Search: “context”

A Generalized Formalism of Auto-Regressive Decoding for Speech Processing

arXiv:2606.20714v1 Announce Type: cross Abstract: In speech processing, most state-of-the-art sequence prediction models rely on auto-regressive (AR) strategies to generate output sequences based on t

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A Generative Model for Closed-Loop Microsimulation of Signalized Intersections

DGX agent

arXiv:2606.23588v1 Announce Type: new Abstract: Traffic microsimulators rely on hand-crafted behavior models that reproduce aggregate flow but miss the heterogeneous interactions between vehicles at s

safetyarxiv-cs-ro

23 Jun 2026

A Robust Framework for Secure Cardiovascular Risk Prediction: An Architectural Case Study of Differentially Private Federated Learning

arXiv:2603.13293v2 Announce Type: replace Abstract: Accurate cardiovascular risk prediction is crucial for preventive healthcare; however, the development of robust Artificial Intelligence (AI) models

applicationsarxiv-cs-lg

AI Agents Can Already Autonomously Perform Experimental High Energy Physics

arXiv:2603.20179v3 Announce Type: replace-cross Abstract: Large language model-based AI agents are now able to autonomously execute substantial portions of a high energy physics (HEP) analysis pipelin

AI-Augmented Thyroid Scintigraphy for Robust Classification of Disease

arXiv:2503.00366v3 Announce Type: replace-cross Abstract: Thyroid scintigraphy is vital for diagnosing thyroid disorders, yet deep learning (DL) models in this domain often struggle with limited, imba

Agents

AlphaMemo: Structured Search-Process Memory for Self-Evolving Alpha Mining Agents

arXiv:2606.20625v1 Announce Type: cross Abstract: LLM agents are promising for alpha mining via combining financial priors, symbolic reasoning, executable factor generation, and feedback-driven refine

agentsarxiv-cs-lg

Local Ai

ASTRA: A Scalable Next-Generation ATCO Training Simulator with Autonomous Simpilots

arXiv:2606.18319v2 Announce Type: replace Abstract: Air Traffic Control Operators (ATCOs) are vital in ensuring the safe, orderly, and efficient flow of air traffic, yet training capacity is constrain

local-aiarxiv-cs-lg

BadDreamer: Transferable Backdoor Attacks against Video World Models for Autonomous Driving

arXiv:2606.21172v1 Announce Type: new Abstract: Video world models are increasingly used in autonomous driving to forecast future scene evolution and provide future-aware spatio-temporal representatio

safetyarxiv-cs-cv

Beyond ROC-AUC: Operating-Point Performance Reporting for Biometric Verification

arXiv:2606.20680v1 Announce Type: new Abstract: A biometric verifier is often deployed with a strict false match budget, so only a narrow, low false match rate (FMR) slice of the score range is used.

BoxCtrl: 3D-Aware Visual Prompting for Geometric Image Editing

arXiv:2606.23270v1 Announce Type: new Abstract: As instruction-based editing models and multimodal large language models advance, diverse image editing tasks have become feasible. However, achieving p

tutorialsarxiv-cs-cv

Bridging Single Distortion Artifacts and Multifactorial Clinical Quality: Few-shot Biparametric MRI Quality Assessment via Distortion-trained Prototypical Networks

arXiv:2606.18872v2 Announce Type: replace Abstract: Clinical prostate multi-parametric MRI relies heavily on high-quality diffusion-weighted imaging (DWI), yet reading DWI is frequently compromised by

applicationsarxiv-cs-cv

Agents

Causal Discovery in the Era of Agents

arXiv:2606.23608v1 Announce Type: cross Abstract: Recent attempts to combine large language models (LLMs) with causal discovery ask models to infer pairwise directions, propose graph structures, or in

agentsarxiv-cs-lg

ClayBuddy: A Framework, Evaluation, & Mitigation of Coding Agent Failures

arXiv:2606.19380v2 Announce Type: replace-cross Abstract: Software engineering and deployment are increasingly delegated to AI coding agents. The scale of their adoption is surfacing rare, but highly

Concept-Constrained Prompt Learning for Few-Shot CLIP Adaptation

arXiv:2606.22567v1 Announce Type: new Abstract: Few-shot prompt learning is an effective strategy for adapting CLIP to downstream tasks, but class-only prompt optimization can overfit base-class super

Hardware

Concordia: JIT-Compiled Persistent-Kernel Checkpointing for Fault-Tolerant LLM Inference

arXiv:2606.23521v1 Announce Type: cross Abstract: Long-running LLM agents keep valuable state resident on GPUs: KV caches, request schedulers, communication state, and sometimes online adapters. Losin

hardwarearxiv-cs-lg

CoorDex: Coordinating Body and Hand Priors for Continuous Dexterous Humanoid Loco-Manipulation

arXiv:2606.23680v1 Announce Type: cross Abstract: Humanoid loco-manipulation is often simplified into a stop-and-go process: walking to an object, stopping to manipulate it, and then resuming locomoti

