research

Feed-forward Motion In-betweening for Any 4D

arXiv:2606.22131v1 Announce Type: new Abstract: 4D dynamics (3D geometry evolving over time) is a fundamental representation of the physical world and plays a crucial role in world modeling (e.g., ani

FetSelect: Task-Specific Architectures and Self-Supervised Learning for Automated Fetal Ultrasound Frame Selection

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arXiv:2606.22487v1 Announce Type: new Abstract: Automated frame selection for fetal biometry remains under addressed, with most prior work targeting generic quality assessment or downstream measuremen

researcharxiv-cs-cv

23 Jun 2026

Few-Shot Hyperspectral Aphid Detection via FastGAN Synthetic Data Generation, Transformer-Based Classification and Explainable AI

arXiv:2606.21267v1 Announce Type: new Abstract: Early detection of aphid infestation in crops is essential for preventing yield loss and reducing unnecessary pesticide use. Hyperspectral imaging combi

Fidelity- and Perception-Aware Local Implicit Attention for Arbitrary-Scale Image Super-Resolution

arXiv:2606.21910v1 Announce Type: new Abstract: Arbitrary-scale image super-resolution (ASISR) aims to reconstruct high-resolution images from low-resolution inputs over a continuous range of upscalin

Fine-grained Human Motion Understanding with Language Models

arXiv:2606.20888v1 Announce Type: new Abstract: In this work, we propose methodname, an LLM-based model for fine-grained human motion understanding that represents motion as a sequence of skeletal pos

Fine-Grained Uncertainty Quantification for Long-Form Language Model Outputs: A Comparative Study

arXiv:2602.17431v2 Announce Type: replace-cross Abstract: Uncertainty quantification has emerged as an effective approach to closed-book hallucination detection for LLMs, but existing methods are larg

FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation

arXiv:2606.23370v1 Announce Type: cross Abstract: Device-side Large Language Models (LLMs) have grown explosively, offering stronger privacy and higher availability than their cloud-side counterparts.

Flow Annealing Posterior Sampling for Function-Space Regression and Inverse Problems

arXiv:2606.22346v1 Announce Type: cross Abstract: Principled regression for stochastic processes is a long-standing challenge with deep connections to scientific inverse problems. We introduce Flow An

Flow Matching for Efficient and Scalable Data Assimilation

arXiv:2508.13313v5 Announce Type: replace-cross Abstract: Data assimilation (DA) estimates a dynamical system's state from noisy observations. Recent generative models like the ensemble score filter (

FoMoE: Breaking the Full-Replica Barrier with a Federation of MoEs

arXiv:2606.19025v2 Announce Type: replace Abstract: Pre-training Large Language Models (LLMs) typically demands large-scale infrastructure with tightly coupled hardware accelerators. Mixture-of-Expert

ForeHOI: Feed-forward 3D Object Reconstruction from Daily Hand-Object Interaction Videos

arXiv:2602.06226v2 Announce Type: replace Abstract: The ubiquity of monocular videos capturing daily hand-object interactions presents a valuable resource for embodied intelligence. While 3D hand reco

FORGE: Fused On-Register Gradient Elimination for Memory-Efficient LLM Training

arXiv:2606.22932v1 Announce Type: new Abstract: Reverse-mode differentiation computes every weight gradient, writes it to memory, and only then lets the optimizer read it back. This two-phase schedule

Foundation Models for Epileptogenic Zone Identification in Drug-Resistant Epilepsy

arXiv:2606.22657v1 Announce Type: new Abstract: Accurate identification of the epileptogenic zone (EZ) is essential for seizure freedom after resective surgery in drug-resistant epilepsy, yet seizure

FPGA-Accelerated Neuromorphic Vision System for Real-Time Orbital Object Detection

arXiv:2606.20727v1 Announce Type: cross Abstract: The escalating congestion in orbital space demands advanced monitoring solutions. This work presents a comprehensive open-source framework for neuromo

Frequency-Domain Neural ODEs for Modeling Non-Linear Dynamical Systems

arXiv:2606.22075v1 Announce Type: new Abstract: Standard continuous-depth models, such as Neural Ordinary Differential Equations (NODEs), offer significant advantages in modeling physical systems by l

Frequency Response Identification of Low-Order Systems: Finite-Sample Analysis

arXiv:2508.17142v3 Announce Type: replace-cross Abstract: This paper proposes a frequency-domain estimator for low-order systems from repeated noisy measurements. The estimator minimizes a quadratic d

