Banger paper on RSI with foundation model systems.

(bookmark it)

Seriously, I highly recommend this paper if you want to start experimenting with RSI in your own company.

Lots of great insights and tips.

They propose Recursa, which raises DeepSeek-V4.1-Flash from 30.0% to 55.0% all-pass on held-out legal tasks, without a stronger teacher or human experts.

The model weights stay the same. The gain comes from a knowledge repository the system writes for itself.

The repository stores domain skills, instructions on when to use them, memories of past tasks, and reflections.

Recursa first builds it from domain materials. Then parallel learners run tasks and propose fixes, and only validated fixes get merged back.

On Harvey LAB's Firm Knowledge benchmark, Qwen3.8-Flash goes from 35.0% to 46.0% with the same loop.

If you are adapting an agent to a new domain, try a validated skill repository before reaching for fine-tuning.

https://arxiv.org/abs/2611.04035

Chat with Paper: https://academy.dair.ai/papers/recursa-toward-recursively-self-improving-foundation-model-systems-2611.04035