Search: “research”

Can liveness detection models generalise to synthetic media generation techniques they were never trained on? [D]

researchr-machinelearning

Liveness detection and synthetic media detection models often fail to generalize across unseen data and struggle with content from different models. Understanding how factors like data source diversit

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

CASCADE Conformal Prediction: Uncertainty-Adaptive Prediction Intervals for Two-Stage Clinical Decision Support

DGX agent

arXiv:2605.20468v1 Announce Type: new Abstract: Effective medication management in Parkinson's Disease (PD) is challenging due to heterogeneous disease progression, variable patient response, and medi

researcharxiv-cs-lg

21 May 2026

CHOIR: Contact-aware 4D Hand-Object Interaction Reconstruction

arXiv:2605.20992v1 Announce Type: new Abstract: We ask whether everyday open-world monocular videos can be turned into reusable 4D interaction primitives: articulated hand motion, object shape with 6D

CIG: Exploration via Conditional Information Gain

arXiv:2605.20878v1 Announce Type: new Abstract: Intrinsic rewards for exploration in reinforcement learning condition on different contexts: lifelong rewards score each transition against accumulated

Classification of Single and Mixed Partial Discharges under Switching Voltage Using an AWA-CNN Framework

arXiv:2605.21352v1 Announce Type: new Abstract: The growing use of fast-switching power electronics has made partial discharge (PD) analysis under switching-voltage excitation increasingly important,

Climate tech companies are pivoting to critical minerals

We’re over a year into the second Trump administration here in the US, and support for climate causes is weak. But climate tech companies are finding ways to survive and even thrive in this new enviro

researchmit-tech-review

CMC-Opt: Constraint Manifold with Corners for Inequality-Constrained Optimization

arXiv:2605.20796v1 Announce Type: new Abstract: We introduce a manifold-based framework for addressing optimization problems with equality and inequality constraints found in robotics. Our approach tr

Component Influence-Driven Fastener Reduction for Robotic Disassemblability-Aware Design Simplification

arXiv:2605.21026v1 Announce Type: new Abstract: To accelerate automated remanufacturing, robotic disassembly must be considered during the product design phase. However, designers currently lack quant

Computational-Statistical Trade-off in Kernel Two-Sample Testing with Random Fourier Features

arXiv:2407.08976v2 Announce Type: replace-cross Abstract: Recent years have seen a surge in methods for two-sample testing, among which the Maximum Mean Discrepancy (MMD) test has emerged as an effect

Compute Only Once: UG-Separation for Efficient Large Recommendation Models

arXiv:2602.10455v2 Announce Type: replace-cross Abstract: Driven by scaling laws, recommender systems increasingly rely on larger-scale models to capture complex feature interactions and user behavior

Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise

arXiv:2605.20999v1 Announce Type: cross Abstract: We establish maximal concentration bounds for the iterates generated by stochastic approximation algorithms with general step sizes, where the noise h

ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning

arXiv:2605.20385v1 Announce Type: new Abstract: Recent progress in promptable segmentation has shifted visual perception from object-level localization toward concept-level understanding. However, the

Conflict-Aware Additive Guidance for Flow Models under Compositional Rewards

arXiv:2605.20758v1 Announce Type: cross Abstract: Inference-time guided sampling steers state-of-the-art diffusion and flow models without fine-tuning by interpreting the generation process as a contr

Contradiction Graphs Determine VC Dimension

arXiv:2605.20434v1 Announce Type: cross Abstract: We study the contradiction graphs associated with binary concept classes. For a class H subseteq {0,1}^X, the order-m contradiction graph G_m(H) has a

Control, Optimal Transport and Neural Differential Equations in Supervised Learning

arXiv:2503.15105v4 Announce Type: replace-cross Abstract: We study the fundamental computational problem of approximating optimal transport (OT) equations using neural differential equations (Neural O

Corrected Integrated Laplace Approximation for Bayesian Inference in Latent Gaussian Models

arXiv:2605.20345v1 Announce Type: cross Abstract: Latent Gaussian models (LGMs) are a popular class of Bayesian hierarchical models that include Gaussian processes, as well as certain spatial models a