From Markov to Laplace: How Mamba In-Context Learns Markov Chains

arXiv:2502.10178v2 Announce Type: replace Abstract: While transformer-based language models have driven the AI revolution thus far, their computational complexity has spurred growing interest in viabl

From Zipf's Law to Neural Scaling through Heaps' Law and Hilberg's Hypothesis

arXiv:2512.13491v3 Announce Type: replace-cross Abstract: We inspect the deductive connection between the neural scaling law and Zipf's law -- two statements discussed in machine learning and quantita

Full Nonlinear Nonholonomic Dynamics and Motion Analysis of a 3-DoF Underactuated Spherical Rolling Robot

arXiv:2606.22169v1 Announce Type: new Abstract: This paper presents a full nonlinear constrained dynamic model of MonoRollBot, a novel 3-DoF spherical rolling robot driven by a single motor, a lead-sc

researcharxiv-cs-ro

G-MASt3R-SfM: Graph-based View Pruning and Multi-stage Optimization for Robust SfM

arXiv:2606.22856v1 Announce Type: new Abstract: Structure from Motion (SfM) is essential for multi-view 3D reconstruction, however, its accuracy heavily relies on the accuracy of image matching. While

Gated MLPs as Symmetry-Broken Rank-1 Bilinear Attention

arXiv:2606.22172v1 Announce Type: new Abstract: We show that the conventional gated MLP can be viewed as a rank-1 approximation to a bilinear attention mechanism with two distinct factors correspondin

Generative Modeling via Kernelized Stochastic Interpolants

arXiv:2602.20070v3 Announce Type: replace Abstract: We develop a kernel method for generative modeling within the stochastic interpolant framework, replacing neural network training with linear system

Generative Relightable Avatars

arXiv:2606.22718v1 Announce Type: new Abstract: We present Generative Relightable Avatars (GRA), a person-specific method for photorealistic free-view rendering and environment-map relighting of full-

GeoFlow-SLAM++: A Robust Multi-Camera Visual-Inertial SLAM System with Relocalization

arXiv:2606.22051v1 Announce Type: new Abstract: Monocular and RGB-D visual-inertial SLAM systems remain susceptible to limited field of view, sensor-specific failure modes, and unreliable cross-sessio

researcharxiv-cs-ro

Geometric and Information Compression of Representations in Deep Learning

arXiv:2606.21593v1 Announce Type: new Abstract: Deep neural networks transform input data into latent representations that support a wide range of downstream tasks. These representations can be charac

Geometric Reconstruction of Extrinsic Contact Trajectories using Tactile Sensing and Proprioception for Tool Manipulation

arXiv:2606.22251v1 Announce Type: new Abstract: Tactile sensing enables robots to perceive rich contact information at the grasp, supporting tasks such as object recognition, in-hand pose estimation,

researcharxiv-cs-ro

GIM-ENDO: A Multimodal Endoscopic Image and Video Dataset for Gastric Intestinal Metaplasia Morphology and Pathology

arXiv:2606.20919v1 Announce Type: new Abstract: Gastric intestinal metaplasia (GIM) is a precursor lesion to gastric dysplasia and adenocarcinoma whose early detection is crucial for intervening in th

Go-with-the-Track: Video Compositing and Motion Control with Point Tracking

arXiv:2606.20891v1 Announce Type: new Abstract: Filmmaking demands precise motion control and reference image compositing -- capabilities that existing methods treat separately. Point-track-conditione

Gradient-Descent Steps to Success over Mean Accuracy: A Paradigm Shift for ML

arXiv:2606.22053v1 Announce Type: new Abstract: Traditional evaluation of machine learning (ML) models typically focuses on achieving the maximum possible accuracy irrespective of the computational co

Gradual Capacity Growth for Sparse Network Discovery

arXiv:2509.25665v2 Announce Type: replace Abstract: Sparse neural network methods typically assume that the target sparsity (or density) is fixed in advance, even though the relationship between netwo

GRAIN: Group Aggregation via Min-Norm Objective

arXiv:2606.22917v1 Announce Type: new Abstract: Learning instability is a long-standing problem across machine learning, but it is especially acute in the overparameterized regime that defines modern

GRIMIP: A General Framework for Instance-Specific Configuration of MIP Solvers Using LLMs

arXiv:2606.23299v1 Announce Type: new Abstract: Configuring the hyperparameters of Mixed-integer programming (MIP) solvers is a high-dimensional, instance-dependent optimization problem where suboptim

