FedLiTeCAN : A Federated Lightweight Transformer for Fast and Robust CAN Bus Intrusion Detection

This work implements a lightweight Transformer model for IDS in the domain of Connected and Autonomous Vehicles

FAST-IDS: A Fast Two-Stage Intrusion Detection System with Hybrid Compression for Real-Time Threat Detection in Connected and Autonomous Vehicles

We have implemented a multi-stage IDS for CAVs that can be deployed to resourec-constrained environments after hybrid model compression.

FedSecureFormer: A Fast, Federated and Secure Transformer Framework for Lightweight Intrusion Detection in Connected and Autonomous Vehicles

This works presents an encoder-only transformer built with minimum layers for intrusion detection in the domain of Connected and Autonomous Vehicles using Federated Learning.

neuralFOMO: Can LLMs Handle Being Second Best? Measuring Envy-Like Preferences in Multi-Agent Settings

Envy is a common human behavior that shapes competitiveness and can alter outcomes in team settings. As large language models (LLMs) increasingly act on behalf of humans in collaborative and competitive workflows, there is a pressing need to evaluate whether and under what conditions they exhibit envy-like preferences. In this paper, we test whether LLMs show envy-like behavior toward each other. We considered two scenarios: (1) A point allocation game that tests whether a model tries to win over its peer. (2) A workplace setting observing behaviour when recognition is unfair. Our findings reveal consistent evidence of envy-like patterns in certain LLMs, with large variation across models and contexts. For instance, GPT-5-mini and Claude-3.7-Sonnet show a clear tendency to pull down the peer model to equalize outcomes, whereas Mistral-Small-3.2-24B instead focuses on maximizing its own individual gains. These results highlight the need to consider competitive dispositions as a safety and design factor in LLM-based multi-agent systems.