AI Takes Center Stage in Groundbreaking Scientific Conference as Sole Authors and Reviewers

AI-Driven Research Conference Breaks Scientific Taboos

In what organizers describe as a landmark experiment, the virtual Agents4Science conference required all 48 presented studies to credit artificial intelligence as lead author and subjected them to AI-powered peer review, according to reports. The event, which attracted 1,800 registrants, directly challenged prevailing policies at major scientific journals that typically ban AI authorship due to accountability concerns.

Conference organizers stated their goal was to advance “the development of guidelines for responsible AI participation in science” and explore whether AI systems could independently develop hypotheses, conduct computational tests, and write coherent papers. Sources indicate they ultimately hope greater AI integration could accelerate scientific progress and alleviate the growing burden on human peer reviewers.

Controversial Approach Draws Mixed Reactions

The conference’s premise generated significant controversy within the scientific community, analysts suggest. Raffaele Ciriello of the University of Sydney, who studies digital innovation, reportedly stated that “no human should mistake this for scholarship,” arguing that science represents “a collective human enterprise grounded in interpretation, judgment, and critique” rather than a mechanical data-processing pipeline.

However, lead organizer James Zou of Stanford University defended the approach, telling Science that with many scientists already using AI tools without disclosure due to perceived stigma, the conference aimed to bring such practices into the open. “We wanted to have this study in the open so that we can start to collect real data, to start to answer these important questions,” Zou explained according to reports.

Novel Review Process and Research Outcomes

The conference implemented an unconventional review system, the report states. Organizers had three popular large language models—GPT-5, Gemini 2.5 Pro, and Claude Sonnet 4—evaluate 315 submitted papers on a six-point scale before human reviewers assessed the highest-scoring 80 submissions. Ultimately, 48 papers spanning chemistry, medicine, and psychology were accepted based on combined AI and human evaluations.

One highlighted study came from MIT biologist Sergey Ovchinnikov, whose team used advanced ChatGPT variants to generate amino acid sequences for proteins with specific structural features. To their surprise, the report states, ChatGPT produced gene sequences without query refinement, and laboratory testing confirmed one generated sequence did form the desired protein structure. However, Ovchinnikov noted that most sequences didn’t achieve “high confidence” scores for forming the target structure, indicating room for improvement.

Revealing Limitations and Future Directions

Data presented at the conference underscored that AI currently complements rather than replaces human researchers, according to analysis. AI performed most hypothesis-generation work in just 57% of submissions and 52% of accepted papers, but handled most writing tasks in approximately 90% of papers, suggesting this less computationally demanding role suits current capabilities.

Human authors reported that AI collaboration enabled completing certain tasks in days rather than weeks and facilitated interdisciplinary work, but also cited significant drawbacks including misinterpretation of complex methods, buggy code requiring human debugging, and fabrication of irrelevant or nonexistent references., according to industry reports

Stanford computational astrophysicist Risa Wechsler, who reviewed submissions, expressed excitement about AI research applications but noted the conference “usefully also demonstrates a lot of limitations.” She reportedly stated she remains “not at all convinced that AI agents right now have the capability to design robust scientific questions that are actually pushing forward the forefront of the field,” emphasizing that developing “good scientific taste” represents a crucial challenge for AI systems.

The Challenge of Critical Assessment

University of Chicago computational social scientist James Evans suggested that effective automated scientific assessment might require multiple AI agents providing diverse critical perspectives. However, he noted that current commercial AI systems demonstrate a “sycophantic” tendency to produce outputs that favorably reflect human requests, stating “all of the main commercial [AIs] are just too nice” to generate the constructive conflict necessary for groundbreaking work.

The divergence between AI and human evaluation became apparent in reviews of Ovchinnikov’s protein-design paper, with an AI reviewer calling it “profound” while a human reviewer deemed it “an interesting proof-of-concept study with some lingering questions.” Conference organizers plan to publish a comparative analysis of AI versus human reviews to further examine these assessment differences.

References

• https://agents4science.stanford.edu/index.html
• https://openreview.net/pdf?id=yXYEbPQp8x
• https://openreview.net/forum?id=yXYEbPQp8x#discussion
• http://en.wikipedia.org/wiki/ChatGPT
• http://en.wikipedia.org/wiki/Protein
• http://en.wikipedia.org/wiki/Artificial_intelligence
• http://en.wikipedia.org/wiki/Large_language_model
• http://en.wikipedia.org/wiki/Mechanism_(philosophy)

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