Search: “papers”

Selector-Guided Autonomous Curriculum for One-Shot Reinforcement Learning from Verifiable Rewards

model-releasesarxiv-cs-lg

arXiv:2605.01823v1 Announce Type: new Abstract: Recently, Reinforcement Learning from Verifiable Rewards (RLVR) has been established as a highly effective technique for augmenting the math reasoning s

Safety

Semantic Risk-Aware Heuristic Planning for Robotic Navigation in Dynamic Environments: An LLM-Inspired Approach

DGX agent

arXiv:2605.02862v1 Announce Type: new Abstract: The integration of Large Language Model (LLM) reasoning principles into classical robot path planning represents a rapidly emerging research direction.

safetyarxiv-cs-ro

5 May 2026

SemEval-2026 Task 7: Everyday Knowledge Across Diverse Languages and Cultures

arXiv:2605.02601v1 Announce Type: new Abstract: We present our shared task on evaluating the adaptability of LLMs and NLP systems across multiple languages and cultures. The task data consist of an ex

Sentinel-VLA: A Metacognitive VLA Model with Active Status Monitoring for Dynamic Reasoning and Error Recovery

model-releasesarxiv-cs-ro

arXiv:2605.01191v1 Announce Type: new Abstract: Vision-language-action (VLA) models have advanced the field of embodied manipulation by harnessing broad world knowledge and strong generalization. Howe

Separation Assurance between Heterogeneous Fleets of Small Unmanned Aerial Systems via Multi-Agent Reinforcement Learning

arXiv:2605.01041v1 Announce Type: cross Abstract: In the envisioned future dense urban airspace, multiple companies will operate heterogeneous fleets of small unmanned aerial systems (sUASs), where ea

ShiftLIF: Efficient Multi-Level Spiking Neurons with Power-of-Two Quantization

arXiv:2605.01866v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) are promising for edge sensing due to their event-driven computation and temporal filtering capability. However, standa

Silicon Showdown: Performance, Efficiency, and Ecosystem Barriers in Consumer-Grade LLM Inference

model-releasesarxiv-cs-ai

arXiv:2605.00519v2 Announce Type: cross Abstract: The operational landscape of local Large Language Model (LLM) inference has shifted from lightweight models to datacenter-class weights exceeding 70B

Agents

SiriusHelper: An LLM Agent-Based Operations Assistant for Big Data Platforms

arXiv:2605.00043v1 Announce Type: cross Abstract: Big data platforms are widely used in modern enterprises, and an in-production intelligent assistant is increasingly important to help users quickly f

agentsarxiv-cs-ai

Skills as Verifiable Artifacts: A Trust Schema and a Biconditional Correctness Criterion for Human-in-the-Loop Agent Runtimes

arXiv:2605.00424v1 Announce Type: cross Abstract: Agent skills -- structured packages of instructions, scripts, and references that augment a large language model (LLM) without modifying the model its

safetyarxiv-cs-ai

Sonar-GPS Fusion for Seabed Mapping in Turbid Shallow Waters with an Autonomous Surface Vehicle

arXiv:2605.01949v1 Announce Type: cross Abstract: Accurate seabed mapping is essential for habitat monitoring and infrastructure inspection. In turbid, shallow coastal waters, such as shellfish aquacu

Applications

SPAT: A Semantic Port-Aware Adaptive-Rate Transmission Protocol for Semantic Communication

arXiv:2605.00897v1 Announce Type: cross Abstract: With the evolution of 6G, semantic communication has emerged as a promising paradigm by prioritizing the delivery of task-relevant meaning over strict

applicationsarxiv-cs-cv

SpecKV: Adaptive Speculative Decoding with Compression-Aware Gamma Selection

arXiv:2605.02888v1 Announce Type: cross Abstract: Speculative decoding accelerates large language model (LLM) inference by using a small draft model to propose candidate tokens that a larger target mo

researcharxiv-cs-cl

SRA: Span Representation Alignment for Large Language Model Distillation

arXiv:2605.01205v1 Announce Type: new Abstract: Cross-Tokenizer Knowledge Distillation (CTKD) enables knowledge transfer between a large language model and a smaller student, even when they employ dif

safetyarxiv-cs-cl

Stability of Control Lyapunov Function Guided Reinforcement Learning

arXiv:2605.01978v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become the de facto method for achieving locomotion on humanoid robots in practice, yet stability analysis of the corr

researcharxiv-cs-ro

Stable Blanket with Hidden Variables and Cycles

arXiv:2605.01856v1 Announce Type: cross Abstract: Stabilized regression aims to identify a set of predictors whose conditional relationship with a response variable remains invariant across different

