Autonomous research
ScholarLoop
An autonomous research loop that can't grade its own homework — frozen scoring, verified numbers, self-rejecting papers.
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Long-horizon AI systems that reason, remember, reflect, and improve over time.
r2m.ai is an independent research and product lab exploring long-horizon agents, memory systems, autonomous research, and reliable behavioral control.
What we're exploring
Long-horizon agents
Beyond the single turn
Agents that stay coherent across hours, days, and weeks of work — pursuing goals that don't fit in one context window.
Memory systems
Systems that remember
Persistent memory that accumulates experience, recalls what matters, and turns an agent's history into its advantage.
Autonomous research
Machines that investigate
Systems that form hypotheses, run experiments, reflect on the results, and refine their own understanding over time.
Behavioral control
Reliable by design
Long-running autonomy demands predictability — systems that are steerable, auditable, and safe to trust with real work.
Research
Working systems, not demos
Agents in markets
AlphaAgent
ML price models, 101-Alphas factors, options strategies, and guarded live execution — up 53.8% in live paper trading vs the S&P's 13.5%.
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Signal & memory
LatentFeed
Six sources, one briefing — the day's AI research collected, distilled, and delivered before you wake up.
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Our team
Built by researchers, engineers, traders from
The most interesting problems in AI are just beginning.
We're a small team working on agents that last — and we're looking for people who want to build them with us.