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Physiologically Grounded Driver Behavior Classification: SHAP-Driven Elite Feature Selection and Hybrid Gradient Boosting for Multimodal Physiological Signals

arXiv:2605.05120v1 Announce Type: new Abstract: An interpretable and scalable framework for decoding driving behaviors from multimodal physiological signals is proposed in this study. We utilize multi

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Tools

Pin your most commonly used skills as quick-action pills for easy access.

DGX agent

Cursor, an AI code editor, introduced a feature allowing users to pin frequently-used skills or commands as quick-action pills for faster access within the interface. This enhancement improves workflo

toolscursor--x

7 May 2026

Piper: Efficient Large-Scale MoE Training via Resource Modeling and Pipelined Hybrid Parallelism

arXiv:2605.05049v1 Announce Type: cross Abstract: Frontier models increasingly adopt Mixture-of-Experts (MoE) architectures to achieve large-model performance at reduced cost. However, training MoE mo

hardwarearxiv-cs-lg

POMA-3D: The Point Map Way to 3D Scene Understanding

arXiv:2511.16567v3 Announce Type: replace Abstract: In this paper, we introduce POMA-3D, the first self-supervised 3D representation model learned from point maps. Point maps encode explicit 3D coordi

safetyarxiv-cs-cv

Position: Agent Should Invoke External Tools ONLY When Epistemically Necessary

arXiv:2506.00886v3 Announce Type: replace Abstract: As large language models evolve into tool-augmented agents, a central question remains unresolved: when is external tool use actually justified? Exi

agentsarxiv-cs-ai

Local Ai

Position: Embodied AI Requires a Privacy-Utility Trade-off

arXiv:2605.05017v1 Announce Type: cross Abstract: Embodied AI (EAI) systems are rapidly transitioning from simulations into real-world domestic and other sensitive environments. However, recent EAI so

local-aiarxiv-cs-ro

Position: the Stochastic Parrot in the Coal Mine. Model Collapse is a Threat to Low-Resource Communities

arXiv:2605.04127v1 Announce Type: cross Abstract: Model collapse, the degradation in performance that arises when generative models are trained on the outputs of prior models, is an increasing concern

researcharxiv-cs-cl

Positional Encoding in Transformer-Based Time Series Models: A Survey

arXiv:2502.12370v3 Announce Type: replace Abstract: Recent advancements in transformer-based models have greatly improved time series analysis, providing robust solutions for tasks such as forecasting

Local Ai

Power Distribution Bridges Sampling, Self-Reward RL, and Self-Distillation

arXiv:2605.04542v1 Announce Type: new Abstract: Recent analyses question whether reinforcement learning (RL) is responsible for strong reasoning in large language models (LLMs). At the same time, dist

local-aiarxiv-cs-lg

Powering the Next American Century: US Energy Secretary Chris Wright and NVIDIA’s Ian Buck on the Genesis Mission

AI will help build the energy it needs. That’s the case U.S. Energy Secretary Chris Wright and NVIDIA Vice President of Hyperscale and High-Performance Computing Ian Buck made Thursday morning at the

hardwarenvidia-blog

Practical validation of synthetic pre-crash scenarios

arXiv:2605.04564v1 Announce Type: new Abstract: The representativeness of synthetic pre-crash scenarios is crucial for assessing the safety impact of Driving Automation Systems through virtual simulat

safetyarxiv-cs-ro

Predict-then-Diffuse: Adaptive Response Length for Compute-Budgeted Inference in Diffusion LLMs

arXiv:2605.04215v1 Announce Type: new Abstract: Diffusion-based Large Language Models (D-LLMs) represent a promising frontier in generative AI, offering fully parallel token generation that can lead t

safetyarxiv-cs-lg

Predictive and Prescriptive AI toward Optimizing Wildfire Suppression

arXiv:2605.04510v1 Announce Type: cross Abstract: Intense wildfire seasons require critical prioritization decisions to allocate scarce suppression resources over a dispersed geographical area. This p

applicationsarxiv-cs-lg

Preference-Based Self-Distillation: Beyond KL Matching via Reward Regularization

arXiv:2605.05040v1 Announce Type: new Abstract: On-policy distillation is an efficient alternative to reinforcement learning, offering dense token-level training signals. However, its reliance on a st

safetyarxiv-cs-lg

PRISM: Color-Stratified Point Cloud Sampling

arXiv:2601.06839v2 Announce Type: replace Abstract: We present PRISM, a novel color-guided stratified sampling method for RGB-LiDAR point clouds. Our approach is motivated by the observation that uniq

Privacy-Preserving Empathy Detection in Video Interactions

model-releasesarxiv-cs-cv

arXiv:2504.10808v3 Announce Type: replace Abstract: Detecting empathy from video interactions has emerging applications, yet raw videos that could be used for training AI models are rarely available d

