arXiv:2605.30824v1 Announce Type: new Abstract: Deep research tasks require LLMs to plan what to investigate, retrieve evidence, and synthesize long-form answers across multiple branches of inquiry. Existing training paradigms either rely on short-form verifiable QA as a proxy or optimize monolithic long trajectories, which makes planning and execution difficult to disentangle and yields weak credit assignment for the planning process. We propose DecomposeR, a planner-centric deep research framework that represents research plans as typed directed acyclic graphs (DAGs), allowing planning to be
The increasing complexity of AI tasks demands more sophisticated planning and research capabilities from LLMs, moving beyond simple QA.
This development enhances the autonomy and analytical depth of AI, potentially accelerating research and development across numerous fields.
AI systems can now engage in more structured and multi-branch inquiry, improving their ability to conduct deep research and synthesize long-form answers.
- · AI developers
- · Research institutions
- · Knowledge-intensive industries
- · Monolithic LLM approaches
- · Basic 'prompt-and-response' paradigms
Improved AI agents capable of complex information synthesis and problem-solving.
Accelerated scientific discovery and intellectual property generation due to more effective AI research assistants.
Reconfiguration of white-collar workflows, with AI agents handling tasks previously requiring significant human planning and research oversight.
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Read at arXiv cs.AI