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Microstructure-Conditioned Surrogate Models for Graded Multiscale Optimization of Mycelium Composites

arXiv:2607.13688v1 Announce Type: new Abstract: Emerging sustainable materials increasingly rely on engineered hierarchy and microstructure to achieve control of their properties and mechanical behavi

applicationsarxiv-cs-lg

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Research

Min-Max Regret Task Allocation and Planning of Heterogeneous Multi-Robot System in Partially Known Environments

DGX agent

arXiv:2607.13403v1 Announce Type: new Abstract: Efficient task allocation for large-scale Heterogeneous Multi-Robot Systems (HMRS) is critical, yet dealing with complex temporal logic tasks in partial

researcharxiv-cs-ro

16 Jul 2026

Mind the Gap: Action Rebinding Attacks against Android GUI Agents

arXiv:2601.12349v3 Announce Type: replace-cross Abstract: Large multimodal model powered GUI agents are emerging as high-privilege operators on mobile platforms, entrusted to perceive screen content a

Mixed-Timescale Differential Coding for Downlink Model Broadcast in Wireless Federated Learning

model-releasesarxiv-cs-lg

arXiv:2607.13119v1 Announce Type: cross Abstract: In standard federated learning systems, the parameter server broadcasts the global model to the participating devices in every iteration. Motivated by

Mono-Z Dark Matter Search with Neural Spline Flows Using CMS Run 2015D Open Data

model-releasesarxiv-cs-lg

arXiv:2607.13771v1 Announce Type: new Abstract: We report a search for dark matter (DM) produced in association with a leptonically decaying (Z) boson at (sqrt{s}=13) TeV using CMS Run 2015D open data

Agents

Multi-Agent Collaborative Reasoning with Tool-Augmented Evidence for Urban Region Profiling

arXiv:2607.13558v1 Announce Type: new Abstract: Urban region profiling constitutes a core problem in urban computing, supporting applications such as population estimation, economic assessment, and en

agentsarxiv-cs-ai

Tutorials

Multi-Dictionary Learning for Low Rank Sparse Coding

arXiv:2509.10033v2 Announce Type: replace Abstract: Sparse dictionary coding represents signals as linear combinations of a few dictionary atoms. It has been applied to images, time series, graph sign

tutorialsarxiv-cs-lg

Multi-Expert Routing for Multi-Domain Low-Resource OCR: A Manchu Case Study

arXiv:2607.14041v1 Announce Type: cross Abstract: Historical Manchu OCR must accommodate various visually distinct writing styles, including regular script, running script, and the semi-cursive chance

applicationsarxiv-cs-ai

Multi-view Hand Reconstruction with a Point-Embedded Transformer

arXiv:2408.10581v3 Announce Type: replace Abstract: This work introduces a novel and generalizable multi-view Hand Mesh Reconstruction (HMR) model, named POEM, designed for practical use in real-world

applicationsarxiv-cs-cv

MultiAnimate: A Unified Framework for Controllable Multi-Character Animation

arXiv:2607.13415v1 Announce Type: new Abstract: Recent advances in generative models and technological innovations have significantly addressed the fundamental challenges of character image animation.

researcharxiv-cs-cv

Local Ai

Multimodal Assessment of Pancreatic Cancer Resectability Using Deep Learning

arXiv:2607.13826v1 Announce Type: cross Abstract: Accurate determination of pancreatic ductal adenocarcinoma (PDAC) resectability relies on evaluating how the tumor interacts with major peripancreatic

local-aiarxiv-cs-ai

Multimodal Empirical Bayes Variational Autoencoders for Joint Longitudinal and Time-to-Event Modeling

arXiv:2607.13984v1 Announce Type: cross Abstract: Longitudinal tumor measurements, dropout information, and genetic covariates provide complementary information about treatment response, but integrati

Music-to-Dance Generation via Atomic Movements

arXiv:2607.13978v1 Announce Type: cross Abstract: Music-driven dance generation aims to produce human motion that is both rhythmically synchronized and semantically consistent with music. While recent

MxGPS: Multiplex Graph Transformers for a Power Grid Foundation Model

arXiv:2607.13763v1 Announce Type: cross Abstract: Single-task fine-tuning of graph neural networks (GNNs) for power grid problems exhibits a systematic failure mode: models that achieve the lowest in-

Hardware

NanoGS: Training-Free Gaussian Splat Simplification

arXiv:2603.16103v2 Announce Type: replace Abstract: 3D Gaussian Splat (3DGS) enables high-fidelity, real-time novel view synthesis by representing scenes with large sets of anisotropic primitives, but

hardwarearxiv-cs-cv

NeMo: Needle in a Montage for Video-Language Understanding

arXiv:2509.24563v3 Announce Type: replace Abstract: Recent advances in video large language models (VideoLLMs) call for new evaluation protocols and benchmarks for video-language understanding. Inspir

