Search: “framework”

arXiv:2607.26903v1 Announce Type: cross Abstract: The key bottleneck in embodied AI is not model architecture but data. Although billions of human manipulation videos exist online, robots cannot direc

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From Uncertainty to Determinism: Coarse-to-Fine Visual Floorplan Localization without Ray Matching

DGX agent

arXiv:2607.26817v1 Announce Type: cross Abstract: Visual Floorplan Localization (FLoc) has emerged as a promising solution for indoor localization by matching egocentric images against minimalist stru

local-aiarxiv-cs-cv

30 Jul 2026

From Unsupervised Subgroups to Hypothetical State-Intervention Policies: An Evaluation of Selected Subgrouping Methods in Observational Health Data

arXiv:2607.26521v1 Announce Type: new Abstract: Conventional subgroup analyses can yield unstable and difficult-to-interpret conclusions, especially in observational biomedical data where each individ

safetyarxiv-cs-lg

Gated Adaptation for Continual Learning in Human Activity Recognition

arXiv:2603.10046v2 Announce Type: replace Abstract: Wearable sensors in Internet of Things (IoT) ecosystems increasingly support applications such as remote health monitoring, elderly care, and smart

Global Sensitive-Based Input Shaping for UAV-Payload Precision Motion Control

arXiv:2607.26717v1 Announce Type: cross Abstract: This work presents a comprehensive analysis and design of global sensitivity-based input shapers for a 3D Unmanned Aerial Vehicle-payload system, emph

researcharxiv-cs-ro

GroupRAG: Cognitively Inspired Group-Aware Retrieval and Reasoning via Knowledge-Driven Problem Structuring

arXiv:2603.26807v2 Announce Type: replace-cross Abstract: The performance of language models is commonly limited by insufficient knowledge and constrained reasoning. Prior approaches such as Retrieval

applicationsarxiv-cs-cl

Local Ai

Hierarchical Spatio-Temporal Transformer for Coherent Emergency Department Forecasting

arXiv:2607.27106v1 Announce Type: new Abstract: Emergency Departments (EDs) are critical access points in healthcare systems, yet they face persistent pressure from unpredictable patient demand, seaso

local-aiarxiv-cs-lg

(Im)Paired Programming: Coding Agents Improve Productivity but Harm Understanding

arXiv:2607.26375v1 Announce Type: new Abstract: Coding agents (e.g., Cursor) improve developer productivity by optimizing task completion, but shifting users from writing code to prompting and reviewi

agentsarxiv-cs-cl

JEPADepth: Masked Predictive Representation Learning for Self-Supervised Monocular Depth Estimation

arXiv:2607.26600v1 Announce Type: new Abstract: Self-supervised monocular depth estimation typically relies on photometric reconstruction losses that couple depth, pose, and appearance assumptions. In

Kairos: Numerically Robust News Recommendation under Item Cold-Start via Cholesky-based LinUCB

arXiv:2607.26832v1 Announce Type: new Abstract: Algorithmic news personalization in regional markets often fails because modern deep learning models require massive interaction data while real-world n

applicationsarxiv-cs-lg

Kinetic Mining in Context: Few-Shot Action Synthesis via Text-to-Motion Distillation

arXiv:2512.11654v3 Announce Type: replace Abstract: The acquisition cost for large, annotated motion datasets remains a critical bottleneck for skeletal-based Human Activity Recognition (HAR). Althoug

Learning Dynamic User Personas from Implicit Interaction Streams via Iterative Refinement

arXiv:2607.26473v1 Announce Type: cross Abstract: Personalizing large language models (LLMs) to individual users is essential for improving user experience, yet existing approaches typically rely on e

applicationsarxiv-cs-cl

Lilith: Backdoor Generalization under Training-Inference Trigger Shift

arXiv:2607.26099v1 Announce Type: cross Abstract: Machine-learning services increasingly rely on public data, third-party providers, and outsourced training, creating opportunities for data-poisoning

safetyarxiv-cs-lg

Linguistic Monoculture in LLM-Assisted Language Use

arXiv:2607.27134v1 Announce Type: cross Abstract: Writing and communication are increasingly mediated by large language models (LLMs) that are being used to draft, revise and polish text. Although suc

researcharxiv-cs-cl

LISA-3D: Lifting Language-Image Segmentation to 3D via Multi-View Consistency

arXiv:2512.01008v2 Announce Type: replace Abstract: Text-driven 3D reconstruction requires masks that understand free-form instructions and remain stable under viewpoint changes. We present LISA-3D, a

LLM-Grounded Dynamic Task Planning with Hierarchical Temporal Logic for Human-Aware Multi-Robot Handover

arXiv:2602.09472v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) enable non-experts to specify open-world multi-robot tasks, but the generated plans are often kinematically infea