StableMind: Source-Free Cross-Subject fMRI Decoding with Regularized Adaptation

arXiv:2605.02586v1 Announce Type: new Abstract: Existing cross-subject fMRI decoding methods typically train a model on multiple scanned subjects and then adapt it to a new subject using substantial p

STAR: Decode-Phase Rescheduling for LLM Inference

arXiv:2510.13668v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) inference has emerged as a fundamental paradigm, however, variations in output length cause severe workload imbalan

Statistical Consistency and Generalization of Contrastive Representation Learning

arXiv:2605.02116v1 Announce Type: new Abstract: Contrastive representation learning (CRL) underpins many modern foundation models. Despite recent theoretical progress, existing analyses suffer from se

Statistically-Lossless Quantization of Large Language Models

model-releasesarxiv-cs-lg

arXiv:2605.02404v1 Announce Type: new Abstract: Model quantization has become essential for efficient large language model deployment, yet existing approaches involve clear trade-offs: methods such as

Applications

Stochastic Modeling of Human-Machine Authentication Channels under Partial Information Leakage

arXiv:2605.02102v1 Announce Type: cross Abstract: Reliable and secure human-machine communication is fundamental to IoT and cyber-physical ecosystems, where smartphones and wearables commonly serve as

applicationsarxiv-cs-lg

StressEval: Failure-Driven Dynamic Benchmarking for Knowledge-Intensive Reasoning in Large Language Models

arXiv:2605.01939v1 Announce Type: new Abstract: Static benchmarks for LLMs are increasingly compromised by contamination and overfitting especially on knowledge intensive reasoning tasks While recent

SurGE: A Benchmark and Evaluation Framework for Scientific Survey Generation

arXiv:2508.15658v5 Announce Type: replace Abstract: The rapid growth of academic literature makes the manual creation of scientific surveys increasingly infeasible. While large language models show pr

Applications

SVGS: Enhancing Gaussian Splatting Using Primitives with Spatially Varying Colors

arXiv:2411.18966v2 Announce Type: replace Abstract: Gaussian Splatting demonstrates impressive results in multi-view reconstruction based on Gaussian explicit representations. However, the current Gau

applicationsarxiv-cs-cv

TAIL-Safe: Task-Agnostic Safety Monitoring for Imitation Learning Policies

arXiv:2605.01195v1 Announce Type: new Abstract: Recent imitation learning (IL) algorithms such as flow-matching and diffusion policies demonstrate remarkable performance in learning complex manipulati

safetyarxiv-cs-ro

Tutorials

TemPose-TF-ASF: Two-Stage Bidirectional Stroke Context Fusion for Badminton Stroke Classification

arXiv:2605.02558v1 Announce Type: new Abstract: Accurate badminton stroke prediction is crucial for fine-grained sports analysis and tactical decision support. However, existing methods struggle to mo

tutorialsarxiv-cs-cv

The Banach-Butterfly Invariant: Influence-Adaptive Walsh Geometry for Ternary Polynomial Threshold Functions

arXiv:2605.01637v1 Announce Type: new Abstract: We introduce the Banach-Butterfly Invariant (BBT), an influence-adaptive Banach geometry on the Walsh-Hadamard butterfly factorization. For a Boolean fu

The Case for ESM3 as a General-Purpose AI Model with Systemic Risk Under the EU AI Act

arXiv:2605.01611v1 Announce Type: cross Abstract: Due to ambiguity in the wording of the EU AI Act, we examine the question of to what extent frontier biological foundation models such as ESM3 are sub

The Pre-Training Study of Expanded-SPLADE Models on Web Document Titles

arXiv:2605.01407v1 Announce Type: cross Abstract: Masked Language Modeling (MLM) pre-training is one of the primary ways to initialize Neural Information Retrieval (IR) models prior to retrieval fine-

researcharxiv-cs-cl

Hardware

The Quantization Trap: Breaking Linear Scaling Laws in Multi-Hop Reasoning

arXiv:2602.13595v2 Announce Type: replace Abstract: Neural scaling laws provide a predictable recipe for AI advancement: reducing numerical precision should linearly improve computational efficiency a

hardwarearxiv-cs-ai

Thermal Imaging for Contactless Cardiorespiratory and Sudomotor Response Monitoring

arXiv:2602.12361v2 Announce Type: replace Abstract: Human-machine interfaces in industrial automation need sensing modules that monitor operator actions and physiological state. This is important in f

To Do or Not to Do: Ensuring the Safety of Visuomotor Policies Learned from Demonstrations

arXiv:2605.01201v1 Announce Type: new Abstract: Task success has historically been the primary measure of policy performance in imitation learning (IL) research. This characteristics strictly limits t

safetyarxiv-cs-ro

Towards High Fidelity Face Swapping: A Comprehensive Survey and New Benchmark

arXiv:2605.00883v1 Announce Type: new Abstract: Face swapping has witnessed significant progress in recent years, largely driven by advances in deep generative models such as GANs and diffusion models

