A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Mike A. Merrill, Alexander G Shaw, Nicholas Carlini, et al.
Terminal-Bench 2.0 is a hard benchmark of 89 real-world-inspired CLI tasks where frontier agents score below 65%, with error analysis and public dataset/harness.
Ziheng Qin, Yaxin Lu, Zhangyang Atlas Wang, et al.
StateM is an agent-native runtime that improves long-horizon agent performance via harness scaling, reaching 95.3% raw accuracy on Terminal-Bench 2.1 without changing model weights.
Jose Juan Almagro Armenteros, Marco Salvatore, Olof Emanuelsson, et al.
TargetP 2.0 uses deep learning with attention to identify N-terminal sorting signals, revealing that residue 2 strongly influences classification, a feature not previously highlighted.