Search: “papers”

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers

arXiv:2405.13901v4 Announce Type: replace Abstract: Self-attention is central to the success of Transformer architectures; however, learning the query, key, and value projections from random initializ

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Distance metric learning for conditional anomaly detection

DGX agent

arXiv:2605.00490v1 Announce Type: new Abstract: Anomaly detection methods can be very useful in identifying unusual or interesting patterns in data. A recently proposed conditional anomaly detection f

researcharxiv-cs-lg

4 May 2026

DMDSC: A Dynamic-Margin Deep Simplex Classifier for Open-Set Recognition on Medical Image Datasets

arXiv:2605.00675v1 Announce Type: new Abstract: Medical imaging datasets are often characterized by extreme class imbalances, where rare pathologies are significantly underrepresented compared to comm

Do Open-Loop Metrics Predict Closed-Loop Driving? A Cross-Benchmark Correlation Study of NAVSIM and Bench2Drive

model-releasesarxiv-cs-ro

arXiv:2605.00066v1 Announce Type: new Abstract: Open-loop evaluation offers fast, reproducible assessment of autonomous driving planners, but its ability to predict real closed-loop driving performanc

Hardware

DPU or GPU for Accelerating Neural Networks Inference -- Why not both? Split CNN Inference

arXiv:2605.00174v1 Announce Type: cross Abstract: Video and image streaming on edge devices requires low latency. To address this, Neural Networks (NNs) are widely used, and prior work mainly focuses

hardwarearxiv-cs-cv

Efficient Spatio-Temporal Vegetation Pixel Classification with Vision Transformers

model-releasesarxiv-cs-cv

arXiv:2605.00296v1 Announce Type: new Abstract: Plant phenology-the study of recurrent life cycle events-is essential for understanding ecosystem dynamics and their responses to climate change impacts

Local Ai

EGREFINE: An Execution-Grounded Optimization Framework for Text-to-SQL Schema Refinement

arXiv:2605.00628v1 Announce Type: cross Abstract: Text-to-SQL enables non-expert users to query databases in natural language, yet real-world schemas often suffer from ambiguous, abbreviated, or incon

local-aiarxiv-cs-cl

Evaluating the Architectural Reasoning Capabilities of LLM Provers via the Obfuscated Natural Number Game

arXiv:2605.00677v1 Announce Type: new Abstract: While Large Language Models have achieved notable success on formal mathematics benchmarks such as MiniF2F, it remains unclear whether these results ste

Exploring the Limits of End-to-End Feature-Affinity Propagation for Single-Point Supervised Infrared Small Target Detection

arXiv:2605.00722v1 Announce Type: new Abstract: Single-point supervised infrared small target detection (IRSTD) drastically reduces dense annotation costs. Current state-of-the-art (SOTA) methods achi

From Backward Spreading to Forward Replay: Revisiting Target Construction in LLM Parameter Editing

arXiv:2605.00358v1 Announce Type: new Abstract: LLM parameter editing methods commonly rely on computing an ideal target hidden-state at a target layer (referred as anchor point) and distributing the

Generative Modeling under Non-Monotone MAR Missingness via Approximate Wasserstein Gradient Flows

arXiv:2604.04567v2 Announce Type: replace-cross Abstract: The prevalence of missing values in data science poses a substantial risk to any further analyses. Despite a wealth of research, principled no

Geometric analysis of attractor boundaries and storage capacity limits in kernel Hopfield networks

arXiv:2605.00366v1 Announce Type: cross Abstract: High-capacity associative memories based on Kernel Logistic Regression (KLR) exhibit strong storage capabilities, but the dynamical and geometric mech

applicationsarxiv-cs-lg

GOR-IS: 3D Gaussian Object Removal in the Intrinsic Space

arXiv:2605.00498v1 Announce Type: new Abstract: Recent advances in Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have made it standard practice to reconstruct 3D scenes from multi-vie

applicationsarxiv-cs-cv

Gradient Regularized Newton Boosting Trees with Global Convergence

arXiv:2605.00581v1 Announce Type: cross Abstract: Gradient Boosting Decision Trees (GBDTs) dominate tabular machine learning, with modern implementations like XGBoost, LightGBM, and CatBoost being bas

High-Probability Convergence in Decentralized Stochastic Optimization with Gradient Tracking

arXiv:2605.00281v1 Announce Type: new Abstract: We study high-probability (HP) convergence guarantees in decentralized stochastic optimization, where multiple agents collaborate to jointly train a mod

