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A daily hub for work where AI is part of the loop that improves AI, each entry with its loop spelled out. Read more →

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A loop in motion

Project · Google DeepMind

Gemini models propose code changes

From the labalphaBell Qwen Open27B post-training research, with capability tradeoffs measured.Explore the model → From the labModel ObservatoryMeasured Flint and Qwen traces with published decoder maps.Open the lab tool → From the labMiniMax Video BudgetVisualize sampled frames, two-frame groups and vision placeholders.Try the inspector → From the labMOSS Blind Layout CheckCompare exact text and placement in two local images.Try the blind check → From the labAgent Request Evidence LadderSee what a trace proves about an agent's request and its outcome.Explore the cases →

Today's radar

9 Nov 2026Full radar →

12 picked from 92 candidates · 31 sources read · 76 loops indexed

01
Loop of the dayNewsAlignment Forum8 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.
Anthropic's internal report suggests the company has crossed its 'dramatic acceleration' threshold, marking a major milestone in AI automating its own R&D.
loop fit 10/10Zach Stein-Perlmanvia Alignment Forum
02
PaperarXiv6 Nov 2026Tools and harness
Flex-RSI: Benchmarking Recursive Self-Improvement of Frontier Agents
The loop: An improver agent revises its own harness and executor based on task feedback, which improves the agent's ability to solve vision and robotics tasks in subsequent rounds.
loop fit 9/10Jingdong Zhang, Jionghao Wang et al.via arXiv
03
ProjectGitHub4 Nov 2026Tools and harness
meat-daemon-404/dsh-plugin-immune
The loop: A coding agent converts its own failed tool calls into immunity rules, which improves the agent's future performance by preventing repeated mistakes.
loop fit 7/10meat-daemon-404via GitHub
04
Blog postAnthropic6 Nov 2026Self-modifying agents
The components of agent memory
The loop: Claude uses a dreaming mechanism to organize memory and correct mistakes, which improves its internal state, which makes the model better at learning from experience.
loop fit 7/10via Anthropic
05
PaperarXiv9 Nov 2026Research automation
ReproBridge: Building Reproducibility into ML Research Artifacts
The loop: The ReproBridge agent constructs reproducible research artifacts, which improves the ability of successor agents to reproduce and build on ML research.
loop fit 9/10Pranjal Srivastavavia arXiv
06
PaperarXiv6 Nov 2026Kernels and compilers
MorphSched: Workload-Aware Adaptive Scheduler Generation for Linux
The loop: MorphSched uses an LLM to synthesize Linux schedulers, which improves system performance, and uses that performance feedback to refine its generation strategy.
loop fit 9/10Baolin Chong, Dongfang Xu et al.via arXiv
07
PaperarXiv6 Nov 2026Self-modifying agents
Test-Time Learning with an Evolving Library
The loop: EvoLib extracts knowledge abstractions from its own inference trajectories to build a shared library, which improves the model's performance on subsequent problem instances.
loop fit 9/10Yuhao Tan, Hanyun Jiang et al.via arXiv
08
PaperarXiv9 Nov 2026Tools and harness
meat-daemon-404/dsh-plugin-immune
The loop: A coding agent converts its own failed tool calls into immunity rules, which are injected to prevent repeated mistakes, improving its future performance.
loop fit 7/10Jianuo Shen, Pengyi Xu, Bo Zhangvia arXiv