# Measuring AI R&D Automation

arXiv: https://arxiv.org/abs/2603.03992 · HTML: https://mlsanity.com/paper/2603.03992/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 72 references that are in the map.
- First posted on arXiv 4 Mar 2026.

## Builds on (most cited first, 30 of 30)

- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering](https://mlsanity.com/paper/2410.07095/) (2024)
- [Alignment faking in large language models](https://mlsanity.com/paper/2412.14093/) (2024)
- [PaperBench: Evaluating AI's Ability to Replicate AI Research](https://mlsanity.com/paper/2504.01848/) (2025)
- [Frontier Models are Capable of In-context Scheming](https://mlsanity.com/paper/2412.04984/) (2024)
- [RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts](https://mlsanity.com/paper/2411.15114/) (2024)
- [Compute Trends Across Three Eras of Machine Learning](https://mlsanity.com/paper/2202.05924/) (2022)
- [Natural Emergent Misalignment from Reward Hacking in Production RL](https://mlsanity.com/paper/2511.18397/) (2025)
- [ForecastBench: A Dynamic Benchmark of AI Forecasting Capabilities](https://mlsanity.com/paper/2409.19839/) (2024)
- [Establishing Best Practices for Building Rigorous Agentic Benchmarks](https://mlsanity.com/paper/2507.02825/) (2025)
- [Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity](https://mlsanity.com/paper/2507.09089/) (2025)
- [Stress Testing Deliberative Alignment for Anti-Scheming Training](https://mlsanity.com/paper/2509.15541/) (2025)
- [Auditing language models for hidden objectives](https://mlsanity.com/paper/2503.10965/) (2025)
- [Measuring the Algorithmic Efficiency of Neural Networks](https://mlsanity.com/paper/2005.04305/) (2020)
- [Sabotage Evaluations for Frontier Models](https://mlsanity.com/paper/2410.21514/) (2024)
- [A sketch of an AI control safety case](https://mlsanity.com/paper/2501.17315/) (2025)
- [A Rosetta Stone for AI Benchmarks](https://mlsanity.com/paper/2512.00193/) (2025)
- [AI capabilities can be significantly improved without expensive retraining](https://mlsanity.com/paper/2312.07413/) (2023)
- [AI-Augmented Predictions: LLM Assistants Improve Human Forecasting Accuracy](https://mlsanity.com/paper/2402.07862/) (2024)
- [Predicting Empirical AI Research Outcomes with Language Models](https://mlsanity.com/paper/2506.00794/) (2025)
- [Human-Written vs. AI-Generated Code: A Large-Scale Study of Defects, Vulnerabilities, and Complexity](https://mlsanity.com/paper/2508.21634/) (2025)
- [CTRL-ALT-DECEIT: Sabotage Evaluations for Automated AI R&D](https://mlsanity.com/paper/2511.09904/) (2025)
- [Scenarios for the Transition to AGI](https://mlsanity.com/paper/2403.12107/) (2024)
- [Frontier AI Auditing: Toward Rigorous Third-Party Assessment of Safety and Security Practices at Leading AI Companies](https://mlsanity.com/paper/2601.11699/) (2026)
- [Societal Adaptation to Advanced AI](https://mlsanity.com/paper/2405.10295/) (2024)
- [AI Behind Closed Doors: a Primer on The Governance of Internal Deployment](https://mlsanity.com/paper/2504.12170/) (2025)
- [On the Origin of Algorithmic Progress in AI](https://mlsanity.com/paper/2511.21622/) (2025)
- [Bare Minimum Mitigations for Autonomous AI Development](https://mlsanity.com/paper/2504.15416/) (2025)
- [Note on Selection Bias in Observational Estimates of Algorithmic Progress](https://mlsanity.com/paper/2508.11033/) (2025)
- [Preparing for the Intelligence Explosion](https://mlsanity.com/paper/2506.14863/) (2025)

## Built on by (most cited first, 9 of 9)

- [Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering](https://mlsanity.com/paper/2607.28568/) (2026)
- [From AGI to ASI](https://mlsanity.com/paper/2606.12683/) (2026)
- [The Dynamics of Intelligence Explosions](https://mlsanity.com/paper/2608.14426/) (2026)
- [Risk Reporting for Developers' Internal AI Model Use](https://mlsanity.com/paper/2604.24966/) (2026)
- [Order Is Not Control: Driven-Dissipative Response Laws Across Artificial and Biological Systems](https://mlsanity.com/paper/2606.12923/) (2026)
- [The Closing Window: How Governments Could Lose Their Ability to Restrain Advanced AI](https://mlsanity.com/paper/2608.05173/) (2026)
- [Recursive Criticality of AI Self-Improvement](https://mlsanity.com/paper/2609.00137/) (2026)
- [RECLAIM: Can Agents Reproduce the Claims of Machine Learning Papers?](https://mlsanity.com/paper/2609.28850/) (2026)
- [Comprehension Audits to Mitigate Risks from Automated AI Research](https://mlsanity.com/paper/2610.10064/) (2026)

## Abstract (arXiv)

The automation of AI R&D (AIRDA) could have significant implications, but its extent and ultimate effects remain uncertain. We need empirical data to resolve these uncertainties, but existing data (primarily capability benchmarks) may not reflect real-world automation or capture its broader consequences, such as whether AIRDA accelerates capabilities more than safety progress or whether our ability to oversee AI R&D can keep pace with its acceleration. To address these gaps, this work proposes metrics to track the extent of AIRDA and its effects on AI progress and oversight. The metrics span dimensions such as capital share of AI R&D spending, researcher time allocation, and AI subversion incidents, and could help decision makers understand the potential consequences of AIRDA, implement appropriate safety measures, and maintain awareness of the pace of AI development. We recommend that companies and third parties (e.g. non-profit research organisations) start to track these metrics, and that governments support these efforts.