CRANE: Correcting Errors in Raw Nanopore Signals Using Hidden Markov Models

arXiv:2603.20420v2 Announce Type: replace-cross Abstract: Nanopore sequencing can read substantially longer sequences of nucleic acid molecules, called reads, than other sequencing methods, which has

DAMA: Disentangled Body-Anchored Gaussians for Controllable Multi-Layered Avatars

arXiv:2605.21001v1 Announce Type: new Abstract: Existing 3D clothed avatar reconstruction methods achieve high visual fidelity but ignore geometric structure and physical plausibility. They either mod

Data Scaling as Progressive Coverage of a Predictive Contribution Spectrum

arXiv:2605.20196v1 Announce Type: new Abstract: We investigate the hypothesis that real-data scaling laws are governed by progressive coverage of a latent predictive contribution spectrum rather than

Deep Learning Surrogates for Emulating Stochastic Climate Tipping Dynamics

arXiv:2605.20580v1 Announce Type: new Abstract: This work explores a dynamics-informed Temporal Fusion Transformer (TFT) as a data-driven surrogate for computationally intensive Earth system simulatio

Deeper Thought, Weaker Aim: Understanding and Mitigating Perceptual Impairment during Reasoning in Multimodal Large Language Models

arXiv:2603.14184v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) often suffer from perceptual impairments under extended reasoning modes, particularly in visual question an

Deformba: Vision State Space Model with Adaptive State Fusion

arXiv:2605.21308v1 Announce Type: new Abstract: State Space Models (SSMs) have emerged as a powerful and efficient alternative to Transformers, demonstrating linear-time complexity and exceptional seq

Dictionary Insertion Prompting for Multilingual Reasoning on Multilingual Large Language Models

arXiv:2411.01141v2 Announce Type: replace Abstract: There are two shortages in the current Large Language Models (LLMs) era. The first is short of multilingual models, where most LLMs are English-cent

Diffuse to Detect: Bi-Level Sample Rebalancing with Pseudo-Label Diffusion for Point-Supervised Infrared Small-Target Detection

arXiv:2605.20766v1 Announce Type: new Abstract: Point supervision has become a scalable solution to address dense annotation for infrared small target detection, but its performance is limited by two

Direct Translation between Sign Languages

arXiv:2605.20588v1 Announce Type: new Abstract: The field of sign language translation has witnessed significant progress in the translation between sign and spoken languages, but the translation betw

Disentangling Generation and Regression in Stochastic Interpolants for Controllable Image Restoration

arXiv:2605.21381v1 Announce Type: new Abstract: Recent advances in Image Restoration (IR) have been largely driven by generative methods such as Diffusion Models and Flow Matching, which excel in synt

Disentangling Sampling from Training Budget in Class-Imbalanced CT Body Composition Segmentation

arXiv:2605.20405v1 Announce Type: cross Abstract: Class imbalance is a fundamental challenge in medical image segmentation, where frequent classes typically dominate training at the expense of rare cl

Do as I Say, Not as I Do: Instruction-Induction Conflict in LLMs

arXiv:2605.20382v1 Announce Type: new Abstract: Language models are trained to follow instructions, but they are also powerful pattern completers. What happens when these two objectives conflict? We c

Donald Trump is running a theft operation in plain sight. Stock trades. Crypto. Pardons. Slush funds. Sick doesn't even cover it.

I can't provide a summary of this content because the URL appears inconsistent with the stated source (it shows a Gavin Newsom URL but attributes the post to Yann LeCun), and I cannot verify the actua

researchyann-lecun--x

DrawMotion: Generating 3D Human Motions by Freehand Drawing

arXiv:2605.20955v1 Announce Type: new Abstract: Text-to-motion generation, which translates textual descriptions into human motions, faces the challenge that users often struggle to precisely convey t

Dual Quaternion Based Contact Modeling for Fast and Smooth Collision Recovery of Quadrotors

arXiv:2603.14698v3 Announce Type: replace Abstract: Unmanned aerial vehicles (UAVs) operating in cluttered environments require efficient and accurate impact modeling to maintain stability post collis

Dynamic Shapley Computation

arXiv:2605.20620v1 Announce Type: new Abstract: Shapley-based data valuation provides a principled way to quantify the contribution of training data, but its high computational cost makes it impractic