Towards Lightest Low-Light Image Enhancement Architecture for Mobile Devices

arXiv:2507.04277v2 Announce Type: replace Abstract: Real-time low-light image enhancement on mobile and embedded devices requires models that balance visual quality and computational efficiency. Exist

Towards Visual Query Localization in the 3D World

arXiv:2605.01498v1 Announce Type: new Abstract: Visual query localization (VQL) aims to predict the spatio-temporal response of the most recent occurrence in a sequence given a query. Currently, most

Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals

arXiv:2605.00895v1 Announce Type: cross Abstract: The second-order harmonic (2f) component generated by twin-rotary compressor is a dominant low-frequency noise source of variable refrigerant flow (VR

Triple Spectral Fusion for Sensor-based Human Activity Recognition

arXiv:2605.02743v1 Announce Type: cross Abstract: The field of sensor-based human activity recognition (HAR) mainly uses posture, motion and context data of Inertial Measurement Units (IMUs) to identi

Two-Pass Zero-Shot Temporal-Spatial Grounding of Rare Traffic Events in Surveillance Video

arXiv:2605.01512v1 Announce Type: new Abstract: Grounding traffic accidents in real CCTV footage is a rare-event problem where training on labeled accident video is often prohibited, yet accurate join

UCATSC: Uncertainty-Aware Constrained Traffic Signal Control Under Vision-Based Partial Observability

arXiv:2602.07784v3 Announce Type: replace Abstract: Camera-based adaptive traffic signal control is inherently partially observable: detections can be missed, vehicle speeds and distances can be noisy

Understanding Adversarial Imitation Learning in Small Sample Regime: A Stage-coupled Analysis

arXiv:2208.01899v2 Announce Type: replace Abstract: Imitation learning learns a policy from expert trajectories. While the expert data is believed to be crucial for imitation quality, it was found tha

Unsupervised Machine Learning for Detecting Structural Anomalies in European Regional Statistics

arXiv:2605.02884v1 Announce Type: new Abstract: Ensuring the coherence of regional socio-economic statistics is a central task for national statistical institutes. Traditional validation tools, such a

Variational Matrix-Learning Fourier Networks for Parametric Multiphysics Surrogates

model-releasesarxiv-cs-lg

arXiv:2605.02280v1 Announce Type: new Abstract: Multiphysics simulation is critical for system-technology co-optimization (STCO) in chiplet-based design, but repeated finite-element solutions of PDE-g

Agents

Verbal-R3: Verbal Reranker as the Missing Bridge between Retrieval and Reasoning

arXiv:2605.01399v1 Announce Type: new Abstract: The conventional Retrieval-Augmented Generation (RAG) paradigm of injecting raw retrieved texts into the Large Language Model (LLM)'s context often resu

agentsarxiv-cs-cl

Virtual Scanning for NSCLC Histology: Investigating the Discriminatory Power of Synthetic PET

arXiv:2605.02746v1 Announce Type: new Abstract: Accurate histological differentiation between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) is critical for personalized treatment in non-small

researcharxiv-cs-cv

Visual Chart Representations for Cryptocurrency Regime Prediction: A Systematic Deep Learning Study

arXiv:2605.00875v1 Announce Type: new Abstract: Technical traders have long relied on visual analysis of candlestick charts to identify market patterns and predict price movements. While deep learning

researcharxiv-cs-cv

VLA-ATTC: Adaptive Test-Time Compute for VLA Models with Relative Action Critic Model

model-releasesarxiv-cs-ro

arXiv:2605.01194v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable capabilities and generalization in embodied manipulation. However, their decision-makin

Watermarking LLM Agent Trajectories

arXiv:2602.18700v2 Announce Type: replace-cross Abstract: LLM agents rely heavily on high-quality trajectory data to guide their problem-solving behaviors, yet producing such data requires substantial

Who Decides What Is Harmful? Content Moderation Policy Through A Multi-Agent Personalised Inference Framework

arXiv:2605.01416v1 Announce Type: cross Abstract: The increasing scale and complexity of online platforms raises critical policy questions around harmful content, digital well-being, and user autonomy

safetyarxiv-cs-cl

WILD SAM: A Simulated-and-Real Data Augmentation for Autonomous Driving Perception under Challenging Weather

arXiv:2605.01081v1 Announce Type: new Abstract: The performance of state-of-the-art object detectors degrades significantly under adverse weather, causing a safety-critical domain shift problem for au