To know what “Google RSI” actually is
Dream-RSI
Weights stay frozen. The system rewrites exploration-policy code using past discovery trees.
THE SEPTEMBER READING LIST
Updated 24 September 2026
12 picks · 13 reads
To know what “Google RSI” actually is
Weights stay frozen. The system rewrites exploration-policy code using past discovery trees.
The skill/wiki paper everyone cites
Agent experience becomes a persistent wiki that informs reusable, evolving skills.
A new way to use papers
Stanford’s Nature paper turns research and code into interactive agents. Works with Claude Code.
Why long-horizon agents lose the plot
An approach to planning with execution structures that agents can inspect and evolve.
Long context without another RAG lecture
Language models explicitly declare where to attend, making attention more selective.
Agents that run for days
A framework for multi-day autonomous software development with continual improvement.
What a research-agent harness looks like
Google Research’s many-agent approach to long-horizon mathematics and theoretical computer science.
How to RL an agent that already has a harness
Microsoft’s reinforcement-learning framework trains agents while retaining their existing harnesses.
What real-world misuse of Claude looks like
Anthropic’s September report documents disrupted operations across cyberattacks, surveillance, fraud, and other harm areas.
How NVIDIA improves harnesses and mathematical reasoning
SoL-Pi evolves efficient agent harnesses. An Open Recipe for IMO Gold describes Nemotron’s training and proof-search pipeline.
“The harness is the model” made concrete
A model generates task-specific harnesses just in time, then repairs and improves them.
Vocabulary so RSI threads don’t eat you
A survey of recursive self-improvement: definitions, mechanisms, and the gap between current systems and the larger claim.
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