Dolphin: Moving Towards Closed-loop Auto-research through Thinking, Practice, and Feedback (2025.acl-long)
Copied to clipboard
Jiakang Yuan, Xiangchao Yan, Bo Zhang, Tao Chen, Botian Shi, Wanli Ouyang, Yu Qiao, Lei Bai, Bowen Zhou
| Challenge: | Recent studies show that AI-assisted research methods can improve research efficiency . a closed-loop framework is used to enhance the automation level of scientific research . |
| Approach: | They propose a closed-loop LLM-driven framework to enhance the automation level of scientific research. |
| Outcome: | The proposed framework improves the efficiency of scientific research by improving data analysis, accelerating computation, and fostering novel idea generation. |
Similar Papers
ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models (2025.naacl-long)
Copied to clipboard
| Challenge: | a new system that leverages the encyclopedic knowledge and linguistic reasoning capabilities of Large Language Models (LLMs) is proposed to enhance the productivity of researchers . a researcher's research idea generation process involves problem identification, method development, experiment design and iterative revision . |
| Approach: | They propose a system that leverages encyclopedic knowledge and linguistic reasoning capabilities of Large Language Models to assist researchers in their work. |
| Outcome: | The proposed system generates novel ideas based on human and model-based evaluations . it leverages encyclopedic knowledge and linguistic reasoning capabilities of Large Language Models based systems . |
All That Glitters is Not Novel: Plagiarism in AI Generated Research (2025.acl-long)
Copied to clipboard
| Challenge: | Recent studies claim autonomous research agents can generate novel research ideas. |
| Approach: | They ask experts to evaluate whether existing work is similar to new ones . they find 24% of the 50 evaluated documents to be either paraphrased or significantly borrowed . |
| Outcome: | The authors find that 24% of the 50 evaluated research documents are either paraphrased, or significantly borrowed from existing work. |
IRIS: Interactive Research Ideation System for Accelerating Scientific Discovery (2025.acl-demo)
Copied to clipboard
| Challenge: | Recent work on automated hypothesis generation focuses on multi-agent frameworks and extending test-time compute, but none incorporates human-in-the-loop (HITL) integration. |
| Approach: | They propose an open-source platform to enable researchers to leverage LLM-assisted scientific ideation. |
| Outcome: | The proposed system empowers researchers with greater control throughout ideation process. |
CodeScientist: End-to-End Semi-Automated Scientific Discovery with Code-based Experimentation (2025.findings-acl)
Copied to clipboard
Peter Jansen, Oyvind Tafjord, Marissa Radensky, Pao Siangliulue, Tom Hope, Bhavana Dalvi Mishra, Bodhisattwa Prasad Majumder, Daniel S Weld, Peter Clark
| Challenge: | Automated scientific discovery (ASD) systems are limited in their evaluation of software artifacts and large volumes of research artifs are typically evaluated using conference-style paper review with limited evaluation of code. |
| Approach: | They propose a novel ASD system that frames ideation and experiment construction as a form of genetic search jointly over combinations of research articles and codeblocks defining common actions in a domain. |
| Outcome: | The proposed system returns 19 discoveries on machine-generated ideas in the domain of agents and virtual environments. |
From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems (2025.findings-emnlp)
Copied to clipboard
Zekun Zhou, Xiaocheng Feng, Lei Huang, Xiachong Feng, Ziyun Song, Ruihan Chen, Liang Zhao, Weitao Ma, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin
| Challenge: | rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research. |
| Approach: | They organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication. |
| Outcome: | The authors summarize the current state of research in three main areas: hypothesis formulation, hypothesis validation, and manuscript publication. |
Preference Optimization for Review Question Generation Improves Writing Quality (2026.findings-acl)
Copied to clipboard
| Challenge: | Peer reviewers are overloaded and face tight deadlines, leading some to rely on large language models (LLMs) to draft questions and comments. |
| Approach: | They use open-review review datasets to train a human preference model based on human reviewer questions . human evaluations show IntelliAsk generates more grounded, substantive and effortful questions than strong baselines . |
| Outcome: | The proposed model predicts reviewer-question quality better than API-based SFT baselines and provides scalable evaluation. |
LLM-Based Web Data Collection for Research Dataset Creation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | researchers across many fields rely on web data to gain new insights and validate methods. |
| Approach: | They propose a human-in-the-loop framework that automates web-scale data collection end-to-end using large language models (LLMs) |
| Outcome: | The proposed framework outperforms existing methods in three different tasks and a user evaluation demonstrates its practical utility. |
Beyond Abstracts: A New Dataset, Prompt Design Strategy and Method for Biomedical Synthesis Generation (2024.acl-srw)
Copied to clipboard
| Challenge: | Existing methods to automate systematic reviews of papers are slow and incomplete . authors propose a new method to automating the systematic review process . |
| Approach: | They propose a method for automatic synthesis generation using a dataset and prompting-based method. |
| Outcome: | The proposed method improves the existing model and prompts the system to generate high-quality syntheses. |
TinyScientist: An Interactive, Extensible, and Controllable Framework for Building Research Agents (2025.emnlp-demos)
Copied to clipboard
Haofei Yu, Keyang Xuan, Fenghai Li, Kunlun Zhu, Zijie Lei, Jiaxun Zhang, Ziheng Qi, Kyle Richardson, Jiaxuan You
| Challenge: | Existing research systems often design and use agentic workflows to perform research tasks such as ideation, scientific coding, review writing, and tree-based search. |
| Approach: | They propose an open-source codebase, an interactive web demonstration, and a PyPI Python package to make state-of-the-art auto-research pipelines broadly accessible to every researcher and developer. |
| Outcome: | The proposed framework adapts easily to new tools and supports iterative growth. |
PaperRobot: Incremental Draft Generation of Scientific Ideas (P19-1)
Copied to clipboard
| Challenge: | a paper robot can read existing papers and create new nodes or links in the knowledge graphs. |
| Approach: | They propose to automate the creation of new ideas by predicting links from the background KGs. |
| Outcome: | The proposed paper automates three tasks: read existing papers, create new ideas, predict links . the paper generated abstracts, conclusion and future work sections, and new titles are chosen over human-written ones up to 30%, 24% and 12% of the time. |