ImplicitMemBench: Measuring Unconscious Behavioral Adaptation in Large Language Models

• URL: http://arxiv.org/abs/2604.08064v1
• Date: Thu, 09 Apr 2026 10:26:32 GMT
• Title: ImplicitMemBench: Measuring Unconscious Behavioral Adaptation in Large Language Models
• Abstract summary: Existing memory benchmarks for LLM agents evaluate explicit recall of facts, yet overlook implicit memory where experience becomes automated behavior without conscious retrieval.<n>We introduce ImplicitMemBench, the first systematic benchmark evaluating implicit memory through three constructs drawn from standard cognitive-science accounts of non-declarative memory.<n>Our 300-item suite employs a unified Learning/Priming-Interfere-Test protocol with first-attempt scoring.
• Score: 60.14219417402433
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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".

Related papers

• Know It, Act on It: Investigating Memory Utilization in LLM Personalization [12.448786190019254]
  We show that large language models (LLM) fail to act on relevant user preferences even when they are fully present in context.<n>We introduce a decoupled evaluation paradigm that administers paired Know and Act tests to the same user preference.<n>We conduct large-scale experiments across 16 systems and five memory architectures, evaluating 1,000 preferences embedded at three levels of expression strength.
  arXiv  Detail & Related papers  (2026-07-31T13:57:21Z)
• Cybersecurity Detection Classification with Reasoning-enabled Language Models [50.72675435043546]
  A major issue in Security Operations Centers (SOCs) is alert fatigue.<n>Prior work prompts or fine-tunes large language models (LLMs) to emit a triage label directly, but does not train them to reason about whether a detection is a genuine threat.<n>We train a chain-of-thought (CoT) reasoning-enabled triage classifier on real, human-labeled Windows endpoint detections.
  arXiv  Detail & Related papers  (2026-07-30T16:22:13Z)
• MEMPROBE: Probing Long-Term Agent Memory via Hidden User-State Recovery [58.64329475683699]
  We argue that long-term memory should be evaluated as an auditable post-interaction artifact.<n>We instantiate this view in MEMPROBE, a benchmark in which a memory-equipped agent assists simulated users.<n>We find that successful assistance and recoverable memory behave as distinct capabilities.
  arXiv  Detail & Related papers  (2026-06-23T13:52:46Z)
• Let the Results Speak: A Replication-First Paradigm for LLM Behavioral Benchmarking [22.825786049667602]
  We propose a replication-first paradigm to anchor validity to a single human-rater consensus.<n>We certify the instrument via four properties -- reliability across K runs, cross-instrument replication across architecturally distinct judges, historical-footprint calibration via judges from earlier training cohorts, and pre-registered prediction.<n>We test it on emotional accompaniment by letting the self-evolve data-driven across: the dimensions are not pre-stipulated and the procedure stabilizes to a 9-dimension set.
  arXiv  Detail & Related papers  (2026-05-27T03:41:11Z)
• Human-Inspired Memory Architecture for LLM Agents [0.9507070656654629]
  We present a biologically-grounded memory architecture comprising six cognitive mechanisms.<n>Each mechanism addresses a specific failure mode of naive memory accumulation.<n>At S-tier scale (50 sessions), dedup-based consolidation yields a +13.3 pp improvement in preference recall.
  arXiv  Detail & Related papers  (2026-05-08T22:52:37Z)
• MemReranker: Reasoning-Aware Reranking for Agent Memory Retrieval [37.54115468116941]
  This report introduces MemReranker, a reranking model family (0.6B/4B) built on Qwen3-Reranker through multi-stage LLM knowledge distillation.<n>On the memory retrieval benchmark, MemReranker-0.6B substantially outperforms BGE-Reranker and matches open-source 4B/8B models as well as GPT-4o-mini on key metrics.<n>MemReranker-4B further achieves 0.737 MAP, with several metrics on par with Gemini-3-Flash, while maintaining inference latency at only 10-20% of large models.
  arXiv  Detail & Related papers  (2026-05-07T12:33:57Z)
• MemEvoBench: Benchmarking Memory MisEvolution in LLM Agents [78.95081012334116]
  Large Language Models (LLMs) with persistent memory enhances interaction continuity and personalization but introduces new safety risks.<n> contaminated or biased memory accumulation can trigger abnormal agent behaviors.<n>MemeEvoBench is the first benchmark evaluating memory safety in LLM agents against adversarial memory injection, noisy tool outputs, and biased feedback.
