arXiv:2607. 20482v1 Announce Type: new Abstract: Recent advances in large language models have enabled web agents to autonomously execute complex tasks.
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arXiv:2607. 03162v1 Announce Type: new Abstract: LLM-powered agents struggle with personalization when users issue raw, underspecified queries.
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.
MemoryArena is a new evaluation gym that benchmarks agent memory in interdependent multi‑session tasks. Unlike prior benchmarks that test memorization or single‑session action in isolation, MemoryArena requires agents to acquire memory while interacting with the environment and then use that memory to guide future decisions across a range of tasks such as web navigation, planning, information search, and formal reasoning. The benchmark reveals that agents excelling on existing long‑context memory tests perform poorly here, highlighting a gap in current memory evaluation methods.
The paper introduces the Multi-Session Personalized Tool Calling (MPT) benchmark, containing 4,695 instances across 459 multi‑session histories that test Preference Recall, Induction, and Transfer. It proposes PRefine, a test‑time memory method that refines a user’s latent preference via a generate‑verify‑refine loop. Experiments with five LLMs show that PRefine outperforms existing memory systems and even full‑history prompting on Preference Transfer, suggesting that personalized agents should encode behavior as preferences rather than merely storing past interactions.
arXiv:2609.36488v1 Announce Type: new Abstract: Large language model (LLM) agents have demonstrated strong performance on complex web navigation tasks, yet they remain brittle in real-world settings...
arXiv:2607. 27056v1 Announce Type: new Abstract: Personalized agents are increasingly applied to assist users across a wide range of tasks.