CoSA: Correlation-Guided Change Attention with Learnable Residual Gating for Remote Sensing Change Detection

model-releasesarxiv-cs-cv

arXiv:2606.21932v1 Announce Type: new Abstract: Remote sensing change detection (CD) from bi-temporal imagery is critical for applications such as urban monitoring, disaster assessment, and environmen

Local Ai

CoVStream: Edge-Cloud Collaboration for Understanding of Long Video Streams

arXiv:2606.22804v1 Announce Type: new Abstract: Long, continuous video streams are an increasingly critical driver of multimedia intelligence. Existing efforts often handle long videos with a sample-e

local-aiarxiv-cs-cv

CVSBench: A Comprehensive Benchmark for Cross-view Spatial Reasoning and Dreaming

model-releasesarxiv-cs-cv

arXiv:2606.22476v1 Announce Type: new Abstract: Humans can effortlessly reason about scenes across different viewpoints, yet it remains unclear whether Vision-Language Models (VLMs) possess similar cr

Delta-Diffusion: Modeling Longitudinal Brain Amyloid-PET Trajectories via Conditional Poisson Diffusion Bridge

arXiv:2606.22216v1 Announce Type: cross Abstract: While longitudinal brain PET imaging is the gold standard for quantifying the spatiotemporal accumulation of Beta-amyloid, its widespread clinical uti

safetyarxiv-cs-cv

Distribution-Aware Diffusion-LLM for Robust Ultra-Long-Term Time Series Forecasting

arXiv:2606.23391v1 Announce Type: new Abstract: Time series forecasting is a fundamental machine learning task. Recent work has explored Large Language Models (LLMs) for this purpose due to their stro

FARM: Find Anything using Relational Spatial Memory

arXiv:2606.15476v2 Announce Type: replace Abstract: Robots operating in homes, warehouses, and other object-rich environments need memory systems that can find specific object instances on demand. Obj

researcharxiv-cs-ro

FlowDec: Temporal Conditional Flow Decorruptor for Robust Continuous Vision-Language Navigation

arXiv:2606.22424v1 Announce Type: new Abstract: Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires agents to follow natural-language instructions in unseen scenes. While Large

applicationsarxiv-cs-cv

From Driving Videos to Simulatable Scenarios

arXiv:2606.21993v1 Announce Type: cross Abstract: Autonomous vehicles (AVs) face driving scenarios ranging from routine traffic to rare events. To assess safety it is crucial to reproduce these scenar

safetyarxiv-cs-cv

From Pixels to Concepts: Growing Rich 3D Semantic Scene Graph Forests utilizing Foundation Models

arXiv:2606.23312v1 Announce Type: new Abstract: Operating in complex real-world environments requires robots to understand their surroundings on a functional semantic level. This demands a detailed mu

applicationsarxiv-cs-ro

GeoTransolver: Learning Physics on Irregular Domains Using Multi-scale Geometry Aware Physics Attention Transformer

arXiv:2512.20399v3 Announce Type: replace Abstract: We present GeoTransolver, a multiscale geometry-aware physics attention transformer for Computer Aided Engineering (CAE). GeoTransolver extends the

GRAG: Generic Response-Augmented Generation Framework for Personalized Conversational Systems

arXiv:2606.21097v1 Announce Type: cross Abstract: Deploying highly capable personalized conversational agents in resource-constrained or privacy-sensitive environments remains a significant challenge.

Agents

Harnessing Agent Skills: Architectural Patterns and a Reference Architecture for Skill-Mediated LLM Agents

arXiv:2606.20631v1 Announce Type: cross Abstract: Agent skills externalise reusable agent-facing behavioural knowledge and guidance as persistent artefacts that can be discovered, activated, and inter

agentsarxiv-cs-lg

HERO: Hypothesis-Driven Evidence Retrieval from Omics for Multi-Task Breast Cancer Analysis

arXiv:2606.21174v1 Announce Type: new Abstract: Matched multi-omics can improve WSI-based biomarker and prognosis prediction, but most existing pipelines use omics as a paral lel feature stream or tex

IndicGuard: A Multilingual Safety Guard Model and Dataset for Indic Languages

arXiv:2606.22841v1 Announce Type: cross Abstract: As Large Language Models (LLMs) achieve widespread integration across diverse linguistic landscapes, ensuring their safety and alignment with regional

Intent-Handover: Grounding Language in Human-Usage Regions for Trustworthy Robot-to-Human Handovers

arXiv:2503.03579v2 Announce Type: replace-cross Abstract: Spoken instructions in robot-to-human handovers may specify either an object ('the cup') or an intended use ('pour water'); in both cases, suc