How Well Does GPT-4o Understand Vision? Evaluating Multimodal Foundation Models on Standard Computer Vision Tasks

model-releasesarxiv-cs-cv

arXiv:2507.01955v3 Announce Type: replace Abstract: Multimodal foundation models (MFMs), such as GPT-4o, have recently made remarkable progress. However, their detailed visual understanding beyond que

Human-in-the-Loop Meta Bayesian Optimization for Fusion Energy and Scientific Applications

arXiv:2605.00068v1 Announce Type: new Abstract: Inertial Confinement Fusion (ICF) holds transformative promise for sustainable, near-limitless clean energy, yet remains constrained by prohibitively hi

Information-geometric adaptive sampling for graph diffusion

arXiv:2605.00250v1 Announce Type: cross Abstract: Standard diffusion models for graph generation typically rely on uniform time-stepping, an approach that overlooks the non-homogeneous dynamics of dis

InpaintSLat: Inpainting Structured 3D Latents via Initial Noise Optimization

arXiv:2605.00664v1 Announce Type: new Abstract: We present a training-free approach for controllable 3D inpainting based on initial noise optimization. In the structured 3D latent diffusion framework,

safetyarxiv-cs-cv

Introducing WARM-VR: Benchmark Dataset for Multimodal Wearable Affect Recognition in Virtual Reality

arXiv:2605.00184v1 Announce Type: new Abstract: With the growing integration of human-computer interaction into everyday life, advances in machine learning have enabled systems to better perceive and

Koopman-Assisted Reinforcement Learning

arXiv:2403.02290v2 Announce Type: replace-cross Abstract: The Bellman equation and its continuous form, the Hamilton-Jacobi-Bellman equation, are ubiquitous in reinforcement learning and control theor

Last-Iterate Analyses of FTRL with the 1/2-Tsallis Entropy in Stochastic Bandits

arXiv:2510.22819v2 Announce Type: replace Abstract: The convergence analysis of online learning algorithms is central to machine learning theory, where the last-iterate convergence is particularly imp

Learn where to Click from Yourself: On-Policy Self-Distillation for GUI Grounding

arXiv:2605.00642v1 Announce Type: cross Abstract: Graphical User Interface (GUI) grounding maps natural language instructions to the visual coordinates of target elements and serves as a core capabili

safetyarxiv-cs-cv

Lightweight Domain Adaptation of a Large Language Model for Legal Assistance in the Indian Context

arXiv:2505.22003v2 Announce Type: replace Abstract: In India, access to legal assistance for the general public has been observed to have a critical gap, as many citizens are not able to take full adv

LIMSSR: LLM-Driven Sequence-to-Score Reasoning under Training-Time Incomplete Multimodal Observations

arXiv:2605.00434v1 Announce Type: new Abstract: Real-world multimodal learning is often hindered by missing modalities. While Incomplete Multimodal Learning (IML) has gained traction, existing methods

applicationsarxiv-cs-cv

LLM-Oriented Information Retrieval: A Denoising-First Perspective

arXiv:2605.00505v1 Announce Type: cross Abstract: Modern information retrieval (IR) is no longer consumed primarily by humans but increasingly by large language models (LLMs) via retrieval-augmented g

Agents

Memory in the LLM Era: Modular Architectures and Strategies in a Unified Framework

arXiv:2604.01707v2 Announce Type: replace Abstract: Memory emerges as the core module in the large language model (LLM)-based agents for long-horizon complex tasks (e.g., multi-turn dialogue, game pla

agentsarxiv-cs-cl

MMAudioReverbs: Video-Guided Acoustic Modeling for Dereverberation and Room Impulse Response Estimation

arXiv:2605.00431v1 Announce Type: cross Abstract: Although recent video-to-audio (V2A) models excelled at synthesizing semantically plausible sounds from visual inputs, they do not explicitly model ro

Model-Based Reinforcement Learning with Double Oracle Efficiency in Policy Optimization and Offline Estimation

arXiv:2605.00393v1 Announce Type: new Abstract: Reinforcement learning (RL) in large environments often suffers from severe computational bottlenecks, as conventional regret minimization algorithms re

safetyarxiv-cs-lg

Modeling Subjective Urban Perception with Human Gaze

arXiv:2605.00764v1 Announce Type: new Abstract: Urban perception describes how people subjectively evaluate urban environments, shaping how cities are experienced and understood. Existing computationa

Near-optimal and Efficient First-Order Algorithm for Multi-Task Learning with Shared Linear Representation

arXiv:2605.00473v1 Announce Type: new Abstract: Multi-task learning (MTL) has emerged as a pivotal paradigm in machine learning by leveraging shared structures across multiple related tasks. Despite i