LLMET: Enabling Cross-Layer Evaluation of Emerging M3D Memories for Energy-Efficient LLM Serving

arXiv:2607.26491v1 Announce Type: cross Abstract: The energy consumption of Large Language Model (LLM) serving is becoming a major system challenge as deployment scales, driven by hardware power and t

Long-Tailed 3D Point Cloud Dataset Distillation

arXiv:2607.26763v1 Announce Type: new Abstract: Dataset distillation compresses large-scale datasets into compact synthetic sets while preserving their training utility, enabling efficient 3D point cl

safetyarxiv-cs-cv

Local Ai

MedARC: Training-Free Adaptive Redundancy Compression of Visual Tokens for 3D Medical Vision-Language Models

arXiv:2607.26554v1 Announce Type: new Abstract: Integrating 3D medical images with vision-language models (VLMs) holds substantial promise for computer-aided diagnosis. However, volumetric images gene

local-aiarxiv-cs-cv

Meta-Learned Reward Shaping for Reinforcement Learning from Human Feedback

model-releasesarxiv-cs-cl

arXiv:2607.26094v1 Announce Type: cross Abstract: Reinforcement Learning from Human Feedback (RLHF) is the standard approach for aligning large language models with human preferences, but its quality

MetaKoopman: Bayesian Meta-Learning of Koopman Operators for Modeling Structured Dynamics under Distribution Shifts

arXiv:2607.26345v1 Announce Type: new Abstract: Modeling and forecasting nonlinear dynamics under distribution shifts is essential for robust decision-making in real-world systems. In this work, we pr

agentsarxiv-cs-lg

Metis: Memory Foundation Model

arXiv:2607.26760v1 Announce Type: new Abstract: Recent advances in AI agents have increasingly internalized native capabilities into their underlying foundation models, giving rise to multimodal found

agentsarxiv-cs-cl

MindForge: Teaching Small Language Models Whole-Life-Cycle Software Engineering via Source-Free Program Synthesis

arXiv:2607.27146v1 Announce Type: cross Abstract: Coding agents have made substantial progress on software engineering tasks that modify existing codebases, including bug fixing and feature implementa

agentsarxiv-cs-cl

MoMo: Dial Motion Mode in Robot Manipulation with Spatiotemporal Action Tokenization

arXiv:2607.26315v1 Announce Type: new Abstract: To operate effectively across diverse contexts, robots must not only perform manipulation tasks accurately but also adapt how their actions unfold to th

researcharxiv-cs-ro

Local Ai

MoSAIC: Aligned Intervention Supervision for Part-Local Motion Style Transfer

arXiv:2607.26304v1 Announce Type: new Abstract: Editing character motion often requires transferring a gesture or gait from one or more reference motions while preserving the source action, timing, ro

local-aiarxiv-cs-cv

No Data Is Not No Risk: Visibility Aware Graph-Based Inference of Business Conduct Risk

arXiv:2607.26859v1 Announce Type: cross Abstract: The monitoring of business conduct risk is hindered by sparse, uneven, and visibility-biased data. Prior studies show that business conduct risk infor

OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning

arXiv:2505.22039v2 Announce Type: replace Abstract: While anomaly detection has made significant progress, generating detailed analyses that incorporate industrial knowledge remains a challenge. To ad

On-Policy Distillation for LLM Safety: A Routing Approach to Template-Robust Realignment

arXiv:2607.27081v1 Announce Type: cross Abstract: Fine-tuning is the dominant paradigm for specializing large language models (LLMs), yet it exposes a critical vulnerability: malicious data providers

safetyarxiv-cs-cl

On the Rademacher Complexity of Graph Neural Networks: Unifying Expressivity and Geometry

arXiv:2510.10101v4 Announce Type: replace Abstract: Understanding the interplay between generalization, expressivity, and the geometry of the input space is a central challenge in graph learning. The

Ontology-driven personalized information retrieval for XML documents

arXiv:2603.21139v2 Announce Type: replace-cross Abstract: This paper addresses the challenge of improving information retrieval from semi-structured eXtensible Markup Language (XML) documents. Traditi

PatchDenoiser: Parameter-efficient multi-scale patch learning and fusion denoiser for Low-dose CT imaging

arXiv:2602.21987v3 Announce Type: replace Abstract: Low-dose CT images are essential for reducing radiation exposure in cancer screening, pediatric imaging, and longitudinal monitoring protocols, but

PowerAtlas: Towards Electricity-Computing Co-Scheduling for Power Systems

arXiv:2607.26710v1 Announce Type: new Abstract: The rapid growth of AI workloads is turning data centers into large-scale, volatile, yet spatiotemporally flexible grid loads, creating an urgent need f

