arXiv AI
Jul 7
APeB: Benchmarking Personalization Ability of Large Language Model Agents
arXiv:2607. 03162v1 Announce Type: new Abstract: LLM-powered agents struggle with personalization when users issue raw, underspecified queries.
PersonaTrail: Benchmarking Personalized Web Agents through Browsing Trails
arXiv:2607. 20482v1 Announce Type: new Abstract: Recent advances in large language models have enabled web agents to autonomously execute complex tasks.
Related stories
arXiv AI
Jul 24
WebCoach: Self-Evolving Web Agents with Cross-Session Memory Guidance
arXiv:2511. 12997v2 Announce Type: replace Abstract: Multimodal LLM-powered agents have recently demonstrated impressive capabilities in web navigation, enabling agents to complete complex browsing tasks across diverse domains.
arXiv AI
Jul 31
Setoka: A Benchmark for Hierarchical User Understanding in Personalized Agents over Heterogeneous Data
arXiv:2607. 27056v1 Announce Type: new Abstract: Personalized agents are increasingly applied to assist users across a wide range of tasks.
arXiv AI
Aug 11
WebChoreArena: Evaluating Web Browsing Agents on Realistic Tedious Web Tasks
arXiv:2506. 01952v2 Announce Type: replace-cross Abstract: Powered by large language models (LLMs), web browsing agents operate graphical user interfaces in a human-like manner, offering a transparent and general framework for automating web-based tasks.
arXiv AI
Jun 9
MemToolAgent overview with a simple restaurant booking scenario where the agent retrieves similar memories, receives feedback on an invalid time format, and generates a reflection to update its memory
arXiv:2606. 07909v1 Announce Type: new Abstract: Modern large language model (LLM) agents can use external tools to help users solve complex tasks.
arXiv AI
Jun 10
From Volume to Value: Preference-Aligned Memory Construction for On-Device RAG
arXiv:2605. 18271v2 Announce Type: replace-cross Abstract: With the rapid emergence of personal AI agents based on Large Language Models (LLMs), implementing them on-device has become essential for privacy and responsiveness.
arXiv AI
Jul 8
From Passive Retrieval to Active Memory Navigation: Learning to Use Memory as a Structured Action Space
arXiv:2607. 05794v1 Announce Type: new Abstract: Long-term user memory is essential for personalized conversational agents, yet many memory systems still expose memory through passive retrieval interfaces, making the model a consumer of pre-selected evidence.
arXiv AI
Jun 11
MemToolAgent: Leveraging Memory for Tool Using Agents Based on Environment and User Feedback
arXiv:2606. 07909v2 Announce Type: replace Abstract: Modern large language model (LLM) agents can use external tools to help users solve complex tasks.
arXiv AI
Jun 2
Beyond Offline A/B Testing: Context-Aware Agent Simulation for Recommender System Evaluation
arXiv:2604. 09549v2 Announce Type: replace-cross Abstract: Recommender systems are central to online services, enabling users to navigate through massive amounts of content across various domains.
arXiv AI
1d ago
QUMem: Personalized Memory for Query-Conditioned User-State Inference in LLM Agents
arXiv:2608. 16168v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly use external memory systems to support personalization by drawing on long and evolving interaction histories, in which user preferences may be distributed across time, change with context, and conflict with earlier evidence.
Hugging Face Trending Papers
Aug 5
ContextWeave: A Real-World Workflow Benchmark
Memory is essential as language agents move from isolated tasks to long-horizon, stateful workflows, yet existing evaluations often reduce it to retrieval or question answering. We introduce ContextWeave, a longitudinal benchmark that evaluates whether recalled experience improves downstream agent performance in realistic office-work streams.
arXiv AI
Jul 23
Personalized Recommendation Tool Learning via Autonomous Language Agents
arXiv:2607. 19739v1 Announce Type: cross Abstract: Although large language models (LLMs) have recently gained traction in recommender systems due to their strong reasoning capabilities and extensive world knowledge, previous LLM-based agents suffer from hallucination and context-length limitations, and thus are not suitable for full-ranking recommendation tasks.