arXiv AI
Aug 11
Mitigating Over-Personalization in LLMs via Structured Memory
arXiv:2608. 08300v1 Announce Type: new Abstract: Conversational assistants increasingly rely on persistent long-term memory to personalize responses across sessions.
PersistBench: When Should Long-Term Memories Be Forgotten by LLMs?
arXiv:2602. 01146v2 Announce Type: replace Abstract: Conversational assistants are increasingly integrating long-term memory with large language models (LLMs).
Related stories
arXiv AI
Jun 6
When Should Memory Stay Silent: Measuring Memory-Use Boundaries in Memory-Augmented Conversational Agents
arXiv:2606. 06055v1 Announce Type: new Abstract: Long-term memory enables language model agents to support personalized interactions, but it remains unclear when available memories warrant integration into responses.
arXiv AI
Jun 10
Recalling Too Well: Sycophancy Evaluation and Mitigation in Memory-Augmented Models
arXiv:2606. 10949v1 Announce Type: new Abstract: Persistent memory systems promise to make LLMs more helpful by storing user beliefs over time.
arXiv AI
2d ago
MemGuard: Preventing Memory Contamination in Long-Term Memory-Augmented Large Language Models
arXiv:2605.28009v2 Announce Type: replace-cross Abstract: Memory-augmented large language models extend reasoning beyond a fixed context window by maintaining long-term memory across interactions. Ho...
arXiv AI
Jul 15
MemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon Conversations
arXiv:2607. 12893v1 Announce Type: new Abstract: Long-term memory has become a foundational capability for LLM-based agents that accompany users across extended, multi-session interactions.
arXiv Computation and Language
4d ago
AMU:Admission and Memory Update for Personalized Conversations---Structured Memory with SLM Guided Control
arXiv:2609.36976v1 Announce Type: new Abstract: Large language models (LLMs) have become the foundation of personalized assistants, but maintaining persistent user memory across long-term interaction...
arXiv Computation and Language
Sep 1
UTILMEM: Benchmarking Evidence Utilization in Long-Term Conversational Memory
UTILMEM is a new diagnostic benchmark that tests how conversational agents use long‑term memory, focusing on reasoning over dense histories, spotting implicitly relevant memories, synthesizing distributed evidence, and resisting interference from similar distractors. It contains 1,717 instances across five domains and evaluates a range of retrieval‑based and memory‑augmented systems. The study shows that strong performance on traditional factual recall does not guarantee effective memory utilization, highlighting a gap between retrieving information and integrating it into coherent, task‑oriented outputs.
arXiv AI
Aug 21
MemTrapBench: Benchmarking Cognitive Traps in LLM Memory Use
arXiv:2608. 20202v1 Announce Type: new Abstract: Memory has become a key component of large language models, enabling them to retain information and learn from long-term interactions.
arXiv AI
Sep 25
PrivDrift: Auditing User-Secret Leakage Under Topic Drift in Active LLM Conversations
PrivDrift is a benchmark that tests whether user‑disclosed secrets can still be recovered by large language models after the conversation shifts to unrelated topics. It includes 1,000 controlled multi‑turn dialogues with seeded secrets, topic‑drift turns, and standardized extraction probes. Experiments on three LLMs with extended context windows show that dialogue‑level leakage remains substantial—between 38.7% and 54.6%—and is influenced by model, secret type, and persuasion intensity, while additional topic drift does not reliably reduce leakage.
arXiv Machine Learning
Jun 29
Continual Learning for Sequential Personalization of Small Language Models: A Stability Monitoring Analysis
arXiv:2606. 27634v1 Announce Type: new Abstract: Small Language Models (SLMs) are increasingly being considered for deployment on edge devices such as laptops, enabling private, low-latency, and locally personalized applications.
arXiv Machine Learning
Aug 4
LiveMem: Maintaining Memory State Continuity in Long-Running LLM Inference
arXiv:2608. 02515v1 Announce Type: cross Abstract: Long-running assistants and agents consume interaction streams that eventually outgrow the context.
arXiv AI
Aug 5
LeanMem: Simple and Efficient Long-Term Memory for LLM Agents
arXiv:2608. 03463v1 Announce Type: new Abstract: Long-term memory is essential for LLM-based agents to sustain interactions and reliably leverage distant history.