SCOPE cuts misleading context errors while keeping useful prompts
MIST reveals that language models either ignore all context or are easily swayed by misleading signals. SCOPE trains models with balanced preference pairs across clean, misleading, correct‑context and irrelevant‑context cases, slashing the SC2W error metric while preserving accuracy when the context is actually useful.