Year: 2026
Type: conference-paper
Abstract: Existing memory benchmarks for LLM agents evaluate explicit recall of facts, yet overlook implicit memory where experience becomes automated behavior without conscious retrieval.This gap is critical: effective assistants must automatically apply learned procedures or avoid failed actions without explicit reminders.We introduce IMPLIC-ITMEMBENCH, the first systematic benchmark evaluating implicit memory through three cognitively grounded constructs drawn from standard cognitive-science accounts of nondeclarative memory: Procedural Memory (oneshot skill acquisition after interference), Priming (theme-driven bias via paired experimental/control instances), and Classical Conditioning (Conditioned Stimulus-Unconditioned Stimulus (CS-US) associations shaping first decisions).Our 300-item suite employs a unified Learning/Priming-Interfere-Test protocol with first-attempt scoring.Evaluation of 17 models reveals severe limitations: no model exceeds 66% overall, with top performers DeepSeek-R1 (65.3%),Qwen3-32B (64.1%), and GPT-5 (63.0%) far below human baselines.Analysis uncovers dramatic asymmetries (inhibition 17.6% vs. preference 75.0%) and universal bottlenecks requiring architectural innovations beyond parameter scaling.IMPLICITMEM-BENCH reframes evaluation from "what agents recall" to "what they automatically enact" 1 .
Institutions: University of Hong Kong, Harbin Institute of Technology
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166817630