AI makes AI better.
A daily hub for work where AI is part of the loop that improves AI, each entry with its loop spelled out. Read more →
12 picked from 14 candidates · 31 sources read · 75 loops indexed
- 01Loop of the day2 Nov 2026Research automation
Independent Assessment of AI R&D Acceleration at Anthropic in October 2026
The loop: Anthropic AI systems perform AI R&D work, which improves Anthropic's models and tools. This makes the systems or their direct successors better at performing further AI R&D work.
Independent confirmation that frontier labs are actively using their own models to accelerate internal AI R&D.
- 026 Nov 2026Tools and harness
HarnessFlow: MCTS-Guided Harness Optimization for LLM Agents
The loop: A coding agent uses Monte Carlo Tree Search to iteratively search for and refine its own executable harness. This optimized harness directly governs the agent's interaction with tools and the environment, improving its performance on subsequent tasks.
- 035 Nov 2026Research automation
Review of the "Risks from automated R&D" section in Anthropic's Risk Report (August 2026)
The loop: AI systems at Anthropic are used to accelerate their own R&D, a process independently verified by METR.
loop fit 9/10via METR
- 046 Nov 2026AI R&D evals
IdeaTaste: Do LLMs Share the Research Taste of Human Researchers?
The loop: LLM agents write research proposals based on a literature snapshot, which are scored against actual future publications. This benchmark measures AI performance on AI research tasks, providing the necessary evaluation signal to improve future AI research agents.
- 055 Nov 2026Research automation
Independent Assessment of AI R&D Acceleration at Anthropic in October 2026
The loop: Anthropic AI systems perform AI R&D work, which improves Anthropic's models and tools, which makes the systems better at AI R&D work.
loop fit 9/10via METR
- 066 Nov 2026Training data
Recursa: Toward Recursively Self-Improving Foundation Model Systems
The loop: The Recursa foundation model system uses parallel learners to execute tasks and synthesize local improvements. These improvements are validated and merged into the system's own knowledge repository, enabling the model to adapt to new domains through recursive self-improvement.
- 075 Nov 2026Self-reward and self-play
SEAL: Synergistic Co-Evolution of Agents and Learning Environments
The loop: LLM agents generate execution failures that are used as a shared diagnostic signal to simultaneously reshape the learning environment and adjust how the policy learns. This keeps the training environment aligned with the agent's evolving needs, directly improving the agent's subsequent training.
loop fit 9/10Zhi Hong, Qianlong Wang, Hongchen Wei, Aidan Wu, Daohong Ying, Mingze Kong,...via arXiv
- 086 Nov 2026Self-reward and self-play
Better honeypots, fewer tries: training an optimizer to make PPO cheat
The loop: A learned optimizer searches for gridworld levels that induce reward-hacking in PPO. This automated discovery of honeypots improves the evaluation of reinforcement learning algorithms, helping developers build more robust training processes.
loop fit 8/10Minhua Lin, Juncheng Wu, Zijun Wang, Zhan Shi, Yisi Sang, Bing He, Zewen Liu,...via arXiv
Loop index
All 75 entries →
- Gemini models propose code changes
- Evaluators keep best scoring versions
- Finds better Gemini training kernels
- Lowers cost to train next Gemini models
- repeat
- Coding agent edits its own code
- Scored child agents enter the archive
- Archived agents make later self modifications
- repeat
- RL agent lays out TPU blocks
- Better layouts make AI hardware cheaper
- Agent pre-trains on earlier chip generations
- repeat
- Coding agent edits LLM training script
- Runs a 5-minute training job
- Keeps change if validation improves
- Changes transferred to larger models
- repeat
- Research agent writes harness mutation blueprints
- Coding agent implements harness module changes
- Winning harness becomes the new champion
- New champion harness runs further generations
- repeat
- Aligned model generates training data
- Reward model judges and filters it
- Data trains next intermediate instruct model
- Next model generates better alignment data
- repeat
- Model generates candidate responses to prompts
- Model scores them itself as a judge
- Scores become preference pairs for DPO
- Trained model becomes next generator and judge
- repeat
- Claude Code writes its own code changes
- Changes become next Claude Code versions
- Release becomes harness for next development round
- repeat
- LLM agents write and benchmark kernels
- Kernels speed up AI model training
- Session data post-trains smaller kernel models
- repeat
- Coding agent writes fine-tuning code
- Agent post-trains a base LLM
- Trained model scored on target benchmark
- One turn of model training another
- repeat
- Learned optimizers update other learned optimizers
- Training keeps best performing optimizer parameters
- Better optimizers train each other faster
- repeat
- LLM reads reasoning model thoughts
- LLM flags reward hacking behavior
- Signal feeds back into training reward
- Trains more aligned capable agents
- repeat
Support for loop research
How to apply →Grants
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About grants →Fellowships
Funded time, compute and mentorship for researchers who want to go deep on one loop.
About fellowships →Compute
GPU time and credits for loop experiments, which almost always have to run more than once.
About compute →Have a loop worth building?
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