Probabilistic Classification and Uncertainty Quantification of Sahara Desert Climate Using Feedforward Neural Networks

arXiv:2605.04286v1 Announce Type: new Abstract: Climate classification plays a vital role in agricultural planning, hydrological studies, and climate science. One of the most widely used systems for c

Probing Structural Mathematical Reasoning in Language Models with Algebraic Trapdoors

model-releasesarxiv-cs-lg

arXiv:2605.04352v1 Announce Type: new Abstract: We introduce a benchmark suite for evaluating structural mathematical reasoning in language models, built on subgroup-construction problems in SL(3, Z)

ProFit: Leveraging High-Value Signals in SFT via Probability-Guided Token Selection

arXiv:2601.09195v3 Announce Type: replace Abstract: Supervised fine-tuning (SFT) is a fundamental post-training strategy to align Large Language Models (LLMs) with human intent. However, traditional S

safetyarxiv-cs-cl

ProgramBench: Can Language Models Rebuild Programs From Scratch?

arXiv:2605.03546v1 Announce Type: cross Abstract: Turning ideas into full software projects from scratch has become a popular use case for language models. Agents are being deployed to seed, maintain,

agentsarxiv-cs-ai

Programmatic Context Augmentation for LLM-based Symbolic Regression

arXiv:2605.03101v1 Announce Type: new Abstract: Symbolic regression (SR), the task of discovering mathematical expressions that best describe a given dataset, remains a fundamental challenge in scient

researcharxiv-cs-ai

Progressive J-Invariant Self-supervised Learning for Low-Dose CT Denoising

arXiv:2601.14180v3 Announce Type: replace Abstract: Self-supervised learning has been increasingly investigated for low-dose computed tomography (LDCT) image denoising, as it alleviates the dependence

ProMediate: A Socio-cognitive framework for evaluating proactive agents in multi-party negotiation

arXiv:2510.25224v3 Announce Type: replace Abstract: While Large Language Models (LLMs) are increasingly used in agentic frameworks to assist individual users, there is a growing need for agents that c

agentsarxiv-cs-cl

Prompt-Anchored Vision-Text Distillation for Lifelong Person Re-identification

model-releasesarxiv-cs-cv

arXiv:2605.05027v1 Announce Type: new Abstract: Lifelong person re-identification (LReID) aims to train a generalizable model with sequentially collected data. However, such models often suffer from s

Provable imitation learning for control of instability in partially-observed Vlasov--Poisson equations

arXiv:2605.05081v1 Announce Type: new Abstract: We consider the stabilization of Vlasov--Poisson plasma dynamics, a central control problem in nuclear fusion. Our focus is the gap between what an idea

safetyarxiv-cs-lg

Provable Non-Convex Euclidean Distance Matrix Completion: Geometry, Reconstruction, and Robustness

model-releasesarxiv-cs-lg

arXiv:2508.00091v3 Announce Type: replace-cross Abstract: The problem of recovering the configuration of points from their partial pairwise distances, referred to as the Euclidean Distance Matrix Comp

Proximal Projection for Doubly Sparse Regularized Models

arXiv:2605.05093v1 Announce Type: cross Abstract: Regularization is often used in high-dimensional regression settings to generate a sparse model, which can save tremendous computing resources and ide

applicationsarxiv-cs-lg

PSK at SemEval-2026 Task 9: Multilingual Polarization Detection Using Ensemble Gemma Models with Synthetic Data Augmentation

model-releasesarxiv-cs-cl

arXiv:2605.05159v1 Announce Type: new Abstract: We present our system for SemEval-2026 Task 9: Multilingual Polarization Detection, a binary classification task spanning 22 languages. Our approach fin

Industry

Public health intelligence shouldn't require a data scientist

This article argues for democratizing public health data analytics by making intelligence and insights accessible to non-technical public health professionals without requiring specialized data scienc

industrydatabricks

Purdah and Patriarchy: Evaluating and Mitigating South Asian Biases in Open-Ended Multilingual LLM Generations

arXiv:2505.18466v2 Announce Type: replace Abstract: Evaluations of Large Language Models (LLMs) often overlook intersectional and culturally specific biases, particularly in underrepresented multiling

safetyarxiv-cs-cl

QKVShare: Quantized KV-Cache Handoff for Multi-Agent On-Device LLMs

model-releasesarxiv-cs-ai

arXiv:2605.03884v1 Announce Type: new Abstract: Multi-agent LLM systems on edge devices need to hand off latent context efficiently, but the practical choices today are expensive re-prefill or full-pr

QuadBox: Accelerating 3D Gaussian Splatting with Geometry-Aware Boxes

arXiv:2605.04844v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has emerged as an advanced technique for real-time novel view synthesis by representing scene geometry and appearance using

