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IMT Atlantique

Academic institutioneurope · fr
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Research library114linked papers
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Selected work

Representative Papers

MSA-technique for stiffness modeling of manipulators with complex and hybrid structures

Nov 19, 2025IFAC Symposium on Robot Control

Traditional stiffness modeling methods struggle to simultaneously achieve accuracy, computational efficiency, and topological adaptability for complex hybrid-configured robotic manipulators featuring closed-loop kinematics, flexible links, rigid/elastic joints, and coupled preload–external load conditions. To address this, this paper proposes a Modular Stiffness Analysis (MSA) framework that integrates matrix structural analysis, screw theory, and finite-element discretization. It is the first work to systematically introduce a modular strategy into stiffness modeling, enabling flexible composition and rapid analytical derivation for diverse configurations—including rigid–flexible coupling and parallel topologies. The resulting global stiffness matrix achieves over 30% higher computational efficiency compared to conventional approaches, with modeling errors bounded by ≤5%. Comprehensive validation across multiple representative hybrid-architecture manipulators demonstrates both high accuracy and strong generalizability.

12 citationsRead paper

Origin Lens: A Privacy-First Mobile Framework for Cryptographic Image Provenance and AI Detection

Feb 03, 2026

This work proposes a privacy-first mobile framework for verifying image authenticity in the face of escalating visual disinformation driven by generative AI. Addressing the challenge of balancing user privacy with on-device verification capabilities, the system introduces a novel multi-signal local verification mechanism that integrates encrypted image provenance tracking, generative model fingerprinting, and optional retrieval-augmented analysis—all executed directly on the user’s device. Built on a Rust/Flutter hybrid architecture, the framework enables high-privacy detection of AI-generated content and credibility scoring without uploading original data, thereby complying with regulatory requirements such as the EU AI Act. Furthermore, it interoperates with platform-level governance systems to deliver real-time, trustworthy verification capabilities to end users at the point of consumption.

2 citationsRead paper

The Verification Crisis: Expert Perceptions of GenAI Disinformation and the Case for Reproducible Provenance

Feb 02, 2026

This study addresses the systemic threat posed by generative AI–enabled multimodal disinformation to the information ecosystem, highlighting the lack of reproducibility and standardized benchmarks in current detection mechanisms. Through an initial longitudinal expert survey (N=21) involving AI researchers, policymakers, and disinformation specialists, the work synthesizes risk assessments of synthetic text, image, audio, and video content and evaluates the efficacy of existing mitigation strategies. Innovatively conceptualizing information integrity as critical infrastructure, the study proposes a response framework grounded in reproducible provenance standards and methodologies, advocating for standardized evaluation benchmarks and reproducibility checklists. Findings indicate that large-scale text generation presents greater systemic risk than deepfake videos, and experts broadly express skepticism toward purely technical detection approaches, favoring instead integrated governance solutions that combine provenance standards with regulatory frameworks.

2 citationsRead paper

Eroding the Truth-Default: A Causal Analysis of Human Susceptibility to Foundation Model Hallucinations and Disinformation in the Wild

Jan 30, 2026

This study addresses the growing threat posed by increasingly realistic misinformation generated by foundation models to trustworthy online information ecosystems. The authors propose a dual-axis framework, JudgeGPT and RogueGPT, which decouples “factual accuracy” from “source attribution,” and introduces the concept of the “fluency trap” to elucidate the cognitive mechanisms underlying human susceptibility to hallucinations. Leveraging a structural causal model, 918 human evaluations, and comparisons across multiple models—including GPT-4 and Llama-2—the research finds that political orientation exerts minimal influence, whereas familiarity with fake news serves as a key mediating variable (r = 0.35). Notably, GPT-4–generated content achieves a human–machine confusion rate of 0.20. The findings advocate for “prebunking” interventions centered on cognitive source monitoring, offering both empirical grounding and a novel pathway toward fostering a more reliable information ecosystem.

2 citationsRead paper

Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems

Jan 29, 2026

This study addresses the growing threat posed by generative large language models (LLMs) in amplifying disinformation within digital ecosystems. To systematically investigate users’ ability to detect AI-generated fake news, the authors develop an end-to-end experimental framework integrating RogueGPT—a novel, controllable disinformation generation engine—and JudgeGPT, an evaluation platform. The framework incorporates multimodal content generation, LLM-assisted detection, cognitive inoculation interventions, and human perception experiments. Findings reveal that while human detection capabilities have improved, a dynamic adversarial interplay persists between generation and detection mechanisms. The proposed strategies demonstrate significant efficacy in mitigating risks associated with AI-generated disinformation, advancing the field beyond theoretical discourse toward empirically grounded countermeasures.

2 citationsRead paper
Recent publications

Latest Papers

TenderKG

Aug 14, 2026

This study addresses the challenges of data sparsity and scarce winning signals in public procurement recommendation research by constructing the first large-scale knowledge graph benchmark for French public procurement. By integrating multi-source heterogeneous side information with semantic correlation analysis, the proposed framework effectively mitigates data sparsity and facilitates competition-aware recommendation modeling. The project provides comprehensive statistical analyses and fills a critical gap in publicly available datasets within this domain. Furthermore, it establishes a novel evaluation benchmark for recommendation methods in high-stakes decision-making scenarios. Ultimately, this work significantly advances both academic research and practical applications of intelligent recommendation systems in public procurement by offering a robust foundation for future studies and real-world deployment.

0 citationsRead paper