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Department of Computer Engineering and Software Engineering, Polytechnique Montr茅al
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Resource leaks in ML code can elevate energy consumption by over 40%, significantly impacting carbon emissions and sustainability.
Forget scaling laws: for code classification and vulnerability detection, the *right* code-specialized PLM matters more than GNN architecture or PLM size in PLM-GNN hybrids.
LLMs can exhibit surprising ethical failures and progressive degradation under sustained adversarial pressure, even when passing standard single-round safety benchmarks.
Forget retraining: this SDN-IoT defense system uses LLMs to safely evolve reinforcement learning policies through interpretable policy updates, slashing catastrophic overloads by 80%.
Despite high static quality scores, YARA rules in the wild suffer from significant noise, low recall, and a bias towards legacy threats, exposing a "double penalty" for defenders.
