Self-improving AI / Épi
Épi
“We present Épi, an autonomous research agent that sustains investigations across model sessions and evolves its methods through accumulated experience.”
Épi 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“Épi stores code, artifacts and detailed research notes in a persistent filesystem and keeps retrievable lessons and dead ends in a memory store outside the model context.”
- Does the change last
- Saved for later runs“Épi retains both the experimental state needed to resume a campaign and lessons intended for reuse on another problem.”
- Does it improve the improver
- improves the task agent; no evidence the improver got better“The agent adapts its methods through experimental feedback, retrieved memory and independent review.”
- Who sets the goals
- people set tasks and scoring“A curator supplies the question and source material; the agent chooses the methods and experiments.”
- How it was tested
- own benchmark numbers“We fix the feasibility tolerances and gain floor before the investigation.”
Date is arXiv v1. The runs used GPT-5.6 Sol and GPT-6-Astra with weights fixed. What carries over between sessions is a memory store of lessons and dead ends, versioned branches of research code, and the reviewer's recorded assessments; the paper reports no comparison with any of them switched off. A record is a result that the fixed verifier accepts as improving a documented literature or registry reference, with references as of September 16, 2026. The authors set each task's verification rule and tolerances; the paper reports no outside re-check. 97 of the 100 construction records are geometric packings; the nine packings in Figure 3 improve by 0.0021% to 0.0527%. The 20 numerical-bound records include an approximately 84-fold narrower interval for the n-queens constant. Held-out instances are used only for Épi-turbo: on 30 FrontierOR tasks new to both systems, it scored 53.96 to Codex's 51.21 with the same model, from one run per task and system.
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 Épi cites, oldest first. Under each: where that work sits on the map, then where Épi sits (changes in bold), and what Épi's own paper says about it. Its whole family on the citation map
Cites AlphaEvolve 2025, Dream-RSI 2026