Text‑Only Language Models Face Unlearnable ‘Information Shadow’ Limits
Researchers introduce the “information shadow,” a set of phenomena that language models trained only on text can never learn, no matter how much data or compute they receive, due to limits in expressibility, identifiability, or gradient training. By releasing probes for each limit, they expose hidden gaps in benchmarks and argue capability assessments must account for these structural blind spots.