Search: “evaluation”

Retrieval-Augmented Anatomical Guidance for Text-to-CT Generation

arXiv:2603.08305v2 Announce Type: replace Abstract: Text-conditioned generative models for volumetric medical imaging provide semantic control but lack explicit anatomical guidance, often resulting in

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Reward-free Pretraining for Reinforcement Learning via Occupancy Coverage Maximization

DGX agent

arXiv:2606.21271v1 Announce Type: new Abstract: Sparse rewards pose a central challenge in reinforcement learning, since agents receive no informative signal until they reach their goal. Intrinsic-rew

researcharxiv-cs-lg

23 Jun 2026

Right Knowledge, Wrong Answer: Test-Time Steering for Temporal Fact Conflicts in Open-Weight Language Models

arXiv:2606.20959v1 Announce Type: new Abstract: Large language models can store both outdated facts and newer superseding facts in their parameters, but standard prompting may still elicit the outdate

Robot Self-Improvement via Human-Video Dynamics Models

arXiv:2606.21406v1 Announce Type: cross Abstract: A central question in robot learning is how to acquire skills from the kinds of data that humans learn from: passive observation, embodied practice, a

safetyarxiv-cs-cv

Robust and Differentially Private Principal Component Analysis

arXiv:2507.15232v2 Announce Type: replace-cross Abstract: Recent advances have sparked significant interest in the development of privacy-preserving Principal Component Analysis (PCA). However, many e

RocketPFN: Accurate Time Series Classification via In-Context Learning

arXiv:2606.21786v1 Announce Type: new Abstract: We introduce RocketPFN, a training-free pipeline for time series classification that combines random convolutional feature extraction (Rocket) with in-c

RogueRover: Autonomous Rogue Device Localization for Incident Response

arXiv:2606.21264v1 Announce Type: new Abstract: Physically localizing unauthorized wireless devices remains a critical bottleneck in cyber-physical security operations, where rogue access points can p

agentsarxiv-cs-ro

RubricRL: Simple Generalizable Rewards for Text-to-Image Generation

arXiv:2511.20651v2 Announce Type: replace Abstract: Reinforcement learning (RL) has recently emerged as a promising approach for aligning text-to-image generative models with human preferences. A key

safetyarxiv-cs-cv

SAGE: An Expert-Annotated South Asian GI Endoscopy Dataset for Multimodal Learning and Hallucination Analysis

arXiv:2606.22144v1 Announce Type: new Abstract: Gastrointestinal cancers represent a growing health burden in the South Asian region, driven largely by rapid changes in socio-economic conditions & lif

safetyarxiv-cs-cv

ScanBot: A Benchmark for Precision Robotic Surface Scanning with Industrial Laser Profilers

model-releasesarxiv-cs-ro

arXiv:2505.17295v2 Announce Type: replace Abstract: We introduce ScanBot, a benchmark for instruction-conditioned, high-precision surface scanning with robot-mounted industrial laser profilers. Unlike

Scene-agnostic ALS boresight self-calibration

arXiv:2606.23101v1 Announce Type: new Abstract: ALS boresight calibration has relied for two decades on dedicated flight patterns over structured scenes containing planar surfaces of varied aspect and

Hardware

SCENIC: Semantic-Conditioned Edge-Aware Neural Framework for Structured IoT Command Generation

arXiv:2606.22296v1 Announce Type: new Abstract: Edge Internet of Things (IoT) agents are often constrained by memory capacity, privacy requirements, communication latency, and recurring inference cost

hardwarearxiv-cs-lg

SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors

arXiv:2606.22700v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative model training without sharing raw data, making it a promising paradigm for privacy-sensitive applicatio

local-aiarxiv-cs-cv

SEED: Simple ViT and Evolving Harness for Explainable Text Forgery Detection

arXiv:2606.21138v1 Announce Type: new Abstract: AI-assisted image editing threatens trust in financial, legal, and identity records. The GenText-Forensics Challenge at ACM MM 2026 addresses this by re

local-aiarxiv-cs-cv

Self-Curriculum Model-based Reinforcement Learning for Shape Control of Deformable Linear Objects

arXiv:2602.21816v2 Announce Type: replace Abstract: Precise shape control of Deformable Linear Objects (DLOs) is crucial in robotic applications such as industrial and medical fields. However, existin

safetyarxiv-cs-ro

Self-Evolution for Multi-Turn Tool-Calling Agents via Divergence-Point Preference Learning

arXiv:2606.23112v1 Announce Type: new Abstract: Multi-turn tool-using agents must coordinate long-horizon tool sequences while tracking dialogue state and policy constraints. Existing approaches often

