3 papers
cs.AI2026
Describe-Then-Act: Proactive Agent Steering via Distilled Language-Action World Models
Massimiliano Pappa, Luca Romani, Valentino Sacco +5
Deploying safety-critical agents requires anticipating the consequences of actions before they are executed. While world models offer a paradigm for this proactive foresight, curre…
cs.CL2025
LongCodeBench: Evaluating Coding LLMs at 1M Context Windows
Stefano Rando, Luca Romani, Alessio Sampieri +5
Context lengths for models have grown rapidly, from thousands to millions of tokens in just a few years. The extreme context sizes of modern long-context models have made it diffic…
cs.LG2025
SeRpEnt: Selective Resampling for Expressive State Space Models
Stefano Rando, Luca Romani, Matteo Migliarini +3
State Space Models (SSMs) have recently enjoyed a rise to prominence in the field of deep learning for sequence modeling, especially as an alternative to Transformers. Their succes…