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artificial intelligence

12,063 papers

#artificial intelligence Conference Open access Aug 2026

STR-Agent: An LLM-Driven Agent for QoS-Aware Routing in LEO Satellite Networks

STR-Agent is proposed, an LLM-driven framework for QoS-aware routing in LEO satellite networks that significantly outperforms conventional baselines, and results demonstrate the potential of LLM-driven agent architectures to enable serviceaware and adaptive QoS routing in future LEO satellite networks.

Bo-Wen Lu, Mu-Gen Peng, Yaohua Sun et al. · 0 citations
#artificial intelligence Preprint Open access Sep 2026

LLM-Guided Transformation of Non-Critical Driving Scenes into Safety-Critical Scenarios Using Augmented Reality

Testing Autonomous Driving Systems (ADS) requires realistic safety-critical scenarios, but collecting such data from real-world driving is costly and unsafe. This paper presents an automated pipeline that transforms safe driving scenes into safety-critical scenarios by combining computer vision, Large Language Models (...

Noura Fady, Farah Khaled, Catherine M. Elias · 0 citations
#artificial intelligence Preprint Open access Sep 2026

Human and AI-generated texts between modal logic and statistics

We read the geometry of semantic neighbourhood graphs as modal logic and give that reading a statistical form, in order to make precise the structural difference between human and machine-generated text. Texts are the worlds of a finite frame whose accessibility is the $k$-nearest-neighbour relation of a transformer em...

Simone Cuconato, Donato Ferrari · 0 citations
#artificial intelligence Preprint Open access Sep 2026

Labeled Incidence Structures for Native Transformer Modeling of Text, Knowledge Graphs, and Hypergraphs

Text, knowledge graphs, and hypergraphs all have elements that play distinct roles within relation instances, structure that is lost when data is flattened into token sequences. We introduce labeled incidence structures (LIS), a uniform representation that encodes each endpoint as $(x_d, s, e)$: content $x_d$, a role o...

Mahesh Godavarti · 0 citations
#artificial intelligence Preprint Open access Sep 2026

A Multi-Objective Optimisation Framework for Corticomuscular EEG-EMG Pair Selection in Hybrid BCI

Hybrid brain-computer interface (BCI) systems that integrate electroencephalography (EEG) and electromyography (EMG) signals have shown significant potential in improving the reliability of motor imagery (MI) classification, particularly in neuro-rehabilitation applications. However, identifying informative EEG-EMG cha...

Dekka Muni Kumar, Yogesh Kumar Meena · 0 citations
#artificial intelligence Preprint Aug 2026

A Hybrid Gaze-Motor Imagery BCI Framework for Effective Decision Communication

Non-invasive brain-computer interfaces (BCIs) and eye-tracking technologies offer promising communication pathways; however, motor imagery (MI)-based BCIs often suffer from low discriminability and high inter-subject variability. To mitigate these issues, this study investigates the impact of visual fixation on neural...

N. GowthamReddy, KongFatt Wong-Lin, Y. Meena · 0 citations
#artificial intelligence Preprint Open access Sep 2026

CleanVideo: Adaptive Concept Erasure for Text-to-Video Diffusion Models

Concept erasure aims to selectively eliminate undesired visual semantics from pre-trained generative models without compromising their general utility. Extending concept erasure from images to video is nontrivial. Target concepts emerge gradually and vary across frames and denoising steps. As a result, fixed interventi...

Junchi Liao, Hongji Li, Wenrui Zhou et al. · 0 citations
#artificial intelligence Preprint Open access Sep 2026

Risk-Set Transported Synthetic Control with Difference-in-Differences Adjustment under Staggered Treatment Adoption

In staggered treatment-adoption designs, later-treated units are valid controls for an earlier-treated cohort only until their own treatment begins, so the admissible donor set contracts with event time. Fixing the donor pool at the longest horizon discards temporarily eligible donors, whereas re-estimating synthetic-c...

Mojtaba Eslami · 0 citations
#artificial intelligence Preprint Jul 2026

Lens: Bringing the Right Semantic Perspective into Focus for Training-Free Multimodal Representation Learning

High-quality representations are essential for a wide range of downstream tasks. Dedicated embedding models are explicitly optimized for representation learning, yet their training data are often more limited in scale and diversity than the massive corpora used to pretrain modern large language models and multimodal la...

Xin-Ran Liu, Shouqian Shi, Yi-Xian Chen et al. · 0 citations
#artificial intelligence Preprint Open access Sep 2026

Self-complementary completions on six vertices

Let \(\cthreshold(n)\) be the largest integer \(q\) such that every loopless digraph on \(n\) vertices with at most \(q\) arcs is isomorphic to a spanning subdigraph of a self-complementary digraph of order \(n\). We prove that \(\cthreshold(6)=7\). The upper bound is witnessed by \[ \bK{3}\dunion (x\longrightarrow y\l...

Xinan Dai, Wenhao Deng, Yingdong Shi et al. · 0 citations
#artificial intelligence Preprint Open access Sep 2026

Is It Still Worth Training a Classical Model in the Era of LLMs? A Crossover Benchmark on Tabular Data

Large language models can label a tabular row from a plain-English description with no training - a capability now shipping in mainstream spreadsheet tools such as Microsoft Copilot in Excel and Anthropic's Claude for Excel - raising a practical question for the many business prediction problems where labels are expens...

Kaihua Ding · 0 citations
#artificial intelligence Preprint Open access Sep 2026

Scene-Conditioned Relation Routing for urban cellular activity forecasting

Urban cellular activity forecasting requires jointly modeling heterogeneous spatiotemporal signals, including SMS usage, mobile network traffic, and call activity. Existing methods often separate temporal modeling, spatial relation learning, and multi-signal prediction, relying on fixed graph structures or static multi...

Qingzhong Li, Jingye Lin, Hui Ma et al. · 0 citations

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MIT News · Artificial Intelligence Sep 29, 2026

Who we become when we talk to machines

Professor Sherry Turkle’s new book, “Artificial Intimacy,” offers a withering critique of chatbots and the antisocial dynamics she believes they encourage.

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