A new method for detecting silent evidence loss in compressed AI contexts
Researchers have proposed SELD, a training-free technique to identify 'silent evidence loss' in long-context compression. This occurs when key supporting information is removed, leaving an answer technically correct but unsupported, which poses risks for systems relying on context reuse.