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 →
7 picked from 17 candidates · 31 sources read · 76 loops indexed
- 01Loop of the day9 Nov 2026Research automation
Where is all the AI safety research uplift?
The loop: Frontier AI models perform research and engineering tasks to accelerate capability progress. This increased development velocity directly speeds up the creation of successor models, which are in turn more capable at research and engineering.
Anthropic's reported capability uplift offers a rare, quantified glimpse into the reality of AI accelerating its own research and development.
loop fit 9/10Yifan Zhang, Hengxuan Lou, Xinkui Zhao, Rengrong Xiong, Naibo Wang, Chang Liu,...via arXiv
- 029 Nov 2026Kernels and compilers
MorphSched: Workload-Aware Adaptive Scheduler Generation for Linux
The loop: An LLM synthesizes custom Linux schedulers and profiles their performance on target workloads. The system accumulates this performance feedback in an evolutionary memory pool to refine its own generation strategy for future schedulers.
- 039 Nov 2026Self-modifying agents
Test-Time Learning with an Evolving Library
The loop: A language model extracts modular skills and reflective insights from its own inference trajectories to build a shared library. It then reuses and evolves these knowledge abstractions to improve its performance on subsequent problem instances without parameter updates.
- 045 Oct 2026Inference efficiency
TailSieve: Partial-Rollout-Guided Tail Routing for LLM Rollouts
The loop: A controller optimizes the routing of long-tail generations during large-scale rollouts. This improves the efficiency of reinforcement learning post-training and distillation, which makes the training of successor models faster.
- 059 Nov 2026Self-reward and self-play
AutoRefine: Auto-Calibrating Code Judges from Workflow Outcomes
The loop: A teacher model observes noisy workflow outcomes like test failures and rewrites the prompt of a deployable code judge to reproduce those labels. This auto-calibration improves the judge's accuracy in evaluating future code changes.
- 068 Nov 2026Research automation
Ajeya: AI is dramatically accelerating AI research right now
The loop: Anthropic's AI systems automate AI R&D, which accelerates capabilities progress, leading to better successor models that are even better at automating AI R&D.
- 078 Nov 2026Tools and harness
Review the Episodes, Refine the Lessons: Simple Memory Evolution for Embodied Agents
The loop: A multimodal language model writes and refines its own text memories based on its execution experience in embodied tasks. It updates this working memory with lessons on procedure and recovery, making the same system better at navigation and manipulation.
- 089 Nov 2026AI R&D evals
Always-On-Intelligence/HALO
The loop: An evaluation harness audits whether self-evolving agents successfully learn from their outcomes. This measurement provides the necessary signal to improve the agents' learning mechanisms, making them better at self-evolution.
loop fit 8/10Haozhen Zhang, Quanyu Long, Jianzhu Bao, Tao Feng, Weizhi Zhang, Haodong Yue,...via arXiv
Loop index
All 76 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.
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