The newly-formed Autoscience Institute has unveiled ‘Carl,’ the first AI system crafting academic research papers to pass a rigorous double-blind peer-review process.
Carl’s research papers were accepted in the Tiny Papers track at the International Conference on Learning Representations (ICLR). Critically, these submissions were generated with minimal human involvement, heralding a new era for AI-driven scientific discovery.
Meet Carl: The ‘automated research scientist’
Carl represents a leap forward in the role of AI as not just a tool, but an active participant in academic research. Described as “an automated research scientist,” Carl applies natural language models to ideate, hypothesise, and cite academic work accurately.
Crucially, Carl can read and comprehend published papers in mere seconds. Unlike human researchers, it works continuously, thus accelerating research cycles and reducing experimental costs.
According to Autoscience, Carl successfully “ideated novel scientific hypotheses, designed and performed experiments, and wrote multiple academic papers that passed peer review at workshops.”
This underlines the potential of AI to not only complement human research but, in many ways, surpass it in speed and efficiency.
Carl is a meticulous worker, but human involvement is still vital
Carl’s ability to generate high-quality academic work is built on a three-step process:
- Ideation and hypothesis formation: Leveraging existing research, Carl identifies potential research directions and generates hypotheses. Its deep understanding of related literature allows it to formulate novel ideas in the field of AI.
- Experimentation: Carl writes code, tests hypotheses, and visualises the resulting data through detailed figures. Its tireless operation shortens iteration times and reduces redundant tasks.
- Presentation: Finally, Carl compiles its findings into polished academic papers—complete with data visualisations and clearly articulated conclusions.
Although Carl’s capabilities make it largely independent, there are points in its workflow where human involvement is still required to adhere to computational, formatting, and ethical standards:
- Greenlighting research steps: To avoid wasting computational resources, human reviewers provide “continue” or “stop” signals during specific stages of Carl’s process. This guidance steers Carl through projects more efficiently but does not influence the specifics of the research itself.
- Citations and formatting: The Autoscience team ensures all references are correctly cited and formatted to meet academic standards. This is currently a manual step but ensures the research aligns with the expectations of its publication venue.
- Assistance with pre-API models: Carl occasionally relies on newer OpenAI and Deep Research models that lack auto-accessible APIs. In such cases, manual interventions – such as copy-pasting outputs – bridge these gaps. Autoscience expects these tasks to be entirely automated in the future when APIs become available.
For Carl’s debut paper, the human team also helped craft the “related works” section and refine the language. These tasks, however, were unnecessary following updates applied before subsequent submissions.
Stringent verification process for academic integrity
Before submitting any research, the Autoscience team undertook a rigorous verification process to ensure Carl’s work met the highest standards of academic integrity:
- Reproducibility: Every line of Carl’s code was reviewed and experiments were rerun to confirm reproducibility. This ensured the findings were scientifically valid and not coincidental anomalies.
- Originality checks: Autoscience conducted extensive novelty evaluations to ensure that Carl’s ideas were new contributions to the field and not rehashed versions of existing publications.
- External validation: A hackathon involving researchers from prominent academic institutions – such as MIT, Stanford University, and U.C. Berkeley – independently verified Carl’s research. Further plagiarism and citation checks were performed to ensure compliance with academic norms.
Undeniable potential, but raises larger questions
Achieving acceptance at a workshop as respected as the ICLR is a significant milestone, but Autoscience recognises the greater conversation this milestone may spark. Carl’s success raises larger philosophical and logistical questions about the role of AI in academic settings.
“We believe that legitimate results should be added to the public knowledge base, regardless of where they originated,” explained Autoscience. “If research meets the scientific standards set by the academic community, then who – or what – created it should not lead to automatic disqualification.”
“We also believe, however, that proper attribution is necessary for transparent science, and work purely generated by AI systems should be discernable from that produced by humans.”
Given the novelty of autonomous AI researchers like Carl, conference organisers may need time to establish new guidelines that account for this emerging paradigm, especially to ensure fair evaluation and intellectual attribution standards. To prevent unnecessary controversy at present, Autoscience has withdrawn Carl’s papers from ICLR workshops while these frameworks are being devised.
Moving forward, Autoscience aims to contribute to shaping these evolving standards. The company intends to propose a dedicated workshop at NeurIPS 2025 to formally accommodate research submissions from autonomous research systems.
As the narrative surrounding AI-generated research unfolds, it’s clear that systems like Carl are not merely tools but collaborators in the pursuit of knowledge. But as these systems transcend typical boundaries, the academic community must adapt to fully embrace this new paradigm while safeguarding integrity, transparency, and proper attribution.
(Photo by Rohit Tandon)
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