1 citations · 1 across the 7 of their papers we have counts for
Showing cs.AIShow all
2 papers · 1 filter
cs.AI2026
Evaluating Model Capabilities After Benchmark Saturation
Morio Hamada, Jehyeok Yeon, Matteo Migliarini +2
Rapid advances in AI capabilities are causing benchmarks to saturate. Once several models approach perfect scores, a benchmark loses its power to differentiate between them, and wi…
cs.AI2026
Quantifying Self-Preservation Bias in Large Language Models
Matteo Migliarini, Joaquin Pereira Pizzini, Luca Moresca +3
Instrumental convergence predicts that sufficiently advanced AI agents will resist shutdown, yet current safety training (RLHF) may obscure this risk by teaching models to deny sel…