Agents

Physically Native World Models: A Hamiltonian Perspective on Generative World Modeling

arXiv:2605.00412v1 Announce Type: cross Abstract: World models have recently re-emerged as a central paradigm for embodied intelligence, robotics, autonomous driving, and model-based reinforcement lea

agentsarxiv-cs-ro

PhysiGen: Integrating Collision-Aware Physical Constraints for High-Fidelity Human-Human Interaction Generation

arXiv:2605.00517v1 Announce Type: new Abstract: Despite substantial progress in text-driven 3D human motion synthesis, generating realistic multi-person interaction sequences remains challenging. Nota

PORTool: Importance-Aware Policy Optimization with Rewarded Tree for Multi-Tool-Integrated Reasoning

arXiv:2510.26020v2 Announce Type: replace Abstract: Multi-tool-integrated reasoning enables LLM-empowered tool-use agents to solve complex tasks by interleaving natural-language reasoning with calls t

safetyarxiv-cs-cl

PPLLaVA: Varied Video Sequence Understanding With Prompt Guidance

arXiv:2411.02327v4 Announce Type: replace Abstract: In the past year, video-based large language models (Video LLMs) have achieved impressive progress, particularly in their ability to process long vi

safetyarxiv-cs-cv

Probing Multimodal Large Language Models on Cognitive Biases in Chinese Short-Video Misinformation

arXiv:2601.06600v2 Announce Type: replace Abstract: Short-video platforms have become major channels for misinformation, where deceptive claims frequently leverage visual experiments and social cues.

Quantum Interval Bound Propagation for Certified Training of Quantum Neural Networks

arXiv:2605.00747v1 Announce Type: cross Abstract: Quantum machine learning is a promising field for efficiently learning features of a dataset to perform a specified task, such as classification. Inte

Representation in large language models

arXiv:2501.00885v2 Announce Type: replace Abstract: The extraordinary success of recent Large Language Models (LLMs) on a diverse array of tasks has led to an explosion of scientific and philosophical

researcharxiv-cs-cl

ResRL: Boosting LLM Reasoning via Negative Sample Projection Residual Reinforcement Learning

arXiv:2605.00380v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) enhances reasoning of Large Language Models (LLMs) but usually exhibits limited generation diver

safetyarxiv-cs-cl

Rethinking LLM Ensembling from the Perspective of Mixture Models

arXiv:2605.00419v1 Announce Type: cross Abstract: Model ensembling is a well-established technique for improving the performance of machine learning models. Conventionally, this involves averaging the

researcharxiv-cs-cl

Revealing graph bandits for maximizing local influence

arXiv:2605.00489v1 Announce Type: new Abstract: We study a graph bandit setting where the objective of the learner is to detect the most influential node of a graph by requesting as little information

Reward Modeling from Natural Language Human Feedback

arXiv:2601.07349v3 Announce Type: replace Abstract: Reinforcement Learning with Verifiable reward (RLVR) on preference data has become the mainstream approach for training Generative Reward Models (GR

researcharxiv-cs-cl

Scaling Federated Linear Contextual Bandits via Sketching

arXiv:2605.00500v1 Announce Type: new Abstract: In federated contextual linear bandits, high data dimensionality incurs prohibitive computation and communication costs: local agents perform O(d^3)-tim

applicationsarxiv-cs-lg

Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification

arXiv:2605.00218v1 Announce Type: cross Abstract: Mobile remote identity verification (RIdV) systems are exposed to attacks that manipulate or replace the facial video stream, including presentation a

Smart Profit-Aware Crop Advisory System: Kisan AI

arXiv:2605.00133v1 Announce Type: new Abstract: Modern crop advisory systems exhibit a critical limitation termed extit{economic blindness}. These systems primarily optimize for biological yield, ofte

Soft Graph Diffusion Transformer for MIMO Detection

arXiv:2605.00449v1 Announce Type: cross Abstract: Learning-based MIMO detection has shown strong empirical performance, yet existing methods typically rely on fixed-depth architectures without explici

safetyarxiv-cs-lg

Sparse VideoGen2: Accelerate Video Generation with Sparse Attention via Semantic-Aware Permutation

arXiv:2505.18875v4 Announce Type: replace Abstract: Diffusion Transformers (DiTs) are essential for video generation but suffer from significant latency due to the quadratic complexity of attention. B

Statistical Impossibility and Possibility of Aligning LLMs with Human Preferences: From Condorcet Paradox to Nash Equilibrium

arXiv:2503.10990v2 Announce Type: replace-cross Abstract: Aligning large language models (LLMs) with diverse human preferences is critical for ensuring fairness and informed outcomes when deploying th

safetyarxiv-cs-lg