Search: “research”

ReasonEdit: Editing Vision-Language Models using Human Reasoning

arXiv:2602.02408v4 Announce Type: replace Abstract: Model editing aims to correct errors in large, pretrained models without altering unrelated behaviors. While some recent works have edited vision-la

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Regret minimization in Linear Bandits with offline data via extended D-optimal exploration

DGX agent

arXiv:2508.08420v3 Announce Type: replace Abstract: We consider the problem of online regret minimization in linear bandits with access to prior observations (offline data) from the underlying bandit

researcharxiv-cs-lg

13 May 2026

Remarks on stochastic cloning and delayed-state filtering

arXiv:2508.21260v2 Announce Type: replace Abstract: Many estimation problems in aerospace navigation and robotics involve measurements that depend on prior states. A prominent example is odometry, whi

RETUYT-INCO at BEA 2026 Shared Task 2: Meta-prompting in Rubric-based Scoring for German

arXiv:2605.11242v1 Announce Type: new Abstract: In this paper, we present the RETUYT-INCO participation at the BEA 2026 shared task 'Rubric-based Short Answer Scoring for German'. Our team participate

RevealLayer: Disentangling Hidden and Visible Layers via Occlusion-Aware Image Decomposition

arXiv:2605.11818v1 Announce Type: new Abstract: Recent diffusion-based approaches have made substantial progress in image layer decomposition. However, accurately decomposing complex natural images re

ReVision: Scaling Computer-Use Agents via Temporal Visual Redundancy Reduction

arXiv:2605.11212v1 Announce Type: new Abstract: Computer-use agents~(CUAs) rely on visual observations of graphical user interfaces, where each screenshot is encoded into a large number of visual toke

Revisiting GAN with Bayes-Optimal Discrimination

arXiv:2510.25609v3 Announce Type: replace Abstract: We propose an alternative to the standard GAN training approach, in which the discriminator is a binary classifier trained by cross-entropy to disti

RNA-FM: Flow-Matching Generative Model for Genome-wide RNA-Seq Prediction

arXiv:2605.11622v1 Announce Type: new Abstract: Histopathology whole-slide images (WSIs) are routinely acquired in clinical practice and contain rich tissue morphology but lack direct molecular archit

Robust Biomedical Publication Type and Study Design Classification with Knowledge-Guided Perturbations

arXiv:2605.11502v1 Announce Type: new Abstract: Accurately and consistently indexing biomedical literature by publication type and study design is essential for supporting evidence synthesis and knowl

Rollbot: a Spherical Robot Driven by a Single Actuator

arXiv:2404.05120v2 Announce Type: replace Abstract: Spherical robots typically require at least two actuators to achieve controlled 2D planar motion. Here we present Rollbot, the first spherical robot

Rotary Masked Autoencoders are Versatile Learners

arXiv:2505.20535v3 Announce Type: replace Abstract: Applying Transformers to irregular time-series typically requires specializations to their baseline architecture, which can result in additional com

Rotation-Preserving Supervised Fine-Tuning

arXiv:2605.10973v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) improves in-domain performance but can degrade out-of-domain (OOD) generalization. Prior work suggests that this degradatio

RT-Transformer: The Transformer Block as a Spherical State Estimator

arXiv:2605.11007v1 Announce Type: new Abstract: We show that the core components of the Transformer block -- attention, residual connections, and normalization -- arise naturally from a single geometr

Sampling-Based Follow-the-Leader Motion Planning for Manipulator-Mounted Continuum Robots

arXiv:2605.11618v1 Announce Type: new Abstract: Follow-the-leader (FTL) motion exploits the unique morphology of continuum robots (CRs) to navigate confined spaces by having the body retrace the path

Scalable Token-Level Hallucination Detection in Large Language Models

arXiv:2605.12384v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable capabilities, but they still frequently produce hallucinations. These hallucinations are diffi

ScaleMoGen: Autoregressive Next-Scale Prediction for Human Motion Generation

arXiv:2605.11704v1 Announce Type: new Abstract: We present ScaleMoGen, a scale-wise autoregressive framework for text-driven human motion generation. Unlike conventional autoregressive approaches that