Local Ai

Keeping the Evidence Chain: Semantic Evidence Allocation for Training-Free Token Pruning in Video Temporal Grounding

arXiv:2603.05663v2 Announce Type: replace Abstract: Video Temporal Grounding (VTG) localizes the temporal boundaries of query-relevant moments in long videos, making video-language-model prohibitively

local-aiarxiv-cs-cv

KEMO: Event-Driven Keyframe Memory for Long-Horizon Robot Manipulation with VLA Policies

arXiv:2606.23589v1 Announce Type: new Abstract: Long-horizon robot manipulation remains challenging because similar observations may occur at different execution stages, while the appropriate action d

applicationsarxiv-cs-ro

Learning Chern Numbers of Topological Insulators with Gauge Equivariant Neural Networks

arXiv:2502.15376v2 Announce Type: replace Abstract: Equivariant network architectures are a well-established tool for predicting invariant or equivariant quantities. However, almost all learning probl

LLM-Based Generalizable Hierarchical Task Planning and Execution for Heterogeneous Robot Teams with Event-Driven Replanning

model-releasesarxiv-cs-ro

arXiv:2511.22354v2 Announce Type: replace Abstract: This paper introduces CoMuRoS (Collaborative Multi-Robot System), a generalizable hierarchical architecture for heterogeneous robot teams that unifi

LSTM Variants for Chaotic Dynamical Systems: An Empirical Study on the Lorenz Attractor

arXiv:2606.22662v1 Announce Type: new Abstract: Forecasting chaotic dynamical systems such as the Lorenz attractor is notoriously difficult: small numerical errors are amplified exponentially over lon

MIRCaps: A Large-Scale Mixed-Domain Dataset with Image-Level and Region-Level Captions for Fine-Grained Vision-Language Learning

arXiv:2606.21419v1 Announce Type: new Abstract: Despite recent progress in Vision-Language Models (VLMs), mixed-domain image-caption datasets for both general-purpose and CCTV-based video surveillance

tutorialsarxiv-cs-cv

Mitigating Cross-Image Information Leakage in Multi-Image Understanding with Large Vision-Language Models

arXiv:2508.13744v2 Announce Type: replace Abstract: Large Vision-Language Models (LVLMs) exhibit strong performance on single-image tasks. However, their performance degrades significantly when handli

MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis

arXiv:2212.04495v3 Announce Type: replace Abstract: Conventional methods for human motion synthesis are either deterministic or struggle with the trade-off between motion diversity and motion quality.

Native space based pipelines outperform template space based pipeline in subcortical segmentation

arXiv:2606.21463v1 Announce Type: new Abstract: Accurate segmentation of subcortical regions is critical for neurosurgical planning and functional research. Most automated methods rely on template spa

tutorialsarxiv-cs-cv

Neural Concept Verifier: Scaling Prover-Verifier Games via Concept Encodings

arXiv:2507.07532v4 Announce Type: replace Abstract: While Prover-Verifier Games (PVGs) offer a promising path toward verifiability in nonlinear classification models, they have not yet been applied to

applicationsarxiv-cs-lg

On the Expressive Power of Weight Quantization in Large Language Models

arXiv:2606.22249v1 Announce Type: new Abstract: In recent years, weight quantization that encodes the learnable parameters of large language models in an n-bit format has garnered significant attentio

Probe-and-Refine Tuning of Repository Guidance for Coding Agents

arXiv:2606.20512v2 Announce Type: replace-cross Abstract: LLM-based coding agents need higher-level operational knowledge about a repository (which files house which subsystems, how to run the test su

Protocol-Aware Tokenization and Architecture Co-Design for Wireless Packet Foundation Models

arXiv:2606.20587v1 Announce Type: cross Abstract: What matters more for building foundation models for wireless packet traces: the tokenizer or the architecture or both? To answer this question, we bu

Provable Learning of Random Hierarchy Models and Hierarchical Shallow-to-Deep Chaining

arXiv:2601.19756v2 Announce Type: replace Abstract: The empirical success of deep learning is often attributed to deep networks' ability to exploit hierarchical structure in data, constructing increas

tutorialsarxiv-cs-lg

ReconMIL: Synergizing Latent Space Reconstruction with Bi-Stream Mamba for Whole Slide Image Analysis

arXiv:2603.19925v2 Announce Type: replace-cross Abstract: Whole slide image (WSI) analysis heavily relies on multiple instance learning (MIL). While recent methods benefit from large-scale foundation

Remember what you did?: Learning Behavioral Memories for Partially Observable Object Manipulation

arXiv:2606.21188v1 Announce Type: new Abstract: Long horizon, contact-rich manipulation is inherently partially observable. This is as a single visual observation rarely captures a robot's full action

safetyarxiv-cs-ro