Self-improving AI / MLEvolve
MLEvolve
“We present MLEvolve, an LLM-based self-evolving multi-agent framework for end-to-end machine learning algorithm discovery.”
MLEvolve is one of the projects on the self-improving AI map. Below is what it changes about itself, whether the change lasts, and whether it improves the improver, each with the line from its own source.
Where it sits
Columns: what it changes, from the surface to the deepest change. Rows: whether the change outlasts the run. A number is how many projects sit in that square. See it on the 3D map
- What it changes
- Prompts, memory, context“a dynamic global memory that automatically accumulates and retrieves historical experience during search”
- Does the change last
- Gone when the run ends“a dynamic global memory that automatically accumulates and retrieves historical experience during search”
- Does it improve the improver
- does not improve how it improves“gradually shifts the search from broad exploration to focused exploitation with an entropy-inspired progressive schedule”
- Who sets the goals
- people set tasks and scoring“MLE-Bench comprises 75 carefully curated Kaggle competitions”
- How it was tested
- own benchmark numbers“MLEvolve achieves a 65.3% average medal rate on MLE-Bench under a 12-hour budget”
Self-evolving here means memory and search state that grow during one task's search. The abstract gives no number for the AlphaEvolve comparison.
Does it count as RSI?
It depends on the definition. Under the four used on What counts as RSI, by what its own source says:
- Loose (Eliezer Yudkowsky, Intelligence Explosion Microeconomics (2013)): does not count
- Durable (Ren et al., self-improvement survey (2026)): does not count
- Strict (Roman Yampolskiy, From Seed AI to Technological Singularity (2015)): does not count
- Autonomous (Anthropic, When AI builds itself): does not count
How it got here
Earlier work in this catalog that MLEvolve cites, oldest first. Under each: where that work sits on the map, then where MLEvolve sits (changes in bold), and what MLEvolve's own paper says about it. Its whole family on the citation map
2025 AlphaEvolve uses it
Prompts, memory · gone when the run ends · does not improve the improver
“To assess cross-domain generalization, we also use 15 open-ended mathematical optimization tasks from AlphaEvolve [24].”
“As shown in Table 2, MLEvolve achieves the best result on 11 of 15 tasks when compared with specialized algorithmic discovery methods, including AlphaEvolve [24], AlphaEvolve-v2 [11], SimpleTES [33], TTT-Discover [34], and OpenEvolve [26].”
2026 MLEvolve
Prompts, memory · gone when the run ends · does not improve the improver
Cited by Dream-RSI 2026, Pu, recursive self-improvement 2026
In the same square
ADAS: Automated Design of Agentic Systems, AlphaEvolve, AREX-2, LM2LM, ParetoRSI-Gym, PostTrainBench, Promptbreeder, Reflexion, Self-Refine, The AI Scientist