ScribbleDose: Scribble-Guided Dose Prediction in Radiotherapy

arXiv:2605.11555v1 Announce Type: new Abstract: Anatomical structure masks are widely adopted in radiotherapy dose prediction, as they provide explicit geometric constraints that facilitate structure-

See the past: Time-Reversed Scene Reconstruction from Thermal Traces Using Visual Language Models

arXiv:2510.05408v2 Announce Type: replace Abstract: Recovering the past from present observations is an intriguing challenge with potential applications in forensics and scene analysis. Thermal imagin

Self-Distilled Trajectory-Aware Boltzmann Modeling: Bridging the Training-Inference Discrepancy in Diffusion Language Models

arXiv:2605.11854v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) have recently emerged as a promising alternative to autoregressive language models, offering stronger global awareness

Semi-Supervised Bayesian GANs with Log-Signatures for Uncertainty-Aware Credit Card Fraud Detection

arXiv:2509.00931v3 Announce Type: replace-cross Abstract: We present a novel deep generative semi-supervised framework for credit card fraud detection, formulated as time series classification task. A

Shapley Value Approximation Based on k-Additive Games

arXiv:2502.04763v2 Announce Type: replace-cross Abstract: The Shapley value is the prevalent solution for fair division problems in which a payout is to be divided among multiple agents. By adopting a

ShardTensor: Domain Parallelism for Scientific Machine Learning

arXiv:2605.11111v1 Announce Type: cross Abstract: Scientific Machine Learning (SciML) faces unique challenges for extreme-resolution data, with mitigations that often fail to scale or degrade the accu

Single-Shot HDR Recovery via a Video Diffusion Prior

arXiv:2605.11628v1 Announce Type: new Abstract: Recent generative methods for single-shot high dynamic range (HDR) image reconstruction show promising results, but often struggle with preserving fidel

Smooth-Rigid-Body Contact as a ReLCP: A Recursively Generated Linear Complementarity Problem

arXiv:2506.14097v2 Announce Type: replace Abstract: This paper reformulates complementarity-based time-stepping for frictionless nonsmooth contact between smooth rigid bodies as a recursively generate

SOAR: Scale Optimization for Accurate Reconstruction in NVFP4 Quantization

arXiv:2605.12245v1 Announce Type: new Abstract: NVFP4 has recently emerged as an efficient 4-bit microscaling format for large language models (LLMs), offering superior numerical fidelity with native

Sobolev Regularized MMD Gradient Flow

arXiv:2605.11884v1 Announce Type: new Abstract: We propose Sobolev-regularized Maximum Mean Discrepancy (SrMMD) gradient flow, a regularized variant of maximum mean discrepancy (MMD) gradient flow bas

SoK: Unlearnability and Unlearning for Model Dememorization

arXiv:2605.11592v1 Announce Type: new Abstract: Advanced model dememorization methods, including availability poisoning (unlearnability) and machine unlearning, are emerging as key safeguards against

Sparse Attention Remapping with Clustering for Efficient LLM Decoding on PIM

arXiv:2505.05772v2 Announce Type: replace Abstract: Transformer-based models are the foundation of modern machine learning, but their execution, particularly during autoregressive decoding in large la

SpatialForge: Bootstrapping 3D-Aware Spatial Reasoning from Open-World 2D Images

arXiv:2605.11462v1 Announce Type: new Abstract: Recent advancements in Large Vision-Language Models (VLMs) have demonstrated exceptional semantic understanding, yet these models consistently struggle

Spectral Vision Transformer for Efficient Tokenization with Limited Data

arXiv:2605.12026v1 Announce Type: new Abstract: We propose a novel spectral vision transformer architecture for efficient tokenization in limited data, with an emphasis on medical imaging. We outline

Split the Differences, Pool the Rest: Provably Efficient Multi-Objective Imitation

arXiv:2605.12000v1 Announce Type: new Abstract: This work investigates multi-objective imitation learning: the problem of recovering policies that lie on the Pareto front given demonstrations from mul

SRG: Score-based Relaxation-guided Generation for Mixed Integer Linear Programming

arXiv:2603.24033v2 Announce Type: replace Abstract: We propose Score-based Relaxation-guided Generation (SRG), a generative framework based on an approximate formulation of relaxation-guided stochasti

