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pTNAS: Progressive Neural Architecture Search for Tabular Data

arXiv:2403.10318v3 Announce Type: replace Abstract: Recent advances have shifted the paradigm of tabular learning toward tabular foundation models, yet their accuracy relies on a heavy inference cost

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QuadVerse: An Integrated Framework Aligning Visual-Physical Reality for Quadruped Simulation

DGX agent

arXiv:2606.07118v1 Announce Type: new Abstract: Simulation is central to robot learning, yet the sim-to-real gap remains a major bottleneck.Existing approaches often tackle visual or dynamic gaps sepa

safetyarxiv-cs-ro

8 Jun 2026

Quantifying Media Representation Dynamics Across 25 Years of News Reporting on Policing-related Deaths

arXiv:2606.06812v1 Announce Type: new Abstract: We perform the largest known computational analysis of Canadian news narratives about police-involved deaths, spanning 4,000 articles from the last quar

researcharxiv-cs-cl

Quantum-Inspired Trace-Augmented Evidence Selection for Reasoning over Structured Hypothesis Spaces

arXiv:2606.06941v1 Announce Type: new Abstract: Large language models (LLMs) now solve a wide range of expert-level exams at or above human level, yet remain brittle on specialised, evidence-intensive

Local Ai

Queen-Bee Agents: A BeeSpec-Centered Architecture for Governed Enterprise MCP Orchestration

arXiv:2606.06545v1 Announce Type: cross Abstract: Enterprise agent systems increasingly need to connect large language models to private tools, internal knowledge, and Model Context Protocol (MCP) int

local-aiarxiv-cs-ai

Rapid co-design of Buoyancy-assisted robots for Challenging Locomotion using Gaussian Evolutionary Specialists

arXiv:2606.07424v1 Announce Type: new Abstract: Designing high-performance legged robots requires jointly optimizing morphology and control. Model-free Reinforcement Learning (RL) offers an alternativ

safetyarxiv-cs-ro

RASFT: Rollout-Adaptive Supervised Fine-Tuning for Reasoning

arXiv:2606.07006v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) is a prevailing method for adapting large language models to reasoning tasks by imitating offline expert demonstrations,

safetyarxiv-cs-cl

RAVEN: Retrieval-Augmented Vulnerability Exploration Network for Memory Corruption Analysis in User Code and Binary Programs

arXiv:2604.17948v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across various cybersecurity tasks, including vulnerability classificat

Re-Centering Humans in LLM Personalization

arXiv:2606.06614v1 Announce Type: cross Abstract: Despite growing interest, most evaluations of large language models' (LLMs') personalization abilities have relied on synthetic data. It remains uncle

researcharxiv-cs-ai

Re-imagining ISO 26262 in the Age of Autonomous Vehicles: Enhancing Controllability through Transferability and Predictability

arXiv:2606.07437v1 Announce Type: cross Abstract: The ISO 26262 standard defines functional safety for road vehicles through risk assessments based on Severity, Exposure, and Controllability, grounded

Tutorials

Reactivity-Informed Machine Learning for Performance Prediction and Design Space Exploration of Alkali-Activated Slag

arXiv:2606.06765v1 Announce Type: cross Abstract: Establishing quantitative relationships among mix design, raw material properties, curing conditions, and performance remains a long-standing challeng

tutorialsarxiv-cs-lg

Real-Time AttentionBender: Granular Interactive Network Bending of Video Diffusion Transformers

arXiv:2606.06497v1 Announce Type: cross Abstract: Generative video models have achieved remarkable visual fidelity, yet their prompt-only interface offers thin creative agency and obscures the model's

RealDocBench: A Benchmark for Field-Level QA and Layout Understanding on Real-World Regulated Documents

arXiv:2606.07401v1 Announce Type: new Abstract: Document parsing systems are increasingly deployed in high-stakes, regulated workflows such as mortgage underwriting, financial reporting, supply-chain

RECAP: Regression Evaluation for Continual Adaptation of Prompts

model-releasesarxiv-cs-cl

arXiv:2606.06698v1 Announce Type: cross Abstract: Production agentic systems routinely face evolving constraints and must comply from the very next interaction. Scenarios like a tool-call notification

ReclAIm: A Multi-Agent Framework for Monitoring and Correcting Performance Decline in Medical Imaging AI

arXiv:2510.17004v2 Announce Type: replace-cross Abstract: Purpose: To develop and evaluate a multi-agent framework (ReclAIm) for automated monitoring, detection, and correction of performance decline

