Search: “framework”

AI Level of Detail: Distance-Aware ML Model Precision Selection for Real-Time Human Motion Prediction in Games

arXiv:2606.06565v1 Announce Type: cross Abstract: Modern game engines spend significant compute animating NPCs with learned motion models. This paper proposes AI Level of Detail (AI LOD), a framework

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alpha-PFN: Fast Entropy Search via In-Context Learning

DGX agent

arXiv:2606.07134v1 Announce Type: new Abstract: Information-theoretic acquisition functions such as Entropy Search (ES) offer a principled exploration-exploitation framework for Bayesian optimization

applicationsarxiv-cs-lg

8 Jun 2026

Attack Selection in Agentic AI Control Evaluations Meaningfully Decreases Safety

arXiv:2606.06529v1 Announce Type: new Abstract: An attacker that strategically chooses when to attack is much harder to catch than one that attacks indiscriminately. AI control is a safety framework f

Attention-Guided Autoencoder Fusion for Insulator Defect Detection Using UAV Transmission-Line Imaging

arXiv:2606.06536v1 Announce Type: cross Abstract: Automated defect detection in high-voltage transmission-line insulators remains challenging due to severe class imbalance, large scale variation, and

local-aiarxiv-cs-ai

Aumann-SHAP: The Geometry of Counterfactual Interaction Explanations in Machine Learning

model-releasesarxiv-cs-lg

arXiv:2603.14014v2 Announce Type: replace Abstract: We introduce Aumann-SHAP, an interaction-aware framework that decomposes counterfactual transitions by restricting the model to a local hypercube co

Beyond Backscatter: InSAR coherence from detected SAR images

arXiv:2606.07374v1 Announce Type: cross Abstract: In this work, we propose a deep learning framework for coherence regression directly from detected SAR images, without the need for accurate coregistr

tutorialsarxiv-cs-cv

COF26: A new on-top functional for multiconfiguration pair-density functional theory

model-releasesarxiv-cs-ai

arXiv:2605.06215v2 Announce Type: replace-cross Abstract: Multiconfiguration pair-density functional theory (MC-PDFT) provides an efficient and accurate framework for computing electronic energies in

Coordinated optimization of departure sequencing and section-track allocation in railway short-term concentrated departure scenarios based on qubo and hybrid quantum algorithms

arXiv:2606.06543v1 Announce Type: cross Abstract: This study examines the coordinated optimization of departure sequencing and section-track allocation in railway short-term concentrated departure sce

CountsDiff: A Diffusion Model on the Natural Numbers for Generation and Imputation of Count-Based Data

arXiv:2604.03779v2 Announce Type: replace-cross Abstract: Diffusion models have excelled at generative tasks for both continuous and token-based domains, but their application to discrete ordinal data

Detecting and Mitigating Bias by Treating Fairness as a Symmetry Operation

arXiv:2606.06514v1 Announce Type: new Abstract: Machine learning systems deployed in high stakes socioeconomic settings routinely display bias. We formalize bias as a symmetry breaking operation: a cl

Enhancing Conformal Prediction via Class Similarity

arXiv:2511.19359v2 Announce Type: replace Abstract: Conformal Prediction (CP) has emerged as a powerful statistical framework for high-stakes classification applications. Instead of predicting a singl

Agents

GOPAgen: Motion-Aware and Efficient Agentic Long-Video Understanding with Structural Memory and Hierarchical Reasoning

arXiv:2606.06532v1 Announce Type: new Abstract: Despite significant progress in agentic long video understanding, existing methods still lack detailed motion comprehension coupled with an efficient me

agentsarxiv-cs-cv

GP-Adapter: Gaussian Process CLIP-Adapter for Few-Shot Out-of-Distribution Detection

arXiv:2606.07102v1 Announce Type: cross Abstract: We propose GP-Adapter, a training-free framework that augments CLIP (Contrastive Language-Image Pre-training) with Gaussian Process (GP) uncertainty m

Limitations of Normalization in Attention Mechanism

arXiv:2508.17821v3 Announce Type: replace-cross Abstract: This paper investigates the limitations of the normalization in attention mechanisms. We begin with a theoretical framework that enables the i

MADRAG: Multi-Agent Debate with Retrieval-Augmented Generation for Training-Free Analytic Essay Scoring

arXiv:2606.06754v1 Announce Type: cross Abstract: We present MADRAG, a training-free framework for analytic essay scoring that combines multi-agent reasoning with retrieval-augmented grounding. Unlike

safetyarxiv-cs-cl

Native3D: End-to-End 3D Scene Generation via Unified Mesh-Texture Modeling and Semantic Alignment

arXiv:2606.07117v1 Announce Type: cross Abstract: This paper presents Native3D, the first end-to-end 3D scene generation framework that completely bypasses 2D intermediate representations. Traditional

