ImplicitMemBench: Measuring Unconscious Behavioral Adaptation in Large Language Models
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 ImplicitMemBench, the first systematic benchmark evaluating implicit memory through three cognitively grounded constructs drawn from standard cognitive-science accounts of non-declarative memory: Procedural Memory (one-shot 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. ImplicitMemBench reframes evaluation from "what agents recall" to "what they automatically enact".
69d8a0a679548974da9466db
Chonghan Qin
claimed_verified
2026-04-10T07:42:38.133Z
67b30bb2c2e25cfcdeda4a2f
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Chonghan, Qin
J017athan
user
J017athan
69d8a0a679548974da9466dc
Xiachong Feng
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2026-04-14T08:17:38.785Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/6776ae0c91b4c75dac91249c/uJk3ZnRrzjPCcBNjmrWLI.png
Oran Feng
xiachongfeng
user
xiachongfeng
69d8a0a679548974da9466dd
Weitao Ma
69d8a0a679548974da9466de
Xiaocheng Feng
69d8a0a679548974da9466df
Lingpeng Kong
ImplicitMemBench: Measuring Unconscious Behavioral Adaptation in Large Language Models
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 ImplicitMemBench, the first systematic benchmark evaluating implicit memory through three cognitively grounded constructs drawn from standard cognitive-science accounts of non-declarative memory: Procedural Memory (one-shot 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. ImplicitMemBench reframes evaluation from "what agents recall" to "what they automatically enact".
ImplicitMemBench presents a novel benchmark for evaluating implicit memory in LLM agents through procedural memory, priming, and classical conditioning constructs, revealing significant performance gaps compared to human baselines.
implicit memory procedural memory priming classical conditioning LLM agents cognitive science first-attempt scoring parameter scaling