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AQuA is a research system with two independent language-model-driven agents that recursively self-improve in quantitative trading research, achieving strong information coefficients on crypto and US equities while using sealed sandboxes to prevent data leakage.
Google co-founder Sergey Brin is reportedly advocating for Recursive Self-Improvement (RSI) in AI, pushing the company toward more ambitious artificial general intelligence research.
Dwarkesh Patel interviews Ryan Greenblatt about the plausibility of recursive self-improvement, timelines for automating AI R&D, and the alignment implications of superintelligent AI systems.
Dwarkesh Patel and Redwood Research chief scientist Ryan Greenblatt discuss how once AI can automate AI research, recursive self-improvement could accomplish the equivalent of four to five years of AI progress in one year. The conversation discusses the verifiability of AI research and development and feedback loops.
This paper introduces the Mendel Gödel Machine, a recursive self-improving framework that applies comparative evolution to iteratively improve coding agents.
A conceptual essay arguing that recursive self-improvement in AI is limited by verification, not computation, using the metaphor of an epistemically closed prompt matrix and the data-processing inequality.
Miles Brundage argues that normalizing recursive self-improvement as a goal to explicitly pursue was a huge mistake, highlighting concerns about AI safety and alignment.
Google DeepMind CSO Jasjeet Sekhon discusses how AI infrastructure spending is driven by hopes for recursive self-improvement, citing AlphaEvolve's bounded gains like reducing Gemini training time by 1%.
Introduces PAST-Bench, a benchmark for evaluating whether personal AI agents improve from retained experience across sessions, and Hermes+, an extension with targeted interventions. Finds improvement is real but uneven across capabilities and models.
This paper introduces OpenMLE, an open full-stack system for studying recursive self-improvement in machine learning engineering, and presents Frontis-MA1, a 35B model post-trained as a meta-evolution agent. It shows significant improvement over its base model on MLE-BenchLite and transfers to held-out benchmarks, with weights and code released.
Lilian Weng, co-founder of Thinking Machines, stepped down citing health issues and then rejoined OpenAI to lead a team focused on recursive self-improvement research.
Cline used Kimi K3 to recursively self-improve its harness, boosting Terminal Bench performance from 77.5% to 88.8% and reducing run cost from $79 to $49.8 in 17 hours.
Anthropic supports a petition for slowing AI development, citing their research on recursive self-improvement to allow society time to prepare.
Anthropic publicly supports a petition aimed at pacing frontier AI development to allow society to prepare for potential risks, citing their own research on recursive self-improvement.
The author reflects on past LLM scaling eras (model size, chain-of-thought, agents) and speculates about the next big scaling dimension, suggesting recursive self-improvement as a potential breakthrough.
AREX introduces a family of recursively self-improving agents for deep research, alternating between an inner research loop and an outer self-improvement loop, trained with long-horizon reinforcement learning. It substantially outperforms comparable-scale baselines on benchmarks like BrowseComp and Humanity's Last Exam.
Weco AI announces first experimental evidence of recursive self-improvement (Level 1 RSI) using their AIDE and AIDE2 frameworks, where an AI improves the inner loop (model training) and outer loop (search algorithms) automatically, achieving results that surpass human-tuned systems 100x faster.
Researchers present AIDE², a system with recursive auto-research loops that improved its own code over 100 iterations, discovering seven improvements and beating a hand-tuned agent on held-out benchmarks.
Former OpenAI researcher Daniel Kokotajlo and the AI Futures Project released the report "AI 2040: Plan A", arguing that AI companies may first automate their own R&D, triggering a white-collar unemployment wave, and proposing international agreements and universal basic income to address the development of superintelligence.
This paper examines the economic incentives and dynamics of recursive self-improvement in AI systems, addressing how such processes could scale and their implications for governance and safety.