theory that quantifies the discrepancy between two probability distributions. I

Language Reconstruction with Brain Predictive Coding from fMRI Data

arXiv:2405.11597v2 Announce Type: replace-cross Abstract: Many recent studies have shown that the perception of speech can be decoded from brain signals and subsequently reconstructed as continuous la

Learning and Enforcing Context-Sensitive Control for LLMs

arXiv:2604.10667v1 Announce Type: cross Abstract: Controlling the output of Large Language Models (LLMs) through context-sensitive constraints has emerged as a promising approach to overcome the limit

Learning from Contrasts: Synthesizing Reasoning Paths from Diverse Search Trajectories

arXiv:2604.11365v1 Announce Type: new Abstract: Monte Carlo Tree Search (MCTS) has been widely used for automated reasoning data exploration, but current supervision extraction methods remain ineffici

Learning Long-term Motion Embeddings for Efficient Kinematics Generation

arXiv:2604.11737v1 Announce Type: new Abstract: Understanding and predicting motion is a fundamental component of visual intelligence. Although modern video models exhibit strong comprehension of scen

Learning to Forget -- Hierarchical Episodic Memory for Lifelong Robot Deployment

arXiv:2604.11306v1 Announce Type: cross Abstract: Robots must verbalize their past experiences when users ask 'Where did you put my keys?' or 'Why did the task fail?' Yet maintaining life-long episodi

LitPivot: Developing Well-Situated Research Ideas Through Dynamic Contextualization and Critique within the Literature Landscape

arXiv:2604.02600v2 Announce Type: replace-cross Abstract: Developing a novel research idea is hard. It must be distinct enough from prior work to claim a contribution while also building on it. This r

MetroGS: Efficient and Stable Reconstruction of Geometrically Accurate High-Fidelity Large-Scale Scenes

arXiv:2511.19172v4 Announce Type: replace Abstract: Recently, 3D Gaussian Splatting and its derivatives have achieved significant breakthroughs in large-scale scene reconstruction. However, how to eff

MorphoFlow: Sparse-Supervised Generative Shape Modeling with Adaptive Latent Relevance

arXiv:2604.11636v1 Announce Type: new Abstract: Statistical shape modeling (SSM) is central to population level analysis of anatomical variability, yet most existing approaches rely on densely annotat

Multimodal Diffusion Forcing for Forceful Manipulation

arXiv:2511.04812v2 Announce Type: replace-cross Abstract: Given a dataset of expert trajectories, standard imitation learning approaches typically learn a direct mapping from observations (e.g., RGB i

Neural Surface Reconstruction from Sparse Views Using Epipolar Geometry

arXiv:2406.04301v3 Announce Type: replace Abstract: Reconstructing accurate surfaces from sparse multi-view images remains challenging due to severe geometric ambiguity and occlusions. Existing genera

NeuroPath: Practically Adopting Motor Imagery Decoding through EEG Signals

arXiv:2604.09654v1 Announce Type: cross Abstract: Motor Imagery (MI) is an emerging Brain-Computer Interface (BCI) paradigm where a person imagines body movements without physical action. By decoding

Not Your Stereo-Typical Estimator: Combining Vision and Language for Volume Perception

arXiv:2604.09886v1 Announce Type: cross Abstract: Accurate volume estimation of objects from visual data is a long-standing challenge in computer vision with significant applications in robotics, logi

PASTA: Vision Transformer Patch Aggregation for Weakly Supervised Target and Anomaly Segmentation

arXiv:2604.09701v1 Announce Type: new Abstract: Detecting unseen anomalies in unstructured environments presents a critical challenge for industrial and agricultural applications such as material recy

Predicting integers from continuous parameters

arXiv:2602.10751v2 Announce Type: replace Abstract: We study the problem of predicting numeric labels that are constrained to the integers or to a subrange of the integers. For example, the number of

Preventing Latent Rehearsal Decay in Online Continual SSL with SOLAR

arXiv:2604.10586v1 Announce Type: cross Abstract: This paper explores Online Continual Self-Supervised Learning (OCSSL), a scenario in which models learn from continuous streams of unlabeled, non-stat

Probabilistic Prediction of Neural Dynamics via Autoregressive Flow Matching

arXiv:2604.11178v1 Announce Type: cross Abstract: Forecasting neural activity in response to naturalistic stimuli remains a key challenge for understanding brain dynamics and enabling downstream neuro

Quantum-Gated Task-interaction Knowledge Distillation for Pre-trained Model-based Class-Incremental Learning

arXiv:2604.11112v1 Announce Type: cross Abstract: Class-incremental learning (CIL) aims to continuously accumulate knowledge from a stream of tasks and construct a unified classifier over all seen cla

Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning

arXiv:2604.11407v1 Announce Type: cross Abstract: We revisit retrieval-augmented generation (RAG) by embedding retrieval control directly into generation. Instead of treating retrieval as an external

Ro-SLM: Onboard Small Language Models for Robot Task Planning and Operation Code Generation

arXiv:2604.10929v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) provide robots with contextual reasoning abilities to comprehend human instructions. Yet, current LLM-en

tutorialsarxiv-cs-ro

Smart Paste: Automatically Fixing Copy/Paste for Google Developers

arXiv:2510.03843v4 Announce Type: replace-cross Abstract: Manually editing pasted code is a long-standing developer pain point. In internal software development at Google, we observe that code is past

