The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search

AI is increasingly playing a pivotal role in transforming how scientific discoveries are made. We introduce The AI Scientist-v2, an end-to-end agentic system capable of producing the first entirely AIgenerated peer-review-accepted workshop paper. This system iteratively formulates scientific hypotheses, designs and executes experiments, analyzes and visualizes data, and autonomously authors scientific manuscripts. Compared to its predecessor (v1, Lu et al., 2024), The AI Scientist-v2 eliminates the reliance on human-authored code templates, generalizes effectively across diverse machine learning domains, and leverages a novel progressive agentic tree-search methodology managed by a dedicated experiment manager agent. Additionally, we enhance the AI reviewer component by integrating a Vision-Language Model (VLM) feedback loop for iterative refinement of content and aesthetics of the figures. We evaluated The AI Scientist-v2 by submitting three fully autonomous manuscripts to a peer-reviewed ICLR workshop. Notably, one manuscript achieved high enough scores to exceed the average human acceptance threshold, marking the first instance of a fully AI-generated paper successfully navigating a peer review. This accomplishment highlights the growing capability of AI in conducting all aspects of scientific research. We anticipate that further advancements in autonomous scientific discovery technologies will profoundly impact human knowledge generation, enabling unprecedented scalability in research productivity and significantly accelerating scientific breakthroughs, greatly benefiting society at large. We have open-sourced the code at https://github.com/SakanaAI/AI-Scientist-v2 to foster the future development of this transformative technology. We also discuss the role of AI in science, including AI safety.

Introduction. Automated scientific discovery empowered by artificial intelligence (AI) has garnered considerable attention in recent years (Cornelio et al., 2023; Gil et al., 2014; King et al., 2009; Kitano, 2021; Wang et al., 2023; Xu et al., 2021). The development of end-to-end frameworks capable of autonomously formulating hypotheses, performing experiments, analyzing results, and authoring manuscripts could fundamentally transform the scientific process. A notable recent advance in this direction is The AI Scientist-v1 (Lu et al., 2024), which demonstrated the feasibility of a fully automated scientific workflow and downstream manuscript production. However, significant limitations constrained its broad applicability and autonomy. Specifically, it relied heavily on human-authored code templates requiring manual effort to create a new template for each new topic area. Furthermore, its linear and shallow experimentation approach prevented deeper exploration of scientific hypotheses.

In this paper, we introduce The AI Scientist-v2, a substantially improved successor that directly addresses these limitations. Our contributions are threefold. First, we eliminate the dependency on human-provided code templates, significantly increasing the system’s autonomy and ability to be deployed out of the box across multiple machine learning domains. Second, we introduce an experiment manager agent coupled with a novel agentic tree-search algorithm, enabling deeper and more systematic exploration of complex hypotheses. Third, we enhance the reviewing and refinement stages by integrating a Vision-Language Model (VLM)-based feedback mechanism, improving the quality, clarity, and alignment of generated figures, captions, and text interpretation. To rigorously evaluate the capabilities and limitations of fully autonomous manuscript generation, we conducted a controlled experiment: three manuscripts entirely generated by The AI Scientist-v2 were submitted to a peer-reviewed workshop at ICLR. Remarkably, one manuscript achieved an average reviewer score of 6.33 (placing it roughly in the top 45% of submissions) and would have been accepted after meta-review were it human-generated, thus becoming the first fully AI-generated manuscript to successfully pass a peer-review process.

The accepted paper investigates whether incorporating an explicit compositional regularization term into neural network training can improve compositional generalization. Specifically, it penalizes large deviations between embeddings of successive time steps in sequence models, hypothesizing that this encourages compositionality. The approach is evaluated using synthetic arithmetic expression datasets, but it is found that compositional regularization does not yield significant improvements and occasionally harms performance. The workshop reviewers appreciated the paper for clearly identifying the challenges of effective compositional regularization and reporting on negative results. However, they collectively highlighted shortcomings, including insufficient justification and intuitive explanations for why the chosen regularization method would enhance compositionality. Our personal assessment (detailed further in §4) highlights several additional potential improvements in method description (e.g., making clear exactly which component of the network is being regularized), potential dataset overlap issues, and inaccuracies in figure captions. Overall, reviewers viewed the paper as an interesting and technically sound workshop contribution that needs further development and broader experimentation to reach conference-level rigor.

