Search: “papers”

Quantum Algorithm for Distributed Reduction of Entanglements (QADR): A Trainable and Simulation-Efficient QML Framework

arXiv:2606.01291v1 Announce Type: cross Abstract: Training Variational Quantum Circuits (VQCs) under Noisy Intermediate-Scale Quantum (NISQ) constraints introduces severe computational limitations: cl

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Research

Quantum Tunneling-Aware Machine Learning: Physics-Derived Noise Models for Robust Deployment

DGX agent

arXiv:2606.00741v1 Announce Type: cross Abstract: Transistor scaling is approaching a quantum-mechanical limit, as thin gate oxides induce electron leakage through quantum tunneling. Unlike convention

researcharxiv-cs-ai

2 Jun 2026

Hardware

Quartet II: Accurate LLM Pre-Training in NVFP4 by Improved Unbiased Gradient Estimation

arXiv:2601.22813v2 Announce Type: replace Abstract: The NVFP4 lower-precision format, supported in hardware by NVIDIA Blackwell GPUs, promises to allow, for the first time, end-to-end fully-quantized

hardwarearxiv-cs-lg

Local Ai

RA-LWLM: Retrieval-Augmented In-Context Localization with Wireless Foundation Models

arXiv:2606.01899v1 Announce Type: cross Abstract: Wireless localization is a fundamental capability of sixth-generation (6G) networks. Conventional model-based methods require accurate modeling of the

local-aiarxiv-cs-ai

Recent Advances in Multi-modal 3D Intelligence: A Comprehensive Survey and Evaluation

arXiv:2310.15676v2 Announce Type: replace-cross Abstract: Multi-modal 3D Intelligence has gained considerable attention due to its wide applications in autonomous driving and world simulation, etc. Co

Regime-Adaptive Continual Learning for Portfolio Management

arXiv:2606.00143v1 Announce Type: cross Abstract: Financial markets are inherently non-stationary, exhibiting frequent regime shifts and structural changes that render traditional Portfolio Management

Revisiting Parameter-Based Knowledge Editing in Large Language Models: Theoretical Limits and Empirical Evidence

arXiv:2606.00570v1 Announce Type: cross Abstract: Parameter-based knowledge editing updates the internal knowledge of large language models (LLMs) via localized weight modifications and has attracted

Richer Representations for Neural Algorithmic Reasoning via Auxiliary Reconstruction

arXiv:2606.00559v1 Announce Type: cross Abstract: Neural algorithmic reasoning has emerged as a popular research direction. It aims to train neural networks to mimic the step-by-step behavior of class

tutorialsarxiv-cs-ai

RPCASSM: Robust PCA State Space Model For Infrared Small Target Detection

arXiv:2606.01689v1 Announce Type: cross Abstract: The detection and segmentation of infrared small targets have important application significance in the fields of surveillance and security, maritime

Applications

Safe-Subspace Pseudo-Label Refinement for Source-Free Graph Domain Adaptation

arXiv:2606.00808v1 Announce Type: new Abstract: Source-free graph domain adaptation (SF-GDA) aims to adapt source-trained graph models to unlabeled target graphs when source graphs are no longer acces

applicationsarxiv-cs-lg

SafeSteer: Localized On-Policy Distillation for Efficient Safety Alignment

arXiv:2606.02530v1 Announce Type: new Abstract: Aligning Large Language Models (LLMs) with human values often degrades their general capabilities, termed the alignment tax. Existing methods mitigate t

Scalar-Measurement Attitude Estimation on mathbf{SO}(3) with Bias Compensation

arXiv:2603.02478v2 Announce Type: replace-cross Abstract: Attitude estimation methods typically rely on full vector measurements from inertial sensors such as accelerometers and magnetometers. This pa

safetyarxiv-cs-ro

Scaling Behavior of Single LLM-Driven Multi-Agent Systems

arXiv:2606.00655v1 Announce Type: cross Abstract: The burgeoning field of LLM-based Multi-Agent Systems (MAS) promises to tackle complex tasks through collaborative intelligence, yet fundamental quest

agentsarxiv-cs-ai

// Scaling Behavior of Single LLM-Driven Multi-Agent Systems // Does adding more agents actually make a multi-agent system better? It's poss…

// Scaling Behavior of Single LLM-Driven Multi-Agent Systems // Does adding more agents actually make a multi-agent system better? It's possible that collective intelligence emerges from interaction d

agentsdair-ai--x

SCL: Towards Domain Generalization via Single-Temporal Multimodal Contrastive Learning for Remote Sensing Change Detection

arXiv:2404.11326v5 Announce Type: replace Abstract: In recent years, change detection and anomaly detection models based on CNN and transformer have achieved remarkable success across various datasets

tutorialsarxiv-cs-cv

SECUREVENT: Hybrid AI/ML Security Monitoring for Distributed Event-Based Systems

arXiv:2606.01741v1 Announce Type: cross Abstract: Distributed event-based systems have become a common substrate for Internet-scale publish/subscribe services, IoT telemetry, cloud-native microservice

