EdotEnv

EdotEnv is a Quant Neolab building toward RSI. They turn markets into self-improving research environments for AI agents. The path to RSI is a continuous loop: agents conduct research, learn from the results, and autonomously become better at conducting the next round of research. For that loop to continue, each round must present a harder problem than the last. Markets create this dynamic naturally. Every discovered inefficiency attracts competition, and every successful strategy makes future opportunities harder to find. Quant research therefore provides an advancing frontier for self-improving agents. EdotEnv captures this process in multi-step environments where agents form hypotheses, design experiments, verify results and iterate. They use these environments to evaluate and post-train agents on increasingly difficult research tasks. They work with frontier AI labs and academic groups building research harnesses, evaluation benchmarks, and post-training environments. EdotEnv's team previously worked in quantitative research at G-Research, high frequency options trading at TransMarket Group, and LLM inference research at Etched.