This report provides an in-depth outline of the developed methodological advances, analysis of the workshop-submitted papers, and a discussion on the ethical and safety considerations of systems like The AI Scientist-v2. Our overall contributions are as follows:

Related work. Recent advancements have substantially expanded the field of automated scientific discovery, particularly through approaches leveraging artificial intelligence (AI). Early end-to-end approaches, exemplified by The AI Scientist-v1 (Lu et al., 2024), introduced fully automated frameworks, such as AI-Researcher (Data Intelligence Lab, 2025), capable of autonomously navigating the entire research pipeline. Subsequent works, however, often incorporate varying degrees of human oversight, as demonstrated by Intology (Intology AI, 2025) and Carl (AutoScience AI, 2025). Other systems narrow the scope; for example, CycleResearcher (Weng et al., 2025) focuses specifically on the path from idea generation to manuscript drafting, explicitly excluding experimental execution. Alternative approaches include protocol designs for experiments in self-driving laboratories that do not rely on large language models (LLMs) or use them in complementary roles (Shi et al., 2025). Several concurrent works explore similar territories, including Agent Laboratory (Schmidgall et al., 2025) and agentRxiv (Schmidgall and Moor, 2025), highlighting the rapid development in this area.

LLM-based scientific idea generation has been explicitly investigated in recent studies. Notably, Si et al. (2025) examined the capabilities of LLMs to generate human-level scientific ideas, finding through human evaluations that LLM-generated ideas were typically more novel but often less feasible than those proposed by human experts. GraphEval (Feng et al., 2025) offers graph-based methods for evaluating research ideas, further highlighting the current limitations of LLMs in accurate idea assessment.

Several benchmarks have been established to systematically evaluate AI performance in scientific tasks. MLEBench (Chan et al., 2025) and Aide (Jiang et al., 2025) provide structured environments to assess model capabilities on tasks representative of research engineering workloads. The METR Research Engineer benchmark (Wijk et al., 2024), for instance, demonstrates AI superiority in executing short-duration tasks (sub-2-hour tasks). Comprehensive reviews, such as the one by Eger et al. (2025), document the role and effectiveness of LLMs in scientific workflows. Coding-specific benchmarks such as SciCode (Tian et al., 2024), curated explicitly by domain scientists, address problems across physics, chemistry, and biology, encompassing structured sub-problems to rigorously evaluate research-related programming skills. Similarly, BixBench focuses on computational biology, providing comprehensive evaluations of LLM-based agents (Mitchener et al., 2025). Additionally, independent evaluations specifically target AI scientist frameworks, like the evaluation of The AI Scientist-v1 by Beel et al.

Method. 3. The AI Scientist-v2 We now describe the major innovations introduced in The AI Scientist-v2 relative to The AI Scientist-v1 (Lu et al., 2024). The most significant improvement is the move towards greater autonomy and generalization, starting a more general idea generation phase (§3.1) and eliminating the reliance on fixed, human-authored template code for experimentation. This process begins with generalized idea generation, producing an initial concept, which then feeds into the experimentation phase (§3.2). To manage this, we introduce two critical features in the experimentation phase: coarse-grained experiment management and agentic tree search-based exploration. Additionally, we integrate Vision Language Models (VLMs) into the experimental and review phases (§3.4). Finally, we streamline the manuscript writing phase by replacing the incremental, Aider-based (Gauthier, 2024) iterative writing approach of The AI Scientist-v1 with a simpler, single-pass generation followed by a separate reflection stage powered by reasoning models such as o1 (OpenAI, 2024). We include a full list of sampling hyperparameters and models used in Appendix A and the prompts used for The AI Scientist-v2 in Appendix B.

3.1. More General Idea Generation A key conceptual shift in The AI Scientist-v2 is the approach to research idea generation. Unlike the predecessor system, which primarily focused on proposing incremental modifications or extensions based on an existing codebase, The AI Scientist-v2 adopts a process that begins at a higher level of abstraction. The system is prompted to engage in more open-ended thinking about potential research directions, hypotheses, and experimental designs, akin to formulating a research abstract or grant proposal before committing to a specific implementation.

This approach encourages the exploration of potentially more novel or foundational ideas, rather than being constrained by the structure and topics of pre-existing code. It aligns more closely with how researchers often develop broader research visions, starting with abstract concepts and assessing novelty and feasibility before diving into specific implementations. Crucially, this generalized idea generation phase integrates literature review tools, such as Semantic Scholar, in the loop. The system can query the literature database during the idea formulation process to assess the novelty of a proposed concept and identify relevant prior work. This allows for more informed decisions about pursuing a particular research avenue, ensuring ideas are grounded in the existing scientific landscape from the outset, rather than relying solely on post-hoc checks.