Self-Healing Agentic Orchestrators for Reliable Tool-Augmented Large Language Model Systems

arXiv:2606.01416v1 Announce Type: new Abstract: Tool-augmented large language model (LLM) agents rely on orchestration layers that coordinate planning, retrieval, tool invocation, validation, memory,

Self-Regulating Annealing in Heavy-Tailed Diffusion Models

arXiv:2606.01645v1 Announce Type: cross Abstract: Diffusion models have emerged as a leading framework for deep generative modeling. While the standard Gaussian formulation is theoretically convenient

researcharxiv-cs-lg

SEMBridge: Tagless-Final Program Semantics with Weakest-Precondition and Bounded-Checking Interpretations

arXiv:2606.00220v1 Announce Type: cross Abstract: Formal methods provide rigorous accounts of program behavior, but practical software engineering often works through executable libraries, tests, and

SentimentLens: Reconciling Sentiment and Ratings via Dual-Modality in the Hospitality Sector

arXiv:2606.00084v1 Announce Type: cross Abstract: Online travel platforms generate vast volumes of user-generated hotel reviews, offering rich opportunities to understand traveler experiences at scale

Series-Parallel Integrated Nonlinear Elastic Actuator applied to the lean motion of a bicycle simulator

arXiv:2606.00201v1 Announce Type: new Abstract: Designing robots for high-torque, high-fidelity haptic interaction is challenging. Parallel Elastic Actuators (PEAs) use elastic elements in parallel to

researcharxiv-cs-ro

Shape-Prior-Based Point Cloud Completion for Single-Stage Fully Sparse 3D Object Detection

arXiv:2606.00688v1 Announce Type: new Abstract: Single-stage fully sparse 3D object detectors rely on point clouds data to detect objects in autonomous driving scenarios. However, the sparsity and inc

safetyarxiv-cs-cv

Sharpness-Aware Hybrid Model Learning for Architecture-Agnostic Parameter Estimation

model-releasesarxiv-cs-lg

arXiv:2602.06837v2 Announce Type: replace Abstract: Hybrid modeling, the combination of machine learning models and scientific mathematical models, enables flexible and robust data-driven prediction w

Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models

arXiv:2606.02374v1 Announce Type: new Abstract: Earth Observation (EO) has fundamentally transformed the monitoring of environmental processes and human activities up to planetary scale. Recent advanc

tutorialsarxiv-cs-ai

Spatio-Temporal Reconnection for Multi-Robot Networks using Adaptive Prescribed-Time CBFs

arXiv:2606.01526v1 Announce Type: new Abstract: In multi-robot systems, maintaining persistent communication graph connectivity is often overly restrictive, especially when robots have limited communi

researcharxiv-cs-ro

SpeedAug: Policy Acceleration via Tempo-Enriched Policy and RL Fine-Tuning

arXiv:2512.00062v2 Announce Type: replace-cross Abstract: Robotic policy learning for complex real-world manipulation tasks has seen rapid recent progress, enabled in large part by the ability to coll

Stability Analysis of Sharpness-Aware Minimization

arXiv:2301.06308v2 Announce Type: replace-cross Abstract: Sharpness-aware minimization (SAM) is a training method that seeks to find flat minima in deep learning, resulting in state-of-the-art perform

STARFISH: faST Accuracy Recovery in pruned networks From Internal State Healing

arXiv:2606.01126v1 Announce Type: cross Abstract: Pruning is a process designed to reduce the number of weights in a large neural network. This can substantially speed up inference but might cause a c

// State-Externalizing Harnesses // A new paradigm is emerging on how to effectively build agents and harnesses. If there is a state that th…

// State-Externalizing Harnesses // A new paradigm is emerging on how to effectively build agents and harnesses. If there is a state that the environment can maintain reliably, it probably doesn't bel

safetydair-ai--x

STEM: Semantic Target Search and Exploration using MAVs in Cluttered Environments

arXiv:2606.00762v1 Announce Type: new Abstract: Autonomous target search is crucial for deploying Micro Aerial Vehicles (MAVs) in emergency response and rescue missions. Existing approaches either foc

agentsarxiv-cs-ro

Stop Wandering, Find the Keys: LLMs Discriminate Key States for Efficient Multi-Agent Exploration

arXiv:2410.02511v2 Announce Type: replace Abstract: With expansive state-action spaces, efficient multi-agent exploration remains a longstanding challenge in reinforcement learning. Although pursuing

agentsarxiv-cs-ai

Strategizing at Speed: A Learned Model Predictive Game for Multi-Agent Drone Racing

model-releasesarxiv-cs-ro

arXiv:2602.06925v2 Announce Type: replace Abstract: Autonomous drone racing pushes the boundaries of high-speed motion planning and multi-agent strategic decision-making. Success in this domain requir