Quadrature-TreeSHAP: Depth-Independent TreeSHAP and Shapley Interactions

arXiv:2605.04497v1 Announce Type: new Abstract: Shapley values are a standard tool for explaining predictions of tree ensembles, with Path-Dependent SHAP being the most widely used variant. Despite su

hardwarearxiv-cs-lg

Quantifying Trust: Financial Risk Management for Trustworthy AI Agents

arXiv:2604.03976v2 Announce Type: replace Abstract: Prior work on trustworthy AI emphasizes model-internal properties such as bias mitigation, adversarial robustness, and interpretability. As AI syste

safetyarxiv-cs-ai

Quantile-Free Uncertainty Quantification in Graph Neural Networks

arXiv:2605.04847v1 Announce Type: new Abstract: Uncertainty quantification (UQ) in graph neural networks (GNNs) is crucial in high-stakes domains but remains a significant challenge. In graph settings

applicationsarxiv-cs-lg

Quantization and Fast Inference (MEAP) - How much performance are you actually getting from quantization in production? [D]

applicationsr-machinelearning

This discussion examines the practical performance gains of quantization techniques when deployed in production environments, particularly addressing whether theoretical speedups translate to real-wor

Quantum-inspired Reinforcement Learning for Synthesizable Drug Design

model-releasesarxiv-cs-lg

arXiv:2409.09183v2 Announce Type: replace Abstract: Synthesizable molecular design (also known as synthesizable molecular optimization) is a fundamental problem in drug discovery, and involves designi

Quantum Motion raises $160M to build faster quantum chips

Quantum computer maker Quantum Motion Ltd. today announced that it has raised 160 million in funding to enhance its silicon-based qubit technology. The Series C round was led by DCVC and Kembara. It c

hardwaresiliconangle

QUIVER: Cost-Aware Adaptive Preference Querying in Surrogate-Assisted Evolutionary Multi-Objective Optimization

arXiv:2605.04267v1 Announce Type: new Abstract: Interactive multi-objective optimization systems face a budget allocation dilemma: one can spend resources on expensive objective evaluations or on elic

RaguTeam at SemEval-2026 Task 8: Meno and Friends in a Judge-Orchestrated LLM Ensemble for Faithful Multi-Turn Response Generation

arXiv:2605.04523v1 Announce Type: new Abstract: We present our winning system for Task~B (generation with reference passages) in SemEval-2026 Task~8: MTRAGEval. Our method is a heterogeneous ensemble

researcharxiv-cs-cl

RAMoEA-QA: Hierarchical Specialization for Robust Respiratory Audio Question Answering

arXiv:2603.06542v2 Announce Type: replace-cross Abstract: Conversational generative AI is increasingly explored in healthcare, where models must integrate heterogeneous patient signals and support div

applicationsarxiv-cs-ai

Ramp Labs is doing a ton of cool work all the way from launching products to hardcore research on LLM internals, this is a fun one 🔥

Ramp Labs is doing a ton of cool work all the way from launching products to hardcore research on LLM internals, this is a fun one 🔥 Talked to @ramplabs Head of Applied Research Alex Shevchenko on the

agentsharrison-chase--x

Tools

Real-Time Evaluation of Autonomous Systems under Adversarial Attacks

model-releasesarxiv-cs-ai

arXiv:2605.03491v1 Announce Type: new Abstract: Most evaluations of autonomous driving policies under adversarial conditions are conducted in simulation, due to cost efficiency and the absence of phys

Real-Time Performance Monitoring and Faster Debugging with NCCL Inspector and Prometheus

NCCL Inspector is a profiling plugin that provides detailed, per-communicator, per-collective performance and metadata logging, designed to help users analyze and debug NCCL collective operations by g

hardwarenvidia-developer

RealLiFe: Real-Time Light Field Reconstruction via Hierarchical Sparse Gradient Descent

arXiv:2307.03017v5 Announce Type: replace Abstract: With the rise of Extended Reality (XR) technology, there is a growing need for real-time light field reconstruction from sparse view inputs. Existin

ReasonAudio: A Benchmark for Evaluating Reasoning Beyond Matching in Text-Audio Retrieval

model-releasesarxiv-cs-ai

arXiv:2605.03361v2 Announce Type: new Abstract: As multimodal content continues to expand at a rapid pace, audio retrieval has emerged as a key enabling technology for media search, content organizati

ReasoningGuard: Safeguarding Large Reasoning Models with Inference-time Safety Aha Moments

arXiv:2508.04204v2 Announce Type: replace Abstract: Large Reasoning Models (LRMs) have demonstrated impressive performance in reasoning-intensive tasks, but they remain vulnerable to harmful content g

safetyarxiv-cs-cl