Evaluating Novelty in AI-Generated Research Plans Using Multi-Workflow LLM Pipelines

• URL: http://arxiv.org/abs/2601.09714v1
• Date: Wed, 24 Dec 2025 12:41:31 GMT
• Title: Evaluating Novelty in AI-Generated Research Plans Using Multi-Workflow LLM Pipelines
• Authors: Devesh Saraogi, Rohit Singhee, Dhruv Kumar,
• Abstract summary: This paper investigates whether agentic systems employing iterative reasoning, evolutionary search, and decomposition can generate more novel and feasible research plans.<n>We benchmark five reasoning architectures: Reflection-based iterative refinement, Sakana AI v2 evolutionary algorithms, Google Co-Scientist multi-agent framework, GPT Deep Research, and Gemini3 Pro multimodal long-context pipeline.<n>Results reveal varied performance across research domains, with high-performing maintaining feasibility without sacrificing creativity.
• Score: 1.3986052226424095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The integration of Large Language Models (LLMs) into the scientific ecosystem raises fundamental questions about the creativity and originality of AI-generated research. Recent work has identified ``smart plagiarism'' as a concern in single-step prompting approaches, where models reproduce existing ideas with terminological shifts. This paper investigates whether agentic workflows -- multi-step systems employing iterative reasoning, evolutionary search, and recursive decomposition -- can generate more novel and feasible research plans. We benchmark five reasoning architectures: Reflection-based iterative refinement, Sakana AI v2 evolutionary algorithms, Google Co-Scientist multi-agent framework, GPT Deep Research (GPT-5.1) recursive decomposition, and Gemini~3 Pro multimodal long-context pipeline. Using evaluations from thirty proposals each on novelty, feasibility, and impact, we find that decomposition-based and long-context workflows achieve mean novelty of 4.17/5, while reflection-based approaches score significantly lower (2.33/5). Results reveal varied performance across research domains, with high-performing workflows maintaining feasibility without sacrificing creativity. These findings support the view that carefully designed multi-stage agentic workflows can advance AI-assisted research ideation.

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