Agents

On the Hardness of Optimal Motion on Trees

arXiv:2606.06686v1 Announce Type: new Abstract: This paper presents a simple framework that settles the complexity of Multi-Agent Path Finding (MAPF) on trees across standard objectives--distance, mak

agentsarxiv-cs-ro

PhyRoGen: Synthetic Generation of Physical Robot Manipulation Puzzles Using Procedural Content Generation

model-releasesarxiv-cs-ro

arXiv:2606.06569v1 Announce Type: new Abstract: Robot manipulation of physical puzzles is important for automatic assembly and disassembly tasks. However, to enable robots to solve physical puzzles, m

Privacy Implies Stability: Information-Theoretic Generalization Bounds for Quantum Learning

arXiv:2602.01177v3 Announce Type: replace-cross Abstract: We develop an information-theoretic framework connecting stability, privacy, and generalization for quantum learning algorithms. Learning proc

Streaming Video Generation with Streaming Force Control

arXiv:2606.07508v1 Announce Type: new Abstract: We introduce StreamForce, a streaming video generation framework that enables physically grounded control through continuous force inputs. Unlike prior

local-aiarxiv-cs-cv

Varifold Moment Invariants for Sustainable and Explainable Contour Feature Extraction

arXiv:2606.07333v1 Announce Type: new Abstract: We introduce Varifold Moments Invariants (VMI) as a unifying framework for many previously introduced Moment Invariants. These invariants are deeply rel

researcharxiv-cs-cv

Hardware

VIRTUS-FPP: Virtual Sensor Modeling for Fringe Projection Profilometry in NVIDIA Isaac Sim

arXiv:2509.22685v2 Announce Type: replace-cross Abstract: Fringe projection profilometry (FPP) is a high-precision structured-light sensing technique for 3D surface reconstruction, yet its practical d

hardwarearxiv-cs-cv

Cognitive Threat Intelligence and Explainable Federated Security Analytics for distributed Infrastructure Systems

arXiv:2606.05701v1 Announce Type: cross Abstract: The increasing adoption of distributed infrastructure systems, cloud computing, Internet of Things (IoT) technologies, and edge-based architectures ha

local-aiarxiv-cs-ai

EEGDancer: Dynamic Emotion Latent Space Masked Modeling with Reinforcement Learning for EEG Continuous Emotion Prediction

arXiv:2606.05855v1 Announce Type: cross Abstract: Continuous electroencephalography (EEG) emotion prediction aims to model the temporal evolution of human emotional states from EEG signals. Unlike con

tutorialsarxiv-cs-ai

Goedel-Architect: Streamlining Formal Theorem Proving with Blueprint Generation and Refinement

model-releasesarxiv-cs-ai

arXiv:2606.06468v1 Announce Type: new Abstract: We introduce Goedel-Architect, an agentic framework for formal theorem proving in Lean 4 centered on blueprint generation and refinement. A blueprint is

Hardware

LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels

arXiv:2603.19312v3 Announce Type: replace-cross Abstract: Joint Embedding Predictive Architectures (JEPAs) offer a compelling framework for learning world models in compact latent spaces, yet existing

hardwarearxiv-cs-ai

Metamorphic Testing with the Rashomon Set: Explanation Faithfulness in Machine Learning

arXiv:2606.06056v1 Announce Type: cross Abstract: Multiple machine learning models can achieve near-equivalent predictive performance on the same task, yet provide divergent feature-based explanations

Severity-Aware Curriculum Learning with Multi-Model Response Selection for Medical Text Generation

arXiv:2606.05510v1 Announce Type: new Abstract: Telehealth systems have become increasingly important for delivering accessible and timely medical information. Existing large language models often str

Zero knowledge verification for frontier AI training is possible

arXiv:2606.05433v1 Announce Type: new Abstract: Frontier AI governance frameworks increasingly use cumulative training compute as the primary criterion for designating high-impact models, but enforcem

Can Language Models Learn to Listen?

arXiv:2308.10897v2 Announce Type: replace Abstract: We present a framework for generating appropriate facial responses from a listener in dyadic social interactions based on the speaker's words. Given

tutorialsarxiv-cs-cv

GLASS: GRPO-Trained LoRA for Acoustic Style Steering in Zero-Shot Text-to-Speech

arXiv:2606.05889v1 Announce Type: cross Abstract: We propose GLASS, a framework for composable acoustic style control in zero-shot autoregressive text-to-speech (TTS) that learns controls from post-ge

safetyarxiv-cs-cl

Inverse Design of Realizable Metasurface based Absorbers using Improved Conditioning and Diversity Enhanced Progressively Growing GANs

arXiv:2606.05849v1 Announce Type: cross Abstract: Metasurfaces enable precise manipulation of electromagnetic waves for applications such as beam steering, sensing, and stealth technology. However, in