Agents

Networked Intelligence: Active Shared Context Graphs for Human-AI Team Science

arXiv:2607.13220v1 Announce Type: new Abstract: Most AI-for-science systems focus on scaling a single reasoning process through better models, larger context windows, long-horizon agentic execution, o

agentsarxiv-cs-ai

New universal operator approximation theorem for encoder-decoder architectures

arXiv:2503.24092v2 Announce Type: replace-cross Abstract: Motivated by the rapidly growing field of mathematics for operator approximation with neural networks, we present a novel universal operator a

Nexus: Native Mesh Generation with Diffusion

arXiv:2607.13563v1 Announce Type: new Abstract: Generating high-quality triangle meshes is essential for film, gaming, and interactive 3D applications. Mainstream methods rely on mesh serialization an

researcharxiv-cs-cv

NodeImport: Imbalanced Node Classification with Node Importance Assessment

arXiv:2607.13837v1 Announce Type: cross Abstract: In real-world applications, node classification on graphs often faces the challenge of class imbalance, where majority classes dominate training, resu

applicationsarxiv-cs-ai

Non-Expansive Two-Time-Scale Stochastic Approximation: A Fixed-Schedule One-Quarter Barrier and Bias-Corrected Acceleration

arXiv:2607.13414v1 Announce Type: cross Abstract: Non-expansive two-time-scale stochastic approximation is governed by a slow stochastic Krasnoselskii--Mann fixed-point iteration rather than by contra

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs

arXiv:2601.02023v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) increasingly utilize massive context windows as working memory for autonomous tasks, their reliability fluctua

Not All Retrievals are Useful: Cross-Attention for Input-Aware RAG in Time Series Forecasting

arXiv:2603.14709v3 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) enhances zero-shot time series (TS) forecasting by leveraging external knowledge bases, yet existing approaches

NSNQuant: A Double Normalization Approach for Calibration-Free Low-Bit Vector Quantization of KV Cache

arXiv:2505.18231v3 Announce Type: replace-cross Abstract: Large Language Model (LLM) inference is typically memory-intensive, especially when processing large batch sizes and long sequences, due to th

nuTruck: Benchmarking Autonomous Driving Planning for Distributed Electric-drive Trucks

model-releasesarxiv-cs-ro

arXiv:2607.13704v1 Announce Type: new Abstract: The dominance of traditional rule-based methods in autonomous driving has gradually been replaced by learning-based approaches. While learning-based pla

OccTrack360: 4D Panoptic Occupancy Tracking from Surround-View Fisheye Cameras

arXiv:2603.08521v2 Announce Type: replace Abstract: Understanding dynamic 3D environments in a spatially continuous and temporally consistent manner is fundamental for robotics and autonomous driving.

On the Sublinear Regret of Continuous K-Max Bandits

arXiv:2502.13467v2 Announce Type: replace Abstract: The K-Max combinatorial multi-armed bandit problem arises in applications such as recommendation and distributed decision making, where the reward i

Operational Evidence Gaps for LLMs in Fraud Detection and Trust-and-Safety Workflows

arXiv:2607.13078v1 Announce Type: cross Abstract: LLMs are now proposed for fraud detection, scam investigation, content moderation, and other trust-and-safety workflows. Much of the public literature

Optimal and Efficient Contextual Combinatorial Semi-bandits with General Function Approximation

arXiv:2607.13686v1 Announce Type: new Abstract: We study the contextual combinatorial semi-bandit (CCSB) problem with general reward function approximation. At each round, the learner observes a conte

Optimizing Binary and Ternary Neural Network Inference on RRAM Crossbars using CIM-Explorer

arXiv:2505.14303v3 Announce Type: replace-cross Abstract: Using Resistive Random Access Memory (RRAM) crossbars in Computing-in-Memory (CIM) architectures offers a promising solution to overcome the v

Agents

Oracle Agent Memory as an Enterprise Memory Substrate for Long-Horizon AI Agents

arXiv:2607.13157v1 Announce Type: new Abstract: Agent memory is a systems problem for long-horizon agents. Practical deployments require retention of task state across extended conversations, recovery

agentsarxiv-cs-ai

OrDA: Orthogonal Disentanglement of Access Habits Framework for Homepage Marketing Block Recommendations

arXiv:2607.13420v1 Announce Type: new Abstract: Clicks on homepage marketing blocks are driven by a dual-mechanism of content interest and access habits. However, habitual clicks often create Pseudo-P

