Author

Fatemeh Seyedin

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Conference Open access Sep 2026

Temporal Graph Neural Network-Based Anomaly Detection in in-Vehicle CAN-Bus Sensor Data

Modern vehicles rely on Controller Area Network (CAN) communication, where detecting anomalies in multivariate sensor data is vital for safety and reliability. To capture diverse anomalies in multivariate time series, models must learn both temporal and spatial dependencies. Graph neural network (GNN)-based approaches...

Fatemeh Seyedin, Matthias Becker · IEEE International Conferenc... · 0 citations

#artificial intelligence Preprint Sep 2026

Fair Fact-Checking: Closing the Cross-Lingual Gap in LLM Factual Judgement with RoSh

RoSh is proposed, a per-language shift and rotation of the residual stream, computed in closed form at three layers, with no training and no weight modified, that improves every model and closes 75% of the gap on average, helping most where the model was worst.

Muhammad Ahmad, Fatemeh Seyedin, Adrian Weller et al. · 0 citations

Preprint Aug 2026

Social Behavior Among Autonomous AI: How Large Language Models Interact in Dynamic Networks

Examination of the interactions of AI using Large Language Models using Mistral, Llama3, Gemma3, and Phi3 in a public goods game within dynamic network structures offers a preliminary framework for LLM-based simulations in adaptive social networks.

Narges Fardnia, Fatemeh Seyedin, Matthias Becker et al. · 1 citation

Preprint Aug 2026

The Politician, the Liar, and the Obedient Worker: Emerging Behavior of LLM Agents in Hierarchical Games

LLMs are rapidly embedding themselves into daily life: drafting our emails, managing our schedules, and making decisions on our behalf. As they move from individual tools to participants in multi-agent organizations, an important question arises: do they reproduce the governance failures like free-riding, corruption, a...

Fatemeh Seyedin, Adrian Weller, Jinhyuk Yun et al. · 0 citations