  arXiv  Detail & Related papers  (2026-04-17T07:29:52Z)
• Agentic Cognitive Profiling: Realigning Automated Alzheimer's Disease Detection with Clinical Construct Validity [66.94391219005291]
  This paper proposes Agentic Cognitive Profiling (ACP), an agentic framework that realigns automated screening with clinical protocol logic.<n>Central to our design is decoupling semantic understanding from measurement by delegating all quantification to deterministic function calling.<n>ACP achieves 90.5% score match rate in task examination and 85.3% accuracy in AD prediction, surpassing popular baselines.
  arXiv  Detail & Related papers  (2026-03-18T06:15:35Z)
• Graph-Native Cognitive Memory for AI Agents: Formal Belief Revision Semantics for Versioned Memory Architectures [0.6091702876917279]
  Kumiho is a graph-native cognitive memory architecture grounded in formal belief revision semantics.<n>The architecture implements a dual-store model (Redis working memory, Neo4j long-term graph) with hybrid fulltext and vector retrieval.
  arXiv  Detail & Related papers  (2026-03-18T00:59:49Z)
• MemPO: Self-Memory Policy Optimization for Long-Horizon Agents [52.00646524941419]
  Existing methods typically introduce the external memory module and look up the relevant information from the stored memory.<n>We propose the self-memory policy optimization algorithm (MemPO), which enables the agent to autonomously summarize and manage their memory.<n>MemPO achieves absolute F1 score gains of 25.98% over the base model and 7.1% over the previous SOTA baseline, while reducing token usage by 67.58% and 73.12%.
  arXiv  Detail & Related papers  (2026-02-28T14:43:02Z)
• The Algorithmic Self-Portrait: Deconstructing Memory in ChatGPT [17.579565226391146]
  We analyze 2,050 memory entries from 80 real-world ChatGPT users.<n>A striking 96% of memories in our dataset are created unilaterally by the conversational system.<n>A significant majority of memories (84%) are directly grounded in user context.
  arXiv  Detail & Related papers  (2026-02-01T21:39:36Z)
• HiNS: Hierarchical Negative Sampling for More Comprehensive Memory Retrieval Embedding Model [19.485780251890322]
  Existing training data construction overlooks a critical limitation: the hierarchical difficulty of negative samples.<n>We propose a principled data construction framework HiNS that explicitly models negative sample difficulty tiers and incorporates empirically grounded negative ratios.
  arXiv  Detail & Related papers  (2026-01-21T10:39:48Z)
• Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution [52.76038908826961]
  We propose $textbfReMe$ ($textitRemember Me, Refine Me$) to bridge the gap between static storage and dynamic reasoning.<n>ReMe innovates across the memory lifecycle via three mechanisms: $textitmulti-faceted distillation$, which extracts fine-grained experiences.<n>Experiments on BFCL-V3 and AppWorld demonstrate that ReMe establishes a new state-of-the-art in agent memory system.
  arXiv  Detail & Related papers  (2025-12-11T14:40:01Z)
• CIMemories: A Compositional Benchmark for Contextual Integrity of Persistent Memory in LLMs [62.116710797795314]
  Large Language Models (LLMs) increasingly use persistent memory from past interactions to enhance personalization and task performance.<n>We present CIMemories, a benchmark for evaluating whether LLMs appropriately control information flow from memory based on task context.
  arXiv  Detail & Related papers  (2025-11-18T21:51:23Z)
• Early Detection and Reduction of Memorisation for Domain Adaptation and Instruction Tuning [4.145513103869504]
  We fine-tune Pythia, Llama3, and Mistral models spanning 1.4B-70B parameters on common evaluation datasets.<n>We find that memorisation increases dramatically in the first few epochs, often significantly before either validation perplexity or evaluation performance is optimised.<n>We introduce an n-gram-aware loss regulariser and show that it reduces memorisation across all model families tested by up to 40%.
  arXiv  Detail & Related papers  (2025-10-13T13:12:46Z)
• Erasing Without Remembering: Implicit Knowledge Forgetting in Large Language Models [81.62767292169225]
  We investigate knowledge forgetting in large language models with a focus on its generalisation.<n>We propose PerMU, a novel probability perturbation-based unlearning paradigm.<n>Experiments are conducted on a diverse range of datasets, including TOFU, Harry Potter, ZsRE, WMDP, and MUSE.
  arXiv  Detail & Related papers  (2025-02-27T11:03:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.