MoFlow: Multi-Objective Agentic Workflow Generation

arXiv:2605.19330v1 Announce Type: new Abstract: LLM agents organize behavior through skills - structured natural-language specifications governing how an agent reasons, retrieves,

arXiv:2603.19639v1 Announce Type: new Abstract: Although agentic workflows have demonstrated strong potential for solving complex tasks, existing automated generation methods

arXiv:2605.15871v1 Announce Type: new Abstract: Toward recursive self-improvement, we investigate LLM agents autonomously designing foundation models beyond standard Transformers.

arXiv:2605.13848v1 Announce Type: new Abstract: Agentic LLM frameworks that rely on prompted orchestration, where the model itself determines workflow transitions, often suffer

arXiv:2604.22748v1 Announce Type: new Abstract: As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment

arXiv:2603.27195v1 Announce Type: new Abstract: Designing microstructures that satisfy coupled cross-physics objectives is a fundamental challenge in material science. This

arXiv:2605.01457v1 Announce Type: new Abstract: Generative models have emerged as a major paradigm for offline multi-agent reinforcement learning (MARL), but existing approaches

arXiv:2606.18191v1 Announce Type: new Abstract: Deep research (DR) systems are increasingly used for complex information-seeking tasks, but existing works mainly focus on