Reconstructing Multi-Decadal Forest Disturbances: A Spatio-Temporal Transformer Approach

arXiv:2606.07249v1 Announce Type: new Abstract: Accurate monitoring of forest disturbances is essential for understanding carbon dynamics and land management, yet traditional approaches typically rely

Reference-Free Evaluation of Taxonomies

arXiv:2505.11470v3 Announce Type: replace Abstract: We introduce two reference-free metrics for quality evaluation of taxonomies in the absence of labels. The first metric evaluates robustness by calc

researcharxiv-cs-cl

REMEDI: A Benchmark for Retention and Unlearning Evaluation in Multi-label Clinical Disease Inference

arXiv:2606.07141v1 Announce Type: cross Abstract: Language models trained for clinical disease inference are trained on patient data, which may include sensitive and private information, and data owne

RePo: Language Models with Context Re-Positioning

arXiv:2512.14391v3 Announce Type: replace-cross Abstract: In-context learning is fundamental to modern Large Language Models (LLMs); however, prevailing architectures impose a rigid and fixed contextu

researcharxiv-cs-ai

Residual-Controlled Multiplier Learning for Stochastic Constrained Decision-Making

arXiv:2606.07088v1 Announce Type: new Abstract: Stochastic constrained decision-making requires optimizing performance objectives while enforcing statistical requirements such as safety or fairness. H

safetyarxiv-cs-lg

Rethinking Genomic Modeling Through Optical Character Recognition

arXiv:2602.02014v2 Announce Type: replace-cross Abstract: Recent genomic foundation models largely adopt large language model architectures that treat DNA as a one-dimensional token sequence. However,

researcharxiv-cs-ai

RETROSPECT: RETROsynthesis via Sequential Prediction, and Chemically Transformed-ranking

arXiv:2606.07181v1 Announce Type: cross Abstract: Single-step retrosynthesis needs both accurate first-ranked suggestions and candidate lists that are rich enough for downstream selection. We study th

Reversible Foundations: Training a 120B Sparse MoE through State-Preserving Scaling

model-releasesarxiv-cs-lg

arXiv:2606.07404v1 Announce Type: new Abstract: This paper reports on training a hundred-billion-parameter sparse mixture of experts on a single eight-GPU node, end to end. LightningLM 0.1V is a recur

RhinoVLA Technical Report

model-releasesarxiv-cs-lg

arXiv:2606.07383v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong potential for robotic manipulation, but real-time deployment on edge hardware remains challengin

RigPAPR: Rig-Based Animation of Static Neural Point Clouds from a Fixed-Viewpoint Video

arXiv:2606.06685v1 Announce Type: new Abstract: Static neural point reconstructions capture a subject at high fidelity from posed images. Given such a reconstruction, we aim to animate it to follow a

RISE: Single Static Radar-based Indoor Scene Understanding

arXiv:2511.14019v3 Announce Type: replace Abstract: Robust and privacy-preserving indoor scene understanding remains a fundamental open problem. While optical sensors such as RGB and LiDAR offer high

Robotic Policy Adaptation via Weight-Space Meta-Learning

arXiv:2606.07217v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are emerging as a promising paradigm for robotic manipulation, enabling general-purpose policies trained from larg

safetyarxiv-cs-cv

Robots Need More than VLA and World Models

arXiv:2606.06556v1 Announce Type: new Abstract: Generalist robot intelligence is often framed as a policy-scaling problem: collect more robot demonstrations, train larger Vision-Language-Action (VLA)

safetyarxiv-cs-ro

Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach

arXiv:2510.09041v3 Announce Type: replace-cross Abstract: Deep reinforcement learning (DRL) has demonstrated remarkable success in developing autonomous driving policies. However, its vulnerability to

Robustly estimating heterogeneity in factorial data using Rashomon Partitions

arXiv:2404.02141v5 Announce Type: replace-cross Abstract: In both observational data and randomized control trials, researchers select statistical models to articulate how the outcome of interest vari

RPC-GS: Gaussian Splatting with native RPC Rendering for Satellite Imagery

arXiv:2606.06690v1 Announce Type: new Abstract: We present RPC-GS, the first Gaussian Splatting framework for satellite imagery that operates natively with Rational Polynomial Camera (RPC) models. The

S23DR 2026 Winning Solution

arXiv:2606.06695v1 Announce Type: new Abstract: This text presents the winning solution to the S23DR 2026 challenge for structured 3D wireframe reconstruction from sparse SfM, fitted depth, and semant

