Lacuna: Testing if LLM Unlearning Truly Erases Data
sivareddyg · x · 2026-07-04
This paper introduces Lacuna, a testing platform designed to evaluate the local precision of LLM unlearning. The core argument is that unlearning should genuinely erase knowledge rather than merely obfuscate it. This benchmark is used to verify whether unlearning methods actually delete the targeted information.
More from Research
- UniAction: unified action conditioning lets one world model handle mixed action types — ZeYanjie · 2026-10-27
- Artificial Analysis breaks down all sub-evaluations in Intelligence Index v4.3.2 — ArtificialAnlys · 2026-10-27
- Infini-AI Lab open-sources Hermit, a framework for agent harness evolution and continual learning — BeidiChen · 2026-10-27
- Meta FAIR's Self-Direction turns agent failures into reusable LoRA skills, +13.8pp on Terminal-Bench — karen_ullrich · 2026-10-27
- DONIC hits 86.79% on bimanual dexterous tracking, crushing prior SOTA's 9.43% — liuziwei7 · 2026-10-27
- Why don't more LLMs ship 10M-token context? A researcher asks about attention tradeoffs — agarwl_ · 2026-10-27