arXiv:2606. 19690v1 Announce Type: new Abstract: From the past few years, web intelligent enhancement systems increasingly rely on heterogeneous and dynamic web data to deliver personalized, context-aware services.
arXiv:2508. 00429v5 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) have achieved remarkable success in graph-based learning by propagating information among neighbor nodes via predefined aggregation mechanisms.
arXiv:2607. 23678v1 Announce Type: new Abstract: Large language models (LLMs) enable autonomous agents for reasoning, planning, and tool use.
arXiv:2610.28010v1 Announce Type: new Abstract: The integration of Large Language Models and Reinforcement Learning offers a promising direction for complex sequential decision-making tasks, such as...
The paper examines when multi‑agent collaboration is beneficial versus single‑agent approaches. It finds that collaboration yields systematic advantages mainly in long‑horizon tasks with sparse dependencies, while single agents perform better in tightly coupled, sequential workflows. The authors introduce SAIGE, a lightweight multi‑agent mechanism that models collaboration as a dynamically evolving graph, and show that it balances context efficiency and task performance without always improving outcomes as more agents are added.
SkillGraph introduces a skill library framework that models reusable skills as nodes in a directed graph, with typed edges representing prerequisite, enhancement, and co-occurrence relationships. When presented with a new task, the system retrieves an ordered subgraph of relevant skills, guiding multi-step decision making. The graph is continuously refined through agent trajectories and reinforcement learning, enabling simultaneous improvement of the skill library and the agent policy, and achieving state‑of‑the‑art results on ALFWorld, WebShop, and several search‑augmented QA tasks.
arXiv:2609.39777v1 Announce Type: new Abstract: LLM-based multi-agent systems coordinate specialized reasoning through aggregation, interaction, and adaptive control, yet their potential for graph le...
arXiv:2607. 18368v1 Announce Type: new Abstract: Partially observable reinforcement learning requires deciding what to retain, retrieve, and forget over time.
The paper examines when multi‑agent collaboration is truly beneficial as large language models grow more capable. It finds that multi‑agent systems yield systematic advantages mainly for long‑horizon tasks with sparse dependencies, while single‑agent approaches excel in tightly coupled, sequential workflows. The authors introduce SAIGE, a lightweight, graph‑based collaboration framework that balances context efficiency and performance, demonstrating that adding more agents or deeper recursion does not always improve outcomes.
arXiv:2609.08599v1 Announce Type: new Abstract: Large Language Model (LLM) agents are evolving from single-session tools toward long-term personal assistants that must adapt to individual users acros...
Building capable visual web agents requires long-horizon reasoning, precise grounding, and robust interaction with dynamic real-world websites. Despite rapid progress, the strongest systems remain largely proprietary, while open agents still depend heavily on supervised post-training over large collections of curated web trajectories.
Large Language Model (LLM) agents are evolving from single-session tools toward long-term personal assistants that must adapt to individual users across tasks, contexts, and interactions. This shift m...
arXiv:2606. 02031v1 Announce Type: cross Abstract: Building capable visual web agents requires long-horizon reasoning, precise grounding, and robust interaction with dynamic real-world websites.