Practice Makes Policies: Bootstrapping and Consolidating Robotic Capabilities from Zero Human Demonstrations

arXiv:2607.26809v1 Announce Type: new Abstract: General-purpose robotic manipulation requires robots to perform diverse tasks in open-world environments while improving their skills over time. Despite

agentsarxiv-cs-ro

PRISM-Net: Patient-specific reference-guided inter-breast symmetry matching for three-class breast DCE-MRI classification

arXiv:2607.26799v1 Announce Type: new Abstract: Breast DCE-MRI AI is increasingly being explored for breast-level classification of no-lesion, benign, and malignant findings, beyond conventional lesio

Progressive Multimodal Alignment for Continual Instruction Tuning

arXiv:2607.26947v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) rely on a projector to align visual representations with the language embedding space, making it central to cro

RA-Det: Towards Universal Detection of AI-Generated Images via Robustness Asymmetry

arXiv:2603.01544v2 Announce Type: replace Abstract: Recent image generators produce photo-realistic content that undermines the reliability of downstream recognition systems. As visual appearance cues

Rad-JEPA 3D: Radiology Joint-Embedding Predictive Model for 3D Computed Tomography

arXiv:2607.26196v1 Announce Type: new Abstract: Self-supervised pretraining is central to 3D medical image analysis, where unlabeled CT volumes are abundant but expert annotations are scarce. Yet exis

ReDiSC: A Reparameterized Masked Diffusion Model for Scalable Node Classification with Structured Predictions

arXiv:2507.14484v2 Announce Type: replace Abstract: In recent years, graph neural networks (GNN) have achieved unprecedented successes in node classification tasks. Although GNNs inherently encode spe

Representation Trajectories Matters: Complementary Evidence for OOD Detection and Image Classification

arXiv:2607.26565v1 Announce Type: new Abstract: Vision models do not form a representation at once; each block revises it. We ask whether the resulting computation path contains evidence that the fina

Rethinking Self-Evolution: A Constrained Exploration-Exploitation Process for Mitigating Skill Overfitting

arXiv:2607.26643v1 Announce Type: cross Abstract: Enabling large language model (LLM) agents to accumulate and reuse experience from past interactions remains a central challenge in real-world applica

Revisiting Lossy Verification in Speculative Decoding: Mechanisms, Trade-offs, and Failure Modes

arXiv:2607.26627v1 Announce Type: new Abstract: Speculative Decoding (SD) accelerates large language model inference by allowing a lightweight draft model to propose tokens that are subsequently verif

researcharxiv-cs-cl

ScalablePromptus: Scalable and High-Fidelity Prompt-Based Video Streaming

arXiv:2607.26106v1 Announce Type: cross Abstract: Prompt-based video streaming transmits compact semantic prompts instead of pixel-level content for generative reconstruction, enabling ultra-low-bitra

applicationsarxiv-cs-cv

Scientific Knowledge Discovery in the Age of Large Language Models

arXiv:2607.26670v1 Announce Type: cross Abstract: The rapid growth of scholarly literature has made identifying relevant publications increasingly difficult, and conventional search systems still depe

researcharxiv-cs-cl

SciFigQual-Bench: A Benchmark for Scientific Figure Quality Assessment with Full-Manuscript Context

arXiv:2607.27084v1 Announce Type: new Abstract: Scientific images are the core elements of presenting experimental conclusions, elaborating system architecture, and supporting comparative arguments in

SeasonStereo: Robust Dense Stereo Matching for Multi-Date Satellite Imagery via Generative AI

arXiv:2607.27139v1 Announce Type: new Abstract: Accurate 3D reconstruction from satellite imagery typically relies on near-simultaneous stereo pairs, limiting its applicability to diachronic settings

See2Think: Do Multimodal Models Really Use Intermediate Visual States?

arXiv:2607.26769v1 Announce Type: new Abstract: Multimodal large language models increasingly use sketches, annotations, tools, and intermediate images during reasoning, but it remains unclear whether

applicationsarxiv-cs-cv

Semantic-Aware Temporal Adaptation for UAV Anti-UAV Tracking

arXiv:2607.26511v1 Announce Type: new Abstract: UAV Anti-UAV tracking is an emerging low-altitude security task for localizing an adversarial UAV using the onboard camera of a moving observer UAV. It

Semi-Decentralized Multi-Spacecraft Collision Avoidance under Communication Constraints

arXiv:2607.26570v1 Announce Type: new Abstract: Current spacecraft collision-avoidance operations rely on intermittent ground-station contacts, requiring operators to plan with delayed and asynchronou

agentsarxiv-cs-ro