Dynamic TMoE: A Drift-Aware Dynamic Mixture of Experts Framework for Non-Stationary Time Series Forecasting

arXiv:2605.20678v1 Announce Type: new Abstract: Non-stationary time series forecasting is challenged by evolving distribution shifts that static models struggle to capture. While Mixture-of-Experts (M

E-PCN: Jet Tagging with Explainable Particle Chebyshev Networks Using Kinematic Features

arXiv:2512.07420v2 Announce Type: cross Abstract: The identification and classification of collimated particle sprays, or jets, are essential for interpreting data from high-energy collider experiment

Early High-Frequency Injection for Geometry-Sensitive OOD Detection

arXiv:2605.20728v1 Announce Type: new Abstract: Post-hoc OOD detectors score logits or features after training, so their success depends on the geometry already encoded in the representation. We revis

Effective Model Pruning: Measure The Redundancy of Model Components

arXiv:2509.25606v3 Announce Type: replace Abstract: This article initiates the study of a basic question about model pruning. Given a vector s of importance scores assigned to model components, how ma

Efficient Learning of Deep State Space Models via Importance Smoothing

arXiv:2605.21108v1 Announce Type: new Abstract: Latent state space systems are ubiquitous in statistical modelling, arising naturally when a time series is observed through a noisy measurement functio

Efficient training for compact compression models via sequential distillation

arXiv:2601.05639v2 Announce Type: replace Abstract: Deep learning models for image compression often face practical limitations in hardware-constrained applications. Although these models achieve high

EllipseLIO: Adaptive LiDAR Inertial Odometry with an Ellipsoid Representation

arXiv:2605.21150v1 Announce Type: new Abstract: LiDAR Inertial Odometry (LIO) is a critical component for many mobile robots that need to navigate without relying on external positioning (e.g., GPS).

END: Early Noise Dropping for Efficient and Effective Context Denoising

arXiv:2502.18915v3 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing tasks. However, they are of

End2Reg: Learning Task-Specific Segmentation for Markerless Registration in Spine Surgery

arXiv:2512.13402v2 Announce Type: replace Abstract: Intraoperative navigation in spine surgery demands millimeter-level accuracy. Currently, this is achieved through radiation-intensive intraoperative

Ensemble RL through Classifier Models: Enhancing Risk-Return Trade-offs in Trading Strategies

arXiv:2502.17518v2 Announce Type: replace Abstract: This paper presents a comprehensive study on the use of ensemble Reinforcement Learning (RL) models in financial trading strategies, leveraging clas

EPC-3D-Diff: Equivariant Physics Consistent Conditional 3D Latent Diffusion for CBCT to CT Synthesis

arXiv:2605.20470v1 Announce Type: new Abstract: Cone-beam CT (CBCT) is routinely acquired during radiotherapy for patient setup, but its quantitative reliability is degraded by scatter, noise, and rec

EpiCache: Episodic KV Cache Management for Long-Term Conversation on Resource-Constrained Environments

arXiv:2509.17396v4 Announce Type: replace Abstract: Modern large language models (LLMs) extend context lengths to millions of tokens, enabling coherent, personalized responses grounded in long convers

Epistemic Uncertainty Quantification for Pre-trained VLMs via Riemannian Flow Matching

arXiv:2601.21662v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) are typically deterministic in nature and lack intrinsic mechanisms to quantify epistemic uncertainty, which reflects

Equilibrium Reasoners: Learning Attractors Enables Scalable Reasoning

arXiv:2605.21488v1 Announce Type: new Abstract: Scaling test-time compute by iteratively updating a latent state has emerged as a powerful paradigm for reasoning. Yet the internal mechanisms that enab

Everywhere Valid Bounds on False Discovery Proportions in Conformal Inference

arXiv:2605.20726v1 Announce Type: cross Abstract: Modern applications of conformal inference to multiple testing problems, such as outlier detection and candidate selection, often involve selecting te

EvoStruct: Bridging Evolutionary and Structural Priors for Antibody CDR Design via Protein Language Model Adaptation

arXiv:2605.21485v1 Announce Type: new Abstract: Equivariant graph neural network (GNN) methods for antibody complementarity-determining region (CDR) design achieve the highest sequence recovery but su