Specificity-aware reinforcement learning for fine-grained open-world classification

arXiv:2603.03197v3 Announce Type: replace Abstract: Classifying fine-grained visual concepts under open-world settings, i.e., without a predefined label set, demands models to be both accurate and spe

Spotlight and Shadow: Attention-Guided Dual-Anchor Introspective Decoding for MLLM Hallucination Mitigation

arXiv:2604.10071v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable reasoning capabilities yet continue to suffer from hallucination, where generated

Structured State-Space Regularization for Compact and Generation-Friendly Image Tokenization

arXiv:2604.11089v1 Announce Type: new Abstract: Image tokenizers are central to modern vision models as they often operate in latent spaces. An ideal latent space must be simultaneously compact and ge

SVSR: A Self-Verification and Self-Rectification Paradigm for Multimodal Reasoning

arXiv:2604.10228v1 Announce Type: new Abstract: Current multimodal models often suffer from shallow reasoning, leading to errors caused by incomplete or inconsistent thought processes. To address this

Symmetry-Aware Generative Modeling through Learned Canonicalization

arXiv:2501.07773v3 Announce Type: replace Abstract: Generative modeling of symmetric densities has a range of applications in AI for science, from drug discovery to physics simulations. The existing g

TacMan-Turbo: Proactive Tactile Control for Robust and Efficient Articulated Object Manipulation

arXiv:2508.02204v4 Announce Type: replace Abstract: Adept manipulation of articulated objects is essential for robots to operate successfully in human environments. Such manipulation requires both eff

tutorialsarxiv-cs-ro

TimeRewarder: Learning Dense Reward from Passive Videos via Frame-wise Temporal Distance

arXiv:2509.26627v2 Announce Type: replace Abstract: Designing dense rewards is crucial for reinforcement learning (RL), yet in robotics it often demands extensive manual effort and lacks scalability.

Tipiano: Cascaded Piano Hand Motion Synthesis via Fingertip Priors

arXiv:2604.09692v1 Announce Type: new Abstract: Synthesizing realistic piano hand motions requires both precision and naturalness. Physics-based methods achieve precision but produce stiff motions; da

TRACE: An Experiential Framework for Coherent Multi-hop Knowledge Graph Question Answering

arXiv:2604.11193v1 Announce Type: new Abstract: Multi-hop Knowledge Graph Question Answering (KGQA) requires coherent reasoning across relational paths, yet existing methods often treat each reasoning

tutorialsarxiv-cs-cl

Transformers for dynamical systems learn transfer operators in-context

arXiv:2602.18679v2 Announce Type: replace Abstract: Large-scale foundation models for scientific machine learning adapt to physical settings unseen during training, such as zero-shot transfer between

Transformers Learn Latent Mixture Models In-Context via Mirror Descent

arXiv:2604.10848v1 Announce Type: new Abstract: Sequence modelling requires determining which past tokens are causally relevant from the context and their importance: a process inherent to the attenti

Transformers Learn the Optimal DDPM Denoiser for Multi-Token GMMs

arXiv:2604.10074v1 Announce Type: new Abstract: Transformer-based diffusion models have demonstrated remarkable performance at generating high-quality samples. However, our theoretical understanding o

U^{2}Flow: Uncertainty-Aware Unsupervised Optical Flow Estimation

arXiv:2604.10056v1 Announce Type: new Abstract: Unsupervised optical flow methods typically lack reliable uncertainty estimation, limiting their robustness and interpretability. We propose U^{2}Flow,

Universality of first-order methods on random and deterministic matrices

arXiv:2604.11729v1 Announce Type: cross Abstract: General first-order methods (GFOM) are a flexible class of iterative algorithms which update a state vector by matrix-vector multiplications and entry

Unmixing-Guided Spatial-Spectral Mamba with Clustering Tokens for Hyperspectral Image Classification

arXiv:2604.09948v1 Announce Type: new Abstract: Although hyperspectral image (HSI) classification is critical for supporting various environmental applications, it is a challenging task due to the spe

Use of AI Tools: Guidelines to Maintain Academic Integrity in Computing Colleges

arXiv:2604.11111v1 Announce Type: cross Abstract: The rapid adoption of AI tools such as ChatGPT has significantly transformed academic practices, offering considerable benefits for both students and

Using Deep Learning Models Pretrained by Self-Supervised Learning for Protein Localization

arXiv:2604.10970v1 Announce Type: new Abstract: Background: Task-specific microscopy datasets are often small, making it difficult to train deep learning models that learn robust features. While self-

What do your logits know? (The answer may surprise you!)

arXiv:2604.09885v1 Announce Type: new Abstract: Recent work has shown that probing model internals can reveal a wealth of information not apparent from the model generations. This poses the risk of un

Who Handles Orientation? Investigating Invariance in Feature Matching

arXiv:2604.11809v1 Announce Type: new Abstract: Finding matching keypoints between images is a core problem in 3D computer vision. However, modern matchers struggle with large in-plane rotations. A st

Automatic Self-supervised Learning for Social Recommendations

arXiv:2412.18735v3 Announce Type: replace-cross Abstract: In recent years, researchers have leveraged social relations to enhance recommendation performance. However, most existing social recommendati