Several tools have recently been proposed for assisting researchers during various stages of the research life-cycle. However, these primarily concentrate on tasks such as retrieving and recommending relevant literature, reviewing and critiquing the draft, and writing of research manuscripts. Our investigation reveals a significant gap in availability of tools specifically designed to assist researchers during the challenging ideation phase of the research life-cycle. To aid with research ideation, we propose `Acceleron', a research accelerator for different phases of the research life cycle, and which is specially designed to aid the ideation process. Acceleron guides researchers through the formulation of a comprehensive research proposal, encompassing a novel research problem. The proposals motivation is validated for novelty by identifying gaps in the existing literature and suggesting a plausible list of techniques to solve the proposed problem. We leverage the reasoning and domain-specific skills of Large Language Models (LLMs) to create an agent-based architecture incorporating colleague and mentor personas for LLMs. The LLM agents emulate the ideation process undertaken by researchers, engaging researchers in an interactive fashion to aid in the development of the research proposal. Notably, our tool addresses challenges inherent in LLMs, such as hallucinations, implements a two-stage aspect-based retrieval to manage precision-recall trade-offs, and tackles issues of unanswerability. As evaluation, we illustrate the execution of our motivation validation and method synthesis workflows on proposals from the ML and NLP domain, given by 3 distinct researchers. Our observations and evaluations provided by the researchers illustrate the efficacy of the tool in terms of assisting researchers with appropriate inputs at distinct stages and thus leading to improved time efficiency.

翻译：近期，学界提出了多种辅助研究人员完成科研生命周期各阶段的工具。然而，这些工具主要集中于检索与推荐相关文献、审阅与评析草稿、撰写科研手稿等任务。我们的调研发现，在科研生命周期中具有挑战性的构思阶段，专门辅助研究人员的工具存在显著空缺。为助力研究构思，我们提出了"加速器"——一款覆盖科研生命周期不同阶段、特别针对构思过程设计的科研加速工具。该工具引导研究人员完成包含创新研究问题的综合性研究方案制定，通过识别现有文献中的研究空白来验证方案动机的新颖性，并提出可行的技术手段清单。我们借助大语言模型的推理能力与领域专属技能，构建了融合同事与导师角色代理的智能体架构。大语言模型代理模拟研究人员的构思过程，以交互方式引导研究人员完善研究方案。值得注意的是，本工具解决了大语言模型固有的幻觉问题，实现基于双阶段方面检索以平衡精度与召回率，并处理了不可回答性难题。通过三位不同研究人员提出的机器学习与自然语言处理领域方案，我们展示了动机验证与方法合成工作流的执行过程。研究人员的观察与评估表明，本工具能在不同阶段提供恰当输入，从而有效提升研究效率。