OriginBlame: Record- and Token-Level Data Provenance for AI Training Datasets

arXiv:2607.13037v1 Announce Type: new Abstract: When a data contributor requests removal, model trainers face a practical gap: unlearning algorithms require a forget set, yet no tool can locate which

researcharxiv-cs-ai

OvisOCR2 Technical Report

arXiv:2607.13639v1 Announce Type: cross Abstract: We introduce OvisOCR2, a 0.8B document parsing model. OvisOCR2 is designed as an end-to-end parser: given a document page image, it generates a Markdo

Parallel gradient boosting for flexible estimation of conditional distributions

arXiv:2607.13550v1 Announce Type: cross Abstract: Boosting is one of the most successful learning techniques for standard classification and regression tasks. Its extension to multi-output prediction

Parsimonious disturbance-aware minimum-time planning with parametric uncertainty

model-releasesarxiv-cs-ro

arXiv:2607.13312v1 Announce Type: new Abstract: This study presents and validates a minimum-lap-time planning (MLTP) framework for motorsport applications that embeds robustness against both state dis

Partially Correlated Verifier Cascades in LLM Harnesses: Concave Log-Odds, Polynomial Reliability, and Blind-Spot Ceilings

arXiv:2607.13918v1 Announce Type: cross Abstract: Serial verification gates are a core reliability primitive in LLM harnesses: a candidate answer is returned only if k verifier calls all accept it. Un

PC-Diffuser: Path-Consistent Capsule CBF Safety Filtering for Diffusion-Based Trajectory Planner

arXiv:2603.10330v2 Announce Type: replace-cross Abstract: Autonomous driving in complex traffic requires planners that generalize beyond hand-crafted rules, motivating data-driven approaches that lear

Peak-End-Net: A Peak-End Rule Inspired Framework for Generalizable Video Aesthetic Assessment

arXiv:2607.13941v1 Announce Type: new Abstract: Video aesthetic assessment (VAA) aims to predict how aesthetically pleasing a video is, yet remains far less explored than other visual assessment tasks

Local Ai

Perfect Demo Makes Poor Teacher: Learning Robust Alignment from Critical Motion Segments

arXiv:2606.15587v2 Announce Type: replace Abstract: Expert demonstrations are widely assumed to be the gold standard for robot imitation learning. Yet for fine-grained manipulation such as insertion,

local-aiarxiv-cs-ro

PersGuard: Preventing Malicious Personalization in Text-to-Image Diffusion Models via Model Backdoors

arXiv:2502.16167v2 Announce Type: replace-cross Abstract: Diffusion models (DMs) have advanced text-to-image (T2I) synthesis, yet their personalization capabilities raise serious privacy and copyright

researcharxiv-cs-ai

PhysClaw-0: A Symbiotic Agentic System for Robot Autonomy via Language Corrections

arXiv:2607.14047v1 Announce Type: new Abstract: Autonomous data collection governs the volume and quality of real-world trajectories for manipulation policy learning. Existing pipelines reduce human e

safetyarxiv-cs-ro

PiVoT: A Variational Solution for Real-time Large-scale Multi-object Detection and Tracking under Heavy Clutter

arXiv:2607.13891v1 Announce Type: cross Abstract: Multi-object detection and tracking from noisy point clouds remain challenging in many data-scarce radar applications. Current Bayesian trackers based

Plausible Deniability Guarantees for Whistleblowers

arXiv:2607.13928v1 Announce Type: cross Abstract: Whistleblowers are a key safeguard against organizational wrongdoing, but the threat of retaliation deters reporting. Existing whistleblower-protectio

PlumeQuant: Uncertainty-aware consistency assessment of methane plume masks and emission-rate estimates

arXiv:2607.13945v1 Announce Type: new Abstract: Imaging spectrometers increasingly distribute source-resolved methane plume products in which the plume mask, integrated mass enhancement (IME), plume l

researcharxiv-cs-cv

Tutorials

PokeNet: Learning Kinematic Models of Articulated Objects from Human Observations

arXiv:2602.02741v2 Announce Type: replace Abstract: Articulation modeling enables robots to learn joint parameters of articulated objects for effective manipulation which can then be used downstream f

tutorialsarxiv-cs-ro

Policy of Thoughts: Scaling Test-Time Training for LLM Reasoning via Online Policy Evolution

arXiv:2601.20379v2 Announce Type: replace Abstract: Large language models (LLMs) struggle with complex, long-horizon reasoning due to instability caused by their frozen policy assumption. Current test

Post-Disaster Affected Area Segmentation with a Vision Transformer (ViT)-based EVAP Model using Sentinel-2 and Formosat-5 Imagery

arXiv:2507.16849v3 Announce Type: replace-cross Abstract: We propose a vision transformer (ViT)-based deep learning framework to refine disaster-affected area segmentation from remote sensing imagery,

researcharxiv-cs-ai