3.2. Removing Template Dependency Following the improved idea generation phase, The AI Scientist-v2 proceeds with experimentation. Beyond the code-conditioned idea generation, The AI Scientist-v1 also depended on the predefined template code as a starting baseline implementation. The LLM-driven code changes were then limited to sequential code adaptations. We now outline our strategy for eliminating this limitation, thus improving the system’s flexibility and autonomy.

3.2.1. Experiment Progress Manager Real-world scientific experimentation typically proceeds through distinct stages, from initial feasibility assessments to detailed ablation analyses. To emulate this structured approach, we introduce an The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search experiment progress manager agent that coordinates four clearly defined stages of scientific experimentation:

Stage 1 Preliminary Investigation: Establishing initial feasibility and correctness through a minimal working prototype based on the generated research idea. Stage 2 Hyperparameter Tuning: Refining the initial implementation by optimizing critical hyperparameters (e.g., learning rate, epochs) to create a robust experimental baseline. Stage 3 Research Agenda Execution: Systematically implementing the core research agenda based on the tuned baseline.

Discussion. Observations and Insights. Our internal inspection of the generated experiments and code revealed several noteworthy limitations. First, The AI Scientist-v2 occasionally introduced inaccuracies in citations, similar to the well-known “hallucination” issue encountered in large language models. Second, while the system successfully executed standard experimental pipelines, it sometimes lacked the detailed methodological rigor and in-depth analysis typically required for acceptance at leading main conferences. However, such limitations did not prevent acceptance at the workshop level.

Transparency and Ethical Considerations. We believe it is crucial for the scientific community to engage openly and transparently with AI-generated research, subjecting it to the same rigorous peer-review processes applied to human-authored work. However, responsible oversight is essential. In conducting this evaluation, we obtained IRB approval from the University of British Columbia (H24-02652). We ensured full transparency and coordination with ICLR leadership and the workshop organizers. Before the review process, reviewers were explicitly informed that some submissions could be AI-generated and offered the option to opt out. Following acceptance, we withdrew the AI-generated manuscript prior to publication, which is consistent with our commitment to avoid prematurely inserting purely AI-generated works into the official scientific record without broader community discussion. We emphasize that the community has not yet reached a consensus on integrating AI-generated research into formal scientific publications, making careful and transparent experimentation essential at this preliminary stage. Additionally, we believe that all AI-generated papers should be clearly labeled as such in any public arena, and in The AI Scientist-v1 and The AI Scientist-v2 always make sure to do so.

Conclusion. In this work, we introduced The AI Scientist-v2, a significantly improved automated scientific discovery system featuring enhanced autonomy and exploration capabilities. Compared to its predecessor, The AI Scientist-v1, our system removes reliance on human-crafted templates, incorporates a structured and exploratory agentic tree search methodology supervised by an experiment manager agent, and integrates Vision-Language Model (VLM) feedback loops for iterative refinement of visualizations and manuscript quality. We demonstrated that The AI Scientist-v2 is capable of autonomously generating manuscripts that successfully pass peer review at a workshop of a major machine learning conference.

This achievement, the first instance of a fully AI-generated paper navigating peer review, marks a notable milestone and shows promising early signs of progress, even considering the limitations discussed regarding workshop versus conference standards (§5). While significant challenges remain in consistently achieving top-tier quality and generating truly groundbreaking hypotheses, the capabilities demonstrated here suggest a clear trajectory. We believe that such advancements signal that next-generation AI Scientists will herald a new era in science. This is just the beginning; we expect AI capabilities to continue improving, potentially at an exponential rate. At some point in the future, AI will likely generate papers that match or exceed human quality, even at the highest levels of scientific publishing.

Limitations. & Ethical Considerations While The AI Scientist-v2 demonstrates significant progress by successfully generating a peer-reviewed workshop paper, it is important to contextualize this achievement clearly. First, the acceptance occurred at a workshop level rather than at the main conference track, and only one of the three AI-generated submissions was accepted. Workshop papers generally report preliminary results and exploratory work, and acceptance rates at workshops (typically 60-80%) are notably higher than at main conference tracks (20-30% for leading machine learning venues such as ICLR, ICML, and NeurIPS). Thus, the current version of The AI Scientist-v2 does not yet consistently reach the rigorous standard required for top-tier conference publications, nor does it even reach workshop-level consistently.

Moreover, despite the structured agentic tree search and enhanced autonomy introduced in The AI Scientist-v2, certain aspects of scientific inquiry—such as formulating genuinely novel, highimpact hypotheses, designing truly innovative experimental methodologies, or rigorously justifying design choices with deep domain expertise—remain challenging for purely automated systems.