StreamingVLM: Real-Time Understanding for Infinite Video Streams

arXiv:2510.09608v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) could power real-time assistants and autonomous agents, but they face a critical challenge: understanding near-i

Structure and Scale in Simplicial Sequence Modelling

arXiv:2606.01302v1 Announce Type: new Abstract: Modern large-scale deep learning exhibits two striking empirical phenomena: behavioural scaling laws (predictable performance gains with increasing scal

researcharxiv-cs-lg

Subliminal Learning is a LoRA Artifact

arXiv:2606.00831v1 Announce Type: new Abstract: Subliminal learning is a phenomenon where language models can transmit behavioral traits to other models through seemingly innocuous data (Cloud et al.,

SWARD: Stochastic Window-Attention-Based Relational Distillation for Cross-Architectural Semantic Segmentation

arXiv:2606.00999v1 Announce Type: new Abstract: Large-scale vision foundation models have driven substantial gains on dense prediction tasks such as semantic segmentation, but their size makes deploym

safetyarxiv-cs-cv

TabChange: Precise Attribute Changes in Tabular Data

arXiv:2606.00503v1 Announce Type: cross Abstract: Modifying an attribute in tabular data often introduces an unnatural instance by breaking its relationships with other attributes. The modified instan

Taming System Complexity: Demystifying Software Engineering Agents in Diagnosing Linux Kernel Faults

arXiv:2505.19489v2 Announce Type: replace Abstract: The Linux kernel is a critical system, serving as the foundation for numerous systems. Bugs in the Linux kernel can cause serious consequences, affe

The Alignment Curse: Modality Alignment Supercharges Audio Attacks via Text Transfer

arXiv:2602.02557v2 Announce Type: replace-cross Abstract: Recent advances in end-to-end trained omni-models have substantially improved audio capabilities by strengthening text-audio modality alignmen

The Geometry of Grokking: Norm Minimization on the Zero-Loss Manifold

arXiv:2511.01938v3 Announce Type: replace-cross Abstract: Grokking is a puzzling phenomenon in neural networks where full generalization occurs only after a substantial delay following the complete me

The Role of Ambiguity in Error Prediction via Uncertainty Quantification

arXiv:2606.02093v1 Announce Type: cross Abstract: The task of Error Prediction, namely predicting whether a model output is correct, is commonly tackled with Uncertainty Quantification (UQ). However,

Theoretical Analysis of Engression and Reverse Markov Engression

arXiv:2606.01002v1 Announce Type: cross Abstract: Engression is a recently proposed and effective framework for conditional distribution learning. Its multi-step Reverse Markov extension further impro

researcharxiv-cs-lg

Time-Optimal Collision Avoidance Via a Greedy Polynomial Backward Sweep

model-releasesarxiv-cs-ro

arXiv:2606.01169v1 Announce Type: cross Abstract: Spacecraft collision avoidance for low-thrust satellites often requires determining not only how to maneuver, but also how late a maneuver can begin w

Towards Interactive Video World Modeling: Frontiers, Challenges, Benchmarks, and Future Trends

arXiv:2606.01164v1 Announce Type: new Abstract: With rapid development of large language models and diffusion-based content generation, world modeling has attracted increasing research attention, bene

agentsarxiv-cs-cv

Towards Optimal Robustness in Learning-Augmented Paging

arXiv:2606.01342v1 Announce Type: cross Abstract: Learning-augmented paging has been extensively studied in recent years. A key advantage over naive ML-based approaches is bounded robustness, which gu

tutorialsarxiv-cs-lg

Towards Resolving Optimization Conflicts Between Image- and Text-Based Person Re-Identification

arXiv:2606.02242v1 Announce Type: cross Abstract: The joint optimization of image-based (I2I) and text-based (T2I) person re-identification (ReID) is hindered by modality discrepancies and conflicting

Towards Simple and Provable Parameter-Free Adaptive Gradient Methods

model-releasesarxiv-cs-lg

arXiv:2412.19444v2 Announce Type: replace Abstract: Optimization algorithms such as AdaGrad and Adam have significantly advanced the training of deep models by dynamically adjusting the learning rate

Towards Stable, Globally Expressive Graph Representations with Laplacian Eigenvectors

arXiv:2410.09737v2 Announce Type: replace Abstract: A popular way to improve the expressive power of graph neural networks (GNNs) is to use Laplacian eigenvectors as additional node features, since th

tutorialsarxiv-cs-lg