long-horizon agents

Researchers from Microsoft and City University of Hong Kong released Taste-Bench, a 502-question benchmark that tests whether models can pick the better path at decision forks in long agent runs. The top model, GPT-5.6 Sol, scored 59.7% against a 25% guessing baseline. The authors also show a distilled Qwen3.6-27B student can lift SWE-bench Pro success from 14.6% to 33.7%.