arXiv:2607. 29320v1 Announce Type: new Abstract: Graphical user interface (GUI) agents based on large language models are increasingly deployed across mobile, web, and desktop environments.
arXiv:2607. 04425v2 Announce Type: replace-cross Abstract: Recent advances in multimodal foundation models and agent systems have driven GUI agents from single-platform task execution toward cross-platform interaction.
arXiv:2607. 04425v1 Announce Type: cross Abstract: Recent advances in multimodal foundation models and agent systems have driven GUI agents from single-platform task execution toward cross-platform interaction.
arXiv:2607. 24280v1 Announce Type: new Abstract: Agentic search enables large language models to solve knowledge-intensive tasks by interleaving multi-step reasoning with retrieval, yet optimizing this with outcome-based reinforcement learning (RL) provides only sparse supervision.
arXiv:2606. 29705v1 Announce Type: new Abstract: Data, as the fundamental substrate of modern intelligence, has greatly driven the development of current foundation models.
arXiv:2607. 10891v1 Announce Type: new Abstract: Large language models (LLMs) are rapidly shifting toward agents that solve tasks through diverse interfaces, including web and graphical user interfaces (GUIs).
Agentic language models must learn when to call tools, when to consume tool responses, and when to answer directly. This makes multi-teacher on-policy distillation a natural training strategy: one teacher can specialize in tool calls, another in direct responses, and the student can learn from both on its own generated distribution.
arXiv:2606. 30044v1 Announce Type: new Abstract: A key step toward artificial general intelligence is to train models that can perform multiple tasks.
arXiv:2607. 07050v1 Announce Type: cross Abstract: Agentic language models must learn when to call tools, when to consume tool responses, and when to answer directly.
arXiv:2608. 09555v1 Announce Type: new Abstract: External natural-language skills provide large language model (LLM) agents with reusable and editable guidance for solving complex tasks.
arXiv:2608. 05587v1 Announce Type: new Abstract: Autonomous mobile GUI agents require accurate action reflection for reliable long-horizon execution.
arXiv:2607. 07050v2 Announce Type: replace-cross Abstract: Agentic language models must learn when to call tools, when to consume tool responses, and when to answer directly.
arXiv:2607. 17558v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) offers a promising approach for training large language models without relying on a separate teacher model.