Self-Evolving Cognitive Framework via Causal World Modeling for Embodied Scientific Intelligence

arXiv:2606.22449v1 Announce Type: cross Abstract: Current embodied world models are primarily optimized for predictive objectives, limiting their ability to generalize under distribution shifts and re

researcharxiv-cs-ro

ShotcreteDepth: A Bi-modal Dataset for Robust Robotic Depth Perception in Shotcrete Construction Environments

arXiv:2606.23152v1 Announce Type: new Abstract: We introduce ShotcreteDepth, a bi-modal dataset from the construction domain that captures both an active shotcreting process and general construction e

agentsarxiv-cs-ro

Simulation-Free Estimation of Traffic Flows from Sparse Count Data

arXiv:2606.23536v1 Announce Type: new Abstract: We propose a method for estimating time-varying traffic flow patterns from sparse aggregated vehicle counts. The method partitions the study area into s

Skeleton-to-Image Encoding: Enabling Skeleton Representation Learning via Vision-Pretrained Models

arXiv:2603.05963v2 Announce Type: replace Abstract: Recent advances in large-scale pretrained vision models have demonstrated impressive capabilities across a wide range of downstream tasks, including

SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity

arXiv:2602.08690v2 Announce Type: replace Abstract: Deep Reinforcement Learning (DRL) has achieved remarkable success in domains requiring sequential decision-making, motivating its application to cyb

agentsarxiv-cs-lg

Solve for the Hyperparameter, Skip the Search: Kolmogorov-Optimal Scaling Laws for Spline Regression

arXiv:2606.23575v1 Announce Type: new Abstract: Hyperparameter tuning almost always means search: fit the model at every value on a grid, score each by cross-validation, and keep the winner. For splin

safetyarxiv-cs-lg

Sources: the Trump administration is pressing Meta to submit its AI models for voluntary review; Meta is the only major US AI developer without an agreement (New York Times)

New York Times: Sources: the Trump administration is pressing Meta to submit its AI models for voluntary review; Meta is the only major US AI developer without an agreement — Federal officials are urg

safetytechmeme

Sovereign Execution Broker: Enforcing Certificate-Bound Authority in Agentic Control Planes

arXiv:2606.20520v2 Announce Type: replace-cross Abstract: Autonomous agents are increasingly connected to cloud, deployment, and data-control workflows, but production mutation authority should not re

safetyarxiv-cs-lg

Tutorials

Sparse Point-Guided Fusion of Supervised and Self-Supervised Learning Model for Seaweed Segmentation

arXiv:2606.21026v1 Announce Type: new Abstract: The ocean plays a critical role in sustainable development, particularly in climate change mitigation. Among marine ecosystems, blue carbon ecosystems a

tutorialsarxiv-cs-cv

Specificity- and Calibration-Aware Breast Ultrasound Segmentation via Entropy-Guided Boundary Supervision

arXiv:2606.22308v1 Announce Type: cross Abstract: Lesion segmentation in breast ultrasound involves two related challenges. In images with lesions, speckle noise, low tissue contrast, and posterior ac

Tutorials

Spectral GS-SLAM: Observability-Aware, Degeneracy-Robust Tracking for Real-Time 3D Gaussian Splatting SLAM

arXiv:2606.21258v1 Announce Type: cross Abstract: Recent 3DGS-SLAM systems enable real-time operation by leveraging conventional feature matching or ICP-based tracking, thereby avoiding the heavy dens

tutorialsarxiv-cs-cv

SPiralRoll: A Novel Adjustable-Stiffness Underactuated 3-DoF Joint with Torsion Springs for Rolling Robots

arXiv:2606.22443v1 Announce Type: new Abstract: Compliant mechanisms are important in robotics because they can improve adaptability, safety, and energy efficiency while reducing hardware complexity.

safetyarxiv-cs-ro

StructSAM: Structure- and Spectrum-Preserving Token Merging for Segment Anything Models

arXiv:2603.07307v2 Announce Type: replace Abstract: Recent token merging techniques for Vision Transformers (ViTs) provide substantial speedups by reducing the number of tokens processed by self-atten

Subspace-Constrained Federated Learning with Low-Rank Adaptation

arXiv:2606.22724v1 Announce Type: new Abstract: Federated low-rank adaptation methods are attractive for fine-tuning large models under communication and privacy constraints, but heterogeneous client

local-aiarxiv-cs-lg

SuperCond-GNN: Scalable Graph Neural Network Surrogate for Superconducting Circuit Simulations

arXiv:2606.23548v1 Announce Type: cross Abstract: This paper presents SuperCond-GNN, a graph neural network-based surrogate model for predicting the voltage distribution in high-temperature supercondu

local-aiarxiv-cs-lg

Applications

SURE: Safe Uncertainty-Aware Robot-Environment Interaction using Trajectory Optimization

arXiv:2602.06864v2 Announce Type: replace Abstract: Robotic tasks involving contact interactions pose significant challenges for trajectory optimization due to discontinuous dynamics. Conventional for

applicationsarxiv-cs-ro

Synthetic Network Packet Generation through Statistical Learning and Genetic Algorithms

arXiv:2606.20864v1 Announce Type: cross Abstract: Developing robust intrusion detection systems (IDS) for IoT environments requires large, labeled datasets capturing realistic traffic distributions ac