Hard or Just Unreached? Diagnosing the Sampling Blind Spot in Math-Reasoning Difficulty Estimation

arXiv:2606.19636v2 Announce Type: replace Abstract: Math and science reasoning benchmarks rely on pass@k, the fraction of sampled chains that reach gold, as the canonical per-example difficulty signal

Heads in the game

The Argentina v. France final of the 2022 Men’s World Cup in Qatar was shaping up to be one of the most epic games in soccer history. With just 12 minutes remaining in the extra time added to the game

researchmit-tech-review

HERMAN: Hierarchical Representation Matching for CLIP-based Class-Incremental Learning

arXiv:2509.22645v2 Announce Type: replace Abstract: Class-Incremental Learning (CIL) aims to endow models with the ability to continuously adapt to evolving data streams. Recent advances in pre-traine

HERO: Hypothesis-Driven Evidence Retrieval from Omics for Multi-Task Breast Cancer Analysis

arXiv:2606.21174v1 Announce Type: new Abstract: Matched multi-omics can improve WSI-based biomarker and prognosis prediction, but most existing pipelines use omics as a paral lel feature stream or tex

Hierarchical Concept-to-Appearance Guidance for Multi-Subject Image Generation

arXiv:2602.03448v2 Announce Type: replace Abstract: Multi-subject image generation aims to synthesize images that faithfully preserve the identities of multiple reference subjects while following text

Hierarchical Pooling for Sheaf Neural Networks

arXiv:2606.20932v1 Announce Type: new Abstract: Sheaf Neural Networks (SNNs) generalize Graph Neural Networks (GNNs) by replacing scalar node signals with stalk-valued signals and by using restriction

High-Dimensional Differentially Private Quantile Regression: Distributed Estimation and Statistical Inference

arXiv:2508.05212v2 Announce Type: replace-cross Abstract: With the development of big data and machine learning, privacy concerns have become increasingly critical, especially when handling heterogene

How Well Can Your Video Model Remember? Measuring Memory-Budget Trade-offs in Long Video Understanding

arXiv:2606.20726v1 Announce Type: new Abstract: We introduce a compact empirical model that quantifies how answer accuracy degrades as a function of frame budget B and temporal distance D in long vide

HPP: Hierarchical Programmatic Probing for Long Video Understanding by Decoupling Perception and Reasoning

arXiv:2606.21734v1 Announce Type: new Abstract: Understanding long videos requires fine-grained perception and multi-step, higher-order reasoning over complex, long-range spatio-temporal dynamics. Vis

Human and AI collaboration for pulmonary nodule segmentation

arXiv:2606.22486v1 Announce Type: new Abstract: Medical expert annotators are scarce, and blind reliance on artificial intelligence (AI) can be misleading, motivating approaches in which humans, parti

'I do not believe that AI is likely to cure cancer anytime soon'—@2plus2makes5 https://www.theatlantic.com/technology/2026/06/ai-cancer-prog…

'I do not believe that AI is likely to cure cancer anytime soon'—@2plus2makes5 https://www.theatlantic.com/technology/2026/06/ai-cancer-progress/687654/?gift=yDcMMOSj-3oJk8475KVVBne5CfZXWcmKJJrUAGArmL

researchyann-lecun--x

IDAG-Edit: Multi-Object Video Editing via Instance-Decoupled Attention and Guidance

arXiv:2606.22042v1 Announce Type: new Abstract: Diffusion-based video editing has made significant progress; however, achieving precise and temporally consistent object-level control, especially in mu

In software, complexity is a tax you pay on every future change. You might think, 'as long as it works, I'm good, I don't care about aesthet…

researchfrancois-chollet--x

In software, complexity is a tax you pay on every future change. You might think, 'as long as it works, I'm good, I don't care about aesthetics', but elegant code is about maintainability, not aesthet

Incremental Learning in Mirror Flows

arXiv:2606.23198v1 Announce Type: cross Abstract: We study mirror flows generated by a convex quadratic loss and a general convex lower semicontinuous mirror potential. We show that, when initialized

Input-schema identifiability limits in physics-informed surrogates for mechanics-governed flow

arXiv:2606.20655v1 Announce Type: cross Abstract: Physics-informed and data-driven surrogates are increasingly used to approximate mechanics-governed flow fields, but the target quantities assigned to

Integrated Marketing Attribution: A Bayesian Framework for Privacy-Safe Granular Measurement Anchored in MMM

arXiv:2606.16878v3 Announce Type: replace Abstract: Retail marketing measurement increasingly requires granular campaign-level insights without relying on user-level tracking. However, the two dominan