Stationary MMD Points

arXiv:2505.20754v3 Announce Type: replace-cross Abstract: Approximation of a target probability distribution using a finite set of points is a problem of fundamental importance in numerical integratio

Steerable Neural ODEs on Homogeneous Spaces

arXiv:2605.11133v1 Announce Type: new Abstract: We introduce steerable neural ordinary differential equations on homogeneous spaces M=G/H. These models constitute a novel geometric extension of manifo

Steering Without Breaking: Mechanistically Informed Interventions for Discrete Diffusion Language Models

arXiv:2605.10971v1 Announce Type: cross Abstract: Discrete diffusion language models (DLMs) generate text by iteratively denoising all positions in parallel, offering an alternative to autoregressive

Stop Marginalizing My Dreams: Model Inversion via Laplace Kernel for Continual Learning

arXiv:2605.11804v1 Announce Type: cross Abstract: Data-free continual learning (DFCIL) relies on model inversion to synthesize pseudo-samples and mitigate catastrophic forgetting. However, existing in

Stopping Computation for Converged Tokens in Masked Diffusion-LM Decoding

arXiv:2602.06412v3 Announce Type: replace Abstract: Masked Diffusion Language Models generate sequences via iterative sampling that progressively unmasks tokens. However, they still recompute the atte

Stories in Space: In-Context Learning Trajectories in Conceptual Belief Space

arXiv:2605.12412v1 Announce Type: new Abstract: Large Language Models (LLMs) update their behavior in context, which can be viewed as a form of Bayesian inference. However, the structure of the latent

Streaming of rendered content with adaptive frame rate and resolution

arXiv:2605.10995v1 Announce Type: cross Abstract: Streaming rendered content is an attractive way to bring high-quality graphics to billions of mobile devices that do not have sufficient rendering pow

SurvBench: A Standardised Preprocessing Pipeline for Multi-Modal Electronic Health Record Survival Analysis

arXiv:2511.11935v2 Announce Type: replace Abstract: Deep-learning survival models for electronic health record (EHR) data are hard to compare across papers because the upstream preprocessing step, whi

Tackling Fake Forgetting through Uncertainty Quantification

arXiv:2501.19403v3 Announce Type: replace Abstract: Machine unlearning seeks to remove the influence of specified data from a trained model. While the unlearning accuracy provides a widely used metric

Taking the Road Less Scheduled with Adaptive Polyak Steps

arXiv:2511.07767v2 Announce Type: replace Abstract: Schedule-Free SGD, proposed in [Defazio et al., 2024], achieves optimal convergence rates without requiring the training horizon in advance, by repl

Taming Score-Based Denoisers in ADMM: A Convergent Plug-and-Play Framework

arXiv:2603.10281v3 Announce Type: replace-cross Abstract: While score-based generative models have emerged as powerful priors for solving inverse problems, directly integrating them into optimization

Targeted Tests for LLM Reasoning: An Audit-Constrained Protocol

arXiv:2605.11599v1 Announce Type: new Abstract: Fixed reasoning benchmarks evaluate canonical prompts, but semantically valid changes in presentation can still change model behavior. Studies of prompt

Task-Adaptive Embedding Refinement via Test-time LLM Guidance

arXiv:2605.12487v1 Announce Type: new Abstract: We explore the effectiveness of an LLM-guided query refinement paradigm for extending the usability of embedding models to challenging zero-shot search

TCP-SSM: Efficient Vision State Space Models with Token-Conditioned Poles

arXiv:2605.11563v1 Announce Type: new Abstract: State Space Models (SSMs) have emerged as a compelling alternative to attention models for long-range vision tasks, offering input-dependent recurrence

Testing General Relativity Through Gravitational Wave Classification: A Convolutional Neural Network Framework

arXiv:2605.02453v1 Announce Type: cross Abstract: We present a machine learning framework for testing general relativity (GR) with gravitational wave signals from binary black hole mergers. Using the

The Algorithmic Caricature: Auditing LLM-Generated Political Discourse Across Crisis Events

arXiv:2605.12452v1 Announce Type: new Abstract: Large Language Models (LLMs) can generate fluent political text at scale, raising concerns about synthetic discourse during crises and social conflict.