EvolveScaler paper generates evolving-context data with executable state machines

A new arXiv preprint, EvolveScaler, addresses situations where newer events in a long interaction can override or invalidate statements made earlier. The authors build synthetic datasets of such shifting information by pairing executable state machines with natural-language rendering, yielding material that tests how well models track what remains valid over time. The approach is aimed at benchmarking and training systems that must reason over dynamically changing contexts rather than static records.