research

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

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

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

DGX agent

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

21 May 2026

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 Table QA via TableGrid Navigation and Progressive Inference Prompting

arXiv:2605.20254v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown promising results on NLP tasks, however, their performance on tabular data still needs research attention, bec

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

ELEMENT: Multi-Modal Retinal Vessel Segmentation Based on a Coupled Region Growing and Machine Learning Approach

arXiv:2605.20458v1 Announce Type: new Abstract: Vascular structures in the retina contain important information for the detection and analysis of ocular diseases, including age-related macular degener

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

Enhancing Scientific Discourse: Machine Translation for the Scientific Domain

arXiv:2605.20912v1 Announce Type: new Abstract: The increasing volume of scientific research necessitates effective communication across language barriers. Machine translation (MT) offers a promising

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

Fast and Stable Triangular Inversion for Delta-Rule Linear Transformers

arXiv:2605.21325v1 Announce Type: new Abstract: Linear attention has emerged as a cornerstone for efficient long-context architectures, as evidenced by its integration into state-of-the-art open-sourc

Fast Reconstruction of Exact Maxwell Dynamics from Sparse Data

arXiv:2605.20514v1 Announce Type: new Abstract: We introduce FLASH-MAX, a shallow, exact-by-construction neural network architecture for predicting homogeneous electromagnetic fields from sparse point

Faster or Stronger: Towards Flexible Visual Place Recognition via Weighted Aggregation and Token Pruning

arXiv:2605.20551v1 Announce Type: new Abstract: Visual Place Recognition (VPR) aims to match a query image to reference images of the same place in a large-scale database. Recent state-of-the-art meth

Fault-Tolerant, Rigidity-Preserving Control of Inflatable Truss Robots

arXiv:2605.20561v1 Announce Type: new Abstract: Isoperimetric robotic trusses can adapt to different tasks and environments because they have a high strength-to-weight ratio, can change their own shap

Findings of the Fifth Shared Task on Multilingual Coreference Resolution: Expanding Datasets for Long-Range Entities

arXiv:2605.21369v1 Announce Type: new Abstract: This paper describes the fifth edition of the Shared Task on Multilingual Coreference Resolution, held in conjunction with the CODI-CRAC 2026 workshop.

Finite-Time Regret Analysis of Retry-Aware Bandits

arXiv:2605.20854v1 Announce Type: new Abstract: We study a stochastic bandit algorithm motivated by retry-aware objectives that value the best outcome among multiple attempts, such as pass@k and max@k

Flow Map Language Models: One-step Language Modeling via Continuous Denoising

arXiv:2602.16813v3 Announce Type: replace Abstract: Language models based on discrete diffusion have attracted widespread interest for their potential to provide faster generation than autoregressive

Focus-then-Context: Subject-Centric Progressive Visual Token Reduction for Vision-Language Models

arXiv:2605.20950v1 Announce Type: new Abstract: Vision-Language Models (VLMs) face a bottleneck of prohibitive computational costs arising from massive visual token sequences during inference. Existin

From Circuit Evidence to Mechanistic Theory: An Inductive Logic Approach

arXiv:2605.21303v1 Announce Type: new Abstract: Mechanistic interpretability produces circuit-level causal analyses of neural network behaviour, but discovered circuits often remain isolated experimen

From swept contact to pose: Probe-aware registration via complementary-shape docking

arXiv:2605.21398v1 Announce Type: new Abstract: Accurate registration between a prior model and the real scene is essential for high-precision robotic manipulation, yet optical methods suffer from lon

Fully Actuated Manifold Constraint Based Output Feedback Control for Input-Constrained Uncertain Nonlinear Systems

arXiv:2605.21439v1 Announce Type: cross Abstract: This paper presents a low-complexity, model-free, output-feedback controller for a class of unknown time-varying nonlinear systems with unknown input

FusionCell: Cross-Attentive Fusion of Layout Geometry and Netlist Topology for Standard-Cell Performance Prediction

arXiv:2605.20287v1 Announce Type: cross Abstract: Standard cells form the building blocks of digital circuits, so their delay and power critically influence chip-level performance; yet characterizatio

Gated DeltaNet has been one of my favorite 'hybrid attention' newcomers in the good old transformer stack. Excited to see Gated DeltaNet-2. …

researchsebastian-raschka--x

Gated DeltaNet has been one of my favorite 'hybrid attention' newcomers in the good old transformer stack. Excited to see Gated DeltaNet-2. Adding it to my reading stack. In the meantime, I have a pri

Gaze into the Details: Locality-Sensitive Enhancement for OCTA Retinal Vessel Segmentation

arXiv:2605.20651v1 Announce Type: new Abstract: Existing deep learning frameworks for Optical Coherence Tomography Angiography (OCTA) vessel segmentation are largely derived from the U-Net architectur

Generation of Heterogeneous PET Images from Uniform Organ Activity Maps Using a Pretrained Domain-Adapted Diffusion Model

arXiv:2605.20267v1 Announce Type: new Abstract: Synthetic PET images are valuable for quantitative imaging workflow development, scalable virtual imaging trials, and deep learning model training, but

Generative AI Practices, Literacy, and Divides: An Empirical Analysis in the Italian Context

arXiv:2512.03671v2 Announce Type: replace Abstract: The rise of generative AI (GenAI) chatbots accessible via conversational interfaces is transforming digital interactions and holds economic promise.

Genetic Programming with Transformer-Based Mutation for Approximate Circuit Design

arXiv:2605.21055v1 Announce Type: cross Abstract: A recent trend is to leverage machine learning models to improve the evolutionary design and optimization process. We propose a novel transformer-base

GeoPT: Scaling Physics Simulation via Lifted Geometric Pre-Training

arXiv:2602.20399v2 Announce Type: replace Abstract: Neural simulators promise efficient surrogates for physics simulation, but scaling them is bottlenecked by the prohibitive cost of generating high-f

Goodbye Drift: Anchored Tree Sampling for Long-Horizon Video-to-Video Generation

arXiv:2605.20476v1 Announce Type: new Abstract: Long-horizon video generation suffers from two intertwined issues. First, there is drift, where video quality degrades over time. Second, there are cont

Gradient Scalability and Taylor Surrogation of Quantum Cost Landscapes

arXiv:2507.06344v3 Announce Type: replace-cross Abstract: Variational Quantum Algorithms are promising candidates for near-term quantum computing, yet they face scalability challenges due to barren pl

GraphCSVAE: Graph Categorical Structured Variational Autoencoder for Spatiotemporal Auditing of Physical Vulnerability Towards Sustainable Post-Disaster Risk Reduction

arXiv:2509.10308v2 Announce Type: replace Abstract: In the aftermath of disasters, many institutions worldwide face challenges in monitoring changes in disaster risk, limiting assessment of progress t

Grounding Driving VLA via Inverse Kinematics

arXiv:2605.21061v1 Announce Type: new Abstract: Existing Driving VLAs predict trajectories while largely ignoring their visual tokens -- a phenomenon we trace not to insufficient training but to a str

Group-Aware Matrix Estimation and Latent Subspace Recovery

arXiv:2605.20559v1 Announce Type: cross Abstract: Modern matrix completion problems often involve heterogeneous data whose rows simultaneously belong to many meta-categories, such as demographic and a

GSA-YOLO: A High-Efficiency Framework via Structured Sparsity and Adaptive Knowledge Distillation for Real-Time X-ray Security Inspection

arXiv:2605.20669v1 Announce Type: new Abstract: X-ray security inspection requires accurate real-time detection of prohibited items, but existing models often struggle to balance the challenges of sev