arXiv AI
Jul 24
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.
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.
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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.
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.
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.
Hugging Face Trending Papers
Jul 22
Personalized Recommendation Tool Learning via Autonomous Language Agents
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. To circumvent these limitations through architectural design rather than modifying the LLM itself, we propose an agent-based recommendation framework, memory-based $\textbf{P}$ersonalized $\textbf{R}$ecommendation $\textbf{T}$ool learning via autonomous language $\textbf{A}$gents (PRTA), in which an LLM acts as a central planner interacting with multiple recommendation models as tools.
arXiv AI
Aug 12
UserToolBench: A User-Profile-Hidden Benchmark for Personalized Decision Making in Tool-Use LLMs
arXiv:2608. 10042v1 Announce Type: cross Abstract: Tool-use LLMs are increasingly asked to act on users' behalf, but existing benchmarks usually focus on profile recall, style imitation, generic tool use, or response-level personalization.
arXiv AI
Aug 7
LUNAR: Benchmarking Personalized Large Language Models on UNiversal User BehAvioR Logs
arXiv:2608. 05246v1 Announce Type: new Abstract: Existing personalized LLM benchmarks primarily rely on textual personas or isolated behavioral signals, providing limited evaluation of cross-domain behavioral personalization, where responses must be grounded in heterogeneous daily-life activities.
arXiv Machine Learning
Jun 30
Synthetic Interaction Data for Scalable Personalization in Large Language Models
arXiv:2602. 12394v2 Announce Type: replace Abstract: Personalized prompting offers large opportunities for deploying large language models (LLMs) to diverse users, yet existing prompt optimization methods primarily focus on task-level optimization while largely overlooking user-specific preferences and latent constraints of individual users.
arXiv AI
Jun 17
EComAgentBench: Benchmarking Shopping Agents on Long-Horizon Tasks with Distributed Hidden Intent
arXiv:2606. 17698v1 Announce Type: new Abstract: As LLM-based shopping agents enter production, existing benchmarks fail to capture how a shopper's requirements arrive: stated implicitly in the query, recorded in a profile, or revealed only when the right question is asked.
arXiv AI
Jun 18
MemRerank: Preference Memory for Personalized Product Reranking
arXiv:2603. 29247v3 Announce Type: replace-cross Abstract: LLM-based shopping agents increasingly rely on long purchase histories and multi-turn interactions for personalization, yet naively appending raw history to prompts is often ineffective due to noise, length, and relevance mismatch.
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.
Hugging Face Trending Papers
Jul 29
Improving Item Discoverability in e-Commerce Search via Related Intent Generation
Traditional search systems are optimized to retrieve items that strictly match a query, often prioritizing precision over recall. In e-commerce marketplaces and particularly grocery, this paradigm is limiting, as user satisfaction and commercial outcomes depend heavily on the discoverability of substitute, complementary, and thematically related items.