T-MOR: Learning Motion-Aware Skeleton Representations for Human Action Recognition

arXiv:2606.21607v1 Announce Type: new Abstract: Vision-language models such as CLIP have recently achieved strong performance on a wide range of visual understanding tasks. However, most existing mode

T-Rex: Tactile-Reactive Dexterous Manipulation

arXiv:2606.17055v2 Announce Type: replace Abstract: The ability to react dynamically to tactile signals has long been considered crucial to agile human-level dexterity. Yet contemporary learning-based

researcharxiv-cs-ro

Target-Aware Linear Regression Under Distribution Shift

arXiv:2606.22775v1 Announce Type: cross Abstract: Distribution shift between training and deployment is a pervasive challenge for modern AI systems. In many cases, the target marginals of covariates a

Technical Report for the ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge: Pretraining-Diverse Ensemble of Foundation Vision Encoders for Robust Outdoor Scene Understanding

arXiv:2606.23113v1 Announce Type: new Abstract: This report presents our solution for the ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge, which requires parsing unstructured outdoor s

TeleStyle V2: Beyond Content-Preserving Style Transfer with Self-Distillation and Distribution-Matching-Distillation

arXiv:2606.20709v1 Announce Type: new Abstract: Given a content reference and a style reference, content-preserving style transfer requires the model to generate stylized outputs with content and styl

Tell Me: An LLM-powered Mental Well-being Assistant with RAG, Synthetic Dialogue Generation, and Agentic Planning

arXiv:2511.14445v2 Announce Type: replace-cross Abstract: We present Tell Me, a mental well-being system that leverages advances in large language models to provide accessible, context-aware support f

agentsarxiv-cs-lg

Temper-Then-Tilt: Principled Unlearning for Generative Models through Tempering and Classifier Guidance

arXiv:2602.10217v2 Announce Type: replace Abstract: We study machine unlearning in large generative models by framing the task as density ratio estimation to a target distribution rather than supervis

Temporal Logic Guidance for Action-Only Diffusion Policies with World Models

arXiv:2606.22729v1 Announce Type: new Abstract: Diffusion policies enable multimodal robot behavior but offer limited ability to choose among behavior modes at inference time, even though such control

safetyarxiv-cs-ro

Thalia: A Global, Multi-Modal Dataset for Volcanic Activity Monitoring

arXiv:2505.17782v4 Announce Type: replace Abstract: Monitoring volcanic activity is of paramount importance to safeguarding lives, infrastructure, and ecosystems. However, only a small fraction of kno

The Alignment Problem in Constrained Code Generation

arXiv:2606.21619v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated strong capabilities in code generation, but their outputs frequently contain syntax or type errors that

safetyarxiv-cs-lg

Applications

The Impact of VAE Design on Latent Pose Representations for Diffusion-based Sign Language Production

arXiv:2606.22959v1 Announce Type: cross Abstract: Latent diffusion approaches to sign language production (SLP) rely on an initial stage that learns an encoding of sign pose sequences, enabling genera

applicationsarxiv-cs-cv

The Metanym Game: A Self-Contained, Self-Consistent LLM Peer-Community Benchmark for Structural Intelligence

arXiv:2606.21008v1 Announce Type: cross Abstract: The metanym game is a competitive word game for LLMs that measures structural intelligence against established cognitive-science constructs. No conten

Tutorials

The New Associationism: Lessons from Deep Learning

arXiv:2606.20600v1 Announce Type: cross Abstract: What can the success of modern AI tell us about how humans learn? This paper argues that taking AI seriously as a model of human learning supports a m

tutorialsarxiv-cs-lg

The Trilemma of Truth in Large Language Models

arXiv:2506.23921v5 Announce Type: replace-cross Abstract: The public often attributes human-like qualities to large language models (LLMs), assuming that they 'know' certain things. In reality, LLMs e

The Unreasonable Effectiveness of VLMs for Zero-shot Procedural Mistake Detection

arXiv:2606.21579v1 Announce Type: new Abstract: Procedural mistake detection is important for quality control and user assistance across many disciplines. Recent work in this field has achieved signif