safetyarxiv-cs-cv

Learning of Robot Safety Policies via Adversarial Synthetic Scenarios

arXiv:2606.05952v1 Announce Type: new Abstract: In this work, we propose an agentic gamification framework for hazard-informed learning of robot safety policies through synthetic scenarios. We model s

safetyarxiv-cs-ro

Applications

MotionDisco: Motion Discovery for Extreme Humanoid Loco-Manipulation

arXiv:2606.06139v1 Announce Type: new Abstract: We present MotionDisco, a framework that discovers contact-rich, long-horizon humanoid loco-manipulation motions from scratch, without relying on teleop

applicationsarxiv-cs-ro

Robust Scene Transfer for PointGoal Navigation via Privileged Sensor Guided Contrastive Learning

arXiv:2606.05506v1 Announce Type: new Abstract: We propose a sensor-guided adaptive contrastive learning framework for visual representation learning in PointGoal navigation. During training, privileg

safetyarxiv-cs-cv

Sample-efficient Low-level Motion Planning for Robotic Manipulation Tasks via Zero-shot Transfer Learning

arXiv:2606.06041v1 Announce Type: new Abstract: As robotic systems become more sophisticated, the growing complexity of their motion planning models and the longer training times pose substantial chal

tutorialsarxiv-cs-ro

T-SAR-JEPA: Self-Supervised Temporal Anomaly Detection in SAR Amplitude Stacks via Latent Prediction

arXiv:2606.05700v1 Announce Type: new Abstract: We present T-SAR-JEPA, a self-supervised framework for temporal anomaly detection in SAR amplitude stacks via latent prediction. A ViT-Base/16 encoder f

local-aiarxiv-cs-cv

Towards a Data Flywheel for Embodied Intelligence in Logistics

arXiv:2606.05960v1 Announce Type: new Abstract: Embodied intelligence is moving from laboratory demonstrations toward industrial deployment, with the logistics industry serving as a key application sc

safetyarxiv-cs-ro

Applications

3DThinkVLA: Endowing Vision-Language-Action Models with Latent 3D Priors via 3D-Thinking-Guided Co-training

arXiv:2606.04436v1 Announce Type: new Abstract: We propose a 3D-thinking-guided co-training framework that enables vision-language-action (VLA) models to perform 3D spatial reasoning implicitly during

applicationsarxiv-cs-cv

A Goal-Set Characterization of Task Composition in the Boolean Task Algebra

arXiv:2606.04053v1 Announce Type: cross Abstract: The Boolean Task Algebra (BTA) provides a principled framework for zero-shot task composition in reinforcement learning by equipping goal-reaching tas

Adalina: Adaptive Linear Approximation for the Shapley Value and Beyond

arXiv:2604.08438v2 Announce Type: replace Abstract: The Shapley value, and its broader family of semi-values, has received much attention in various attribution problems. A fundamental and long-standi

Agents

Archi: Agentic Operations at the CMS Experiment

arXiv:2606.04755v1 Announce Type: cross Abstract: We present Archi, an open-source, end-to-end framework for scientific collaborations that combines the systematic ingestion and organization of hetero

agentsarxiv-cs-ai

CLAW: Learning Continuous Latent Action World Models via Adversarial Latent Regularization

arXiv:2606.04130v1 Announce Type: new Abstract: We introduce CLAW, a fully end-to-end self-supervised framework for learning a world model jointly with continuous latent action representations directl

researcharxiv-cs-ro

Conditional PED-ANOVA: Hyperparameter Importance in Hierarchical & Dynamic Search Spaces

arXiv:2601.20800v3 Announce Type: replace-cross Abstract: We propose conditional PED-ANOVA (condPED-ANOVA), a principled framework for estimating hyperparameter importance (HPI) in conditional search

Controllable Dynamic 3D Shape Generation via 3D Trajectories and Text

arXiv:2606.05162v1 Announce Type: new Abstract: We introduce T2Mo, a feed-forward framework for controllable dynamic 3D shape generation conditioned on 3D trajectories and text. Due to the inherent am

researcharxiv-cs-cv

Curvature-aware dynamic precision approach for physics-informed neural networks

model-releasesarxiv-cs-ai

arXiv:2606.04736v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) have become a promising framework for simulating partial differential equations (PDEs) by embedding physical

DPM++: Dynamic Masked Metric Learning for Occluded Person Re-identification

arXiv:2605.06637v2 Announce Type: replace Abstract: Although person re-identification has made impressive progress, occlusion caused by obstacles remains an unsettled issue in real applications. The d

safetyarxiv-cs-cv

Echo-Infinity: Learning Evolving Memory for Real-Time Infinite Video Generation

arXiv:2606.04527v1 Announce Type: cross Abstract: We present Echo Infinity, an autoregressive (AR) framework towards real-time infinite video generation that employs a learnable evolving memory to dyn

local-aiarxiv-cs-cv