SafeGene: Reusable Adapters for Transferable Safety Alignment

arXiv:2606.06519v1 Announce Type: new Abstract: Open-weight LLMs are increasingly fine-tuned into customized assistants, but downstream fine-tuning can weaken safety alignment and make models more vul

Hardware

Scalable Joint Resource Allocation for SLO-Constrained LLM Inference in Heterogeneous GPU Clouds

arXiv:2604.07472v2 Announce Type: replace Abstract: Serving large language model (LLM) inference in cloud environments requires jointly optimizing model selection, GPU provisioning, parallelism config

hardwarearxiv-cs-lg

Agents

SCALE: Scalable Cross-Attention Learning with Extrapolation for Agentic Workflow Scheduling

arXiv:2606.06820v1 Announce Type: cross Abstract: Agentic Large Language Model (LLM) systems decompose complex tasks into workflow Directed Acyclic Graphs (DAGs) whose primitives must be scheduled on

agentsarxiv-cs-ai

Applications

ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physics

arXiv:2604.01313v2 Announce Type: replace Abstract: High-fidelity simulations and complex inverse problems, such as detector modeling and unfolding, are computationally intensive bottlenecks across su

applicationsarxiv-cs-lg

ScenicRules: An Autonomous Driving Benchmark with Multi-Objective Specifications and Abstract Scenarios

arXiv:2602.16073v2 Announce Type: replace-cross Abstract: Developing autonomous driving systems for complex traffic environments requires balancing multiple objectives, such as avoiding collisions, ob

Agents

SCOUT: Semantic scene COverage via Uncertainty-guided Traversal

arXiv:2606.06721v1 Announce Type: cross Abstract: Robots that operate over extended periods should not merely visit space; they should progressively understand it. Yet most 3D scene graph pipelines tr

agentsarxiv-cs-ai

SEAM: Shortcut-Aware Real-Time Detection of Scripted vs. Spontaneous Speech for Interview Guardrails

model-releasesarxiv-cs-lg

arXiv:2606.06837v1 Announce Type: cross Abstract: Scripted vs spontaneous speech detection is appealing for interview guardrails, but benchmark performance can be inflated by shortcuts tied to corpus

Second-Order Path Kernel Interpolation Formulas in Machine Learning

arXiv:2606.07495v1 Announce Type: new Abstract: Understanding how training data shape neural network predictions is a central problem in modern learning theory. In 2020, Pedro Domingos proposed an int

SecretFan: Synthesizing Realistic Data without Breaking Privacy

arXiv:2602.05833v2 Announce Type: replace Abstract: There is a need for synthetic training and test datasets that replicate statistical distributions of original datasets without compromising their co

Seeing Without Exposing: Adaptive Privacy Control for Open-World, Context-Hungry MLLMs

arXiv:2606.07175v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have raised new privacy challenges. On the data side, user-provided inputs often include unpredictable sensitiv

SEEK: Steering LLM Reasoning for RAG via Internal Reasoning Sketches

arXiv:2601.09402v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external knowledge into the generation process. Benefiti

researcharxiv-cs-cl

SegMoTE: Token-Level Mixture of Experts for Medical Image Segmentation

arXiv:2602.19213v2 Announce Type: replace Abstract: Medical image segmentation is vital for clinical diagnosis and quantitative analysis, yet remains challenging due to the heterogeneity of imaging mo

Self-evolving LLM agents with in-distribution Optimization

arXiv:2606.07367v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently emerged as powerful controllers for interactive agents in complex environments, yet training them to perform

safetyarxiv-cs-lg

Semantic-Structural Alignment for Generative Pictorial Charts

arXiv:2606.06498v1 Announce Type: cross Abstract: Traditional statistical graphics are precise but often lack the visual appeal, memorability, and engagement of pictorial charts. We present a generati

safetyarxiv-cs-cv

SERNF: Sample-Efficient Real-World Dexterous Policy Fine-Tuning via Action-Chunked Critics and Normalizing Flows

arXiv:2602.09580v4 Announce Type: replace-cross Abstract: Real-world fine-tuning of dexterous manipulation policies remains challenging due to limited real-world interaction budgets and highly multimo

safetyarxiv-cs-lg

ShallowBench: Benchmarking Generative Drug Design Models on Shallow-Pocket Targets

arXiv:2606.06717v1 Announce Type: cross Abstract: While generative AI models have demonstrated remarkable success in structure-based drug design, they predominantly rely on deep binding pockets and st