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Intellectual lineage

RISE is a new name for an old project. The threads that converge into it have been developing for decades; this page situates them.

e-Science and research infrastructure (2000s)

The first wave of computing for science as an infrastructure problem: grid computing, cyberinfrastructure reports, the rise of data-intensive science. The methodological commitments — durable artifacts, shared standards, machine-readable provenance — survive into RISE intact. What changed is the unit of automation: in e-Science it was the computation; in RISE it is the research act (a literature review, a paper draft, a review).

Open and reproducible science (2010s)

The replicability crisis in psychology, the credibility revolution in economics, pre-registration, open data and open code. This wave established the normative commitments that distinguish RISE from generic AI-for-science: a RISE artifact is judged not only on what it claims but on whether others can verify the claim. The catalog's outputs_reproducibility dimension is a direct descendant of this tradition.

Information Systems as a discipline

The IS field has long defended the sociotechnical unit of analysis — neither pure technology nor pure organization, but their mutual constitution 1. The discipline's design-science research tradition — building artifacts as scientific contributions, with explicit methodological standards for what that requires — is the most direct methodological parent of RISE. The current ISR special issue 2 and the Gopal et al. position piece 3 mark the IS discipline's explicit engagement with GenAI's implications for its own methods of inquiry.

AI4Science and the agentic turn

A separate genealogy runs through machine learning. From narrow predictive models (AlphaFold and its descendants) to tool-using agents (4) to autonomous "AI scientists" (SakanaAI's first release in 2024 was the inflection point), the field has accumulated infrastructure for goal-directed, tool-augmented LLM systems. The framework papers (5, 6) supply the canonical primitives — planning loops, tool use, memory streams — that nearly every RISE project in the catalog instantiates.

The catalog projects most squarely in this lineage: sakana-ai-scientist-v1, sakana-ai-scientist, agent-laboratory, robin.

Critique and friction (mid-2020s)

Alongside the optimistic build-out, a critical literature emerged. Concerns about hallucination (7, 8), reasoning faithfulness (9, 10), the debate over understanding (11), anthropomorphism (12), and the reception of AI in scholarship (13, 14) form a counter-current that RISE must engage rather than route around.

This critical literature is over-represented in the papers catalog by design: a knowledge base that included only the optimistic line would mislead.

Practitioner literature

A genre of practitioner essays has emerged in parallel to the academic literature — Cunningham on Claude Code for causal inference 15, Eberhardt on applied economics 16. These are tracked in the papers catalog with kind: misc because they are cite-worthy records of how working researchers actually use these tools, even when they would not appear in a traditional literature review.

What RISE adds

The name. The threads above have all been visible for a decade or more; what was missing was an explicit subfield identity that makes the agentic research pipeline its primary object of design and study, with sociotechnical accountability built into the methodological norms. The hope is that naming it makes the work easier to find, the comparisons easier to draw, and the standards easier to enforce.

  1. Sarker, S. et al. (2019). The sociotechnical axis of cohesion for the IS discipline: Its historical legacy and its continued relevance. MIS Quarterly, 43(3), 695–719. https://doi.org/10.25300/misq/2019/13747 ↩

  2. Abbasi, A. et al. (2026). ISR special issue: Generative AI and new methods of inquiry in information systems research. INFORMS Information Systems Research, Call for Papers. https://pubsonline.informs.org/page/isre/calls-for-papers ↩

  3. Gopal, R. D. et al. (2025). Inventing with machines: Generative AI and the evolving landscape of IS research. Information Systems Research, 36(4), 1949–1967. https://doi.org/10.1287/isre.2025.editorial.v36.n4 ↩

  4. Schick, T. et al. (2023). Toolformer: Language models can teach themselves to use tools. Advances in Neural Information Processing Systems, 36, 68539–68551. https://arxiv.org/abs/2302.04761 ↩

  5. Wu, J. et al. (2025). Agentic reasoning: A streamlined framework for enhancing LLM reasoning with agentic tools. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/2025.acl-long.1383 ↩

  6. Park, J. S. et al. (2023). Generative agents: Interactive simulacra of human behavior. Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology. https://doi.org/10.1145/3586183.3606763 ↩

  7. Ji, Z. et al. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), 1–38. https://doi.org/10.1145/3571730 ↩

  8. Maynez, J. et al. (2020). On faithfulness and factuality in abstractive summarization. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.173 ↩

  9. Chen, Y. et al. (2025). Reasoning models don’t always say what they think. https://arxiv.org/abs/2505.05410 ↩

  10. Matton, K. et al. (2025). Walk the talk? Measuring the faithfulness of large language model explanations. International Conference on Learning Representations (ICLR). https://arxiv.org/abs/2504.14150 ↩

  11. Mitchell, M., & Krakauer, D. C. (2023). The debate over understanding in AI’s large language models. Proceedings of the National Academy of Sciences, 120(13), e2215907120. https://doi.org/10.1073/pnas.2215907120 ↩

  12. Peter, S., Riemer, K., & West, J. D. (2025). The benefits and dangers of anthropomorphic conversational agents. Proceedings of the National Academy of Sciences, 122(22), e2415898122. https://doi.org/10.1073/pnas.2415898122 ↩

  13. Naddaf, M. (2025). AI is transforming peer review — and many scientists are worried. Nature, 639(8056), 852–854. https://doi.org/10.1038/d41586-025-00894-7 ↩

  14. Gartenberg, C., Murray, F., Hasan, S., & Pierce, L. (2026). More versus better: Artificial intelligence, incentives, and the emerging crisis in peer review. Organization Science, 37(3). https://doi.org/10.1287/orsc.2026.ed.v37.n3 ↩

  15. Cunningham, S. (2025). Claude code for causal inference. Causal Inference Substack. https://causalinf.substack.com/s/claude-code ↩

  16. Eberhardt, M. (2025). Claude code for applied economists. Markus Academy Substack. https://markusacademy.substack.com/p/claude-code-for-applied-economists ↩