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.
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.
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arXiv AI
Jun 4
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).
arXiv AI
Jun 9
Evaluating Hallucinations in Domain-Adapted Large Language Models
arXiv:2606. 07521v1 Announce Type: cross Abstract: This study investigates the phenomenon of hallucinations in domain-adapted Large Language Models (LLMs), focusing on the fine-tuning of the Llama-2 model with the Lamini dataset.
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 AI
Jul 2
MemSyco-Bench: Benchmarking Sycophancy in Agent Memory
arXiv:2607. 01071v1 Announce Type: cross Abstract: Memory has emerged as a cornerstone of modern LLM-based agents, supporting their evolution from single-turn assistants to long-term collaborators.
Hugging Face Trending Papers
Jul 1
MemSyco-Bench: Benchmarking Sycophancy in Agent Memory
Memory has emerged as a cornerstone of modern LLM-based agents, supporting their evolution from single-turn assistants to long-term collaborators. However, memory is not always beneficial: retrieved memories often induce a critical issue of sycophancy, causing agents to over-align with the user at the cost of factual accuracy or objective reasoning.
arXiv AI
Jul 9
Structured Belief State and the First Precision-Aware Benchmark for LLM Memory Retrieval
arXiv:2605. 11325v3 Announce Type: replace-cross Abstract: Current LLM memory benchmarks evaluate answer quality rather than retrieval accuracy.
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
Jul 17
When Does Belief-Based Agent Memory Help? Reliability-Conditional Updating and Provenance-Capped Poisoning Defense
arXiv:2606. 22030v2 Announce Type: replace Abstract: We investigate when belief-based memory actually improves large language model (LLM) agents.
arXiv Machine Learning
Jul 13
Forget Narrowly, Retain Broadly: Unlearning as an Asymmetric Generalization Problem
arXiv:2607. 09236v1 Announce Type: new Abstract: Machine unlearning in LLMs is the targeted removal of specific knowledge while preserving all other capabilities, critical for privacy and safety.
arXiv AI
Jul 28
Reality Monitoring in Large Language Models: Self-Knowledge That Transforms with Conversation Memory
arXiv:2607. 23927v1 Announce Type: new Abstract: A conversational AI that cannot tell its own output from what a user said will treat its own mistakes as user-provided facts.
arXiv AI
Jun 24
Pigeonholing: Bad prompts hurt models to collapse and make mistakes
arXiv:2606. 24267v1 Announce Type: cross Abstract: While in-context learning is generally shown to be effective in Large Language Models (LLMs), bad contexts can cause performance degradation and mode collapse, a phenomenon we call "pigeonholing.