New Continual Learning benchmark， "Morpheus" from @skyfallai
Shows an important area LLMs were never properly tested on.
Most AI benchmarks reward strong average scores inside environments whose rules barely change. But Continual learning demands more because deployed agents must detect shifts and revise behavior continuously.
Morpheus creates those shifts inside enterprise workflows involving resource allocation and scheduling under drift.
Each configuration change tests whether performance reflects fresh learning or recycled pretraining habits.
Morpheus asks the harder question： does the model update when rules， rewards， and constraints begin to drift？
Morpheus therefore separates continual learning from broad pretraining coverage， which conventional averages often confuse.
Overall， the benchmark finds that stable benchmark scores of frontier models concealed a harder truth： top frontier models were not truly adapting.
Check out their below graph. Task 1 tests how well the system allocates resources， while Task 2 tests whether it can keep scheduling correctly as conditions change. Task 1 stays fairly stable； Task 2 becomes volatile and sometimes collapses.