Remyx AI

Remyx AI — ExperimentOps Infrastructure

A scientific interface for debugging, evaluating, and iterating on AI systems.

Remyx AI offers infrastructure for ExperimentOps, a principled layer for managing the design and evaluation of AI systems.

ExperimentOps is a set of practices and methods to operationalize how we learn from a growing history of experiments and design better systems under practical constraints.

🧪 Why ExperimentOps?

AI development is fundamentally empirical. But as the design space grows, it becomes computationally and operationally intractable to explore all combinations.

ExperimentOps provides a formal structure for reasoning under this complexity:

• Every system variant is an intervention; every evaluation is an outcome.
• By modeling experiment history causally, not just correlationally, we identify what contributes to downstream performance.
• Instead of trial-and-error, we build structured knowledge from cumulative evidence.

This causal framing enables teams to experiment with purpose: prioritizing what to try next, what to revisit, and what to discard.

🛠️ What You'll Find Here

• Model variants – e.g., SpaceThinker-Qwen2.5VL-3B, SpaceOm, and others trained through structured, reproducible workflows.
• Open datasets – Synthetic multimodal datasets created with tools like VQASynth.
• Evaluation analyses – Curated results and leaderboard comparisons published via Hugging Face model cards and evaluation tables, reflecting structured experiments conducted in Remyx and other platforms.

Mission: Help teams reason clearly about what works and why, treating experimentation as a scientific process, not guesswork.

Learn more at remyx.ai