Introducing OpenRSI Index

To take ASI seriously is to accept a weak-to-strong premise: human intelligence can build a process that eventually produces intelligence beyond itself. Whether the RSI loop can move past the best human-designed method is the last puzzle on the road to ASI.

Scaling on insights. Progress is limited not only by compute and data, but by the bandwidth of insights. An agent scales both the generation of ideas and their implementation, and sweeps a far larger region of method-space. That is the ambition of RSI, and exactly the thing worth measuring carefully.

From the community, for the community. RSI is built from generations of ideas, code, data, and experience from researchers, independent labs, and domain teams alike, and it should return the benefits to everyone who builds models. We aim to keep RSI open through shared platforms and tools, so more people can participate and benefit.

Shape RSI together. We set RSI’s standards together with the community and challenge frontier models with the hardest problems in our fields. Through about 1 hour of conversation, RSI-Anything (our contribution pipeline) helps clarify research questions, constraints, and evaluations, then packages into runnable autoresearch benchmark tasks. We want researchers and agents to solve these problems together, sharing new insights, methods, and results with the community.

Research will never end. A game turns zero-sum only when the pie is too small to share or a field has a finite ceiling. However, research is definitely the field with the highest ceiling there is. Better Human–AI collaboration makes it more prosperous to be a positive-sum game. What shifts is the mindset: it frees researchers to find and formulate the crazier, more valuable, more exciting problems in the world.

1. Real-world research, not fabricated toys. Open research community will have the benchmark of our own. Every RSI environment is sourced from a real, fully open-source model research project.
2. Optimization, not reproduction. The agent starts from the original code, data, and checkpoints to deliver an artifact that compares with the original recipe.
3. Scientific discovery, not parameter sweeping. Form hypotