ImplicitMemBench: Measuring Unconscious Behavioral Adaptation in Large Language Models (2026.acl-long)
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| Challenge: | Existing memory benchmarks for LLMs evaluate explicit recall of facts, yet overlook implicit memory where experience becomes automated behavior without conscious retrieval. |
| Approach: | They propose a benchmark that evaluates implicit memory using three constructs from non-declarative memory. |
| Outcome: | The new benchmark reframes evaluation from "what agents recall" to "what they automatically enact" no model exceeds 66% overall, with top performers far below human baselines . |
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| Challenge: | a new method for unlearning large language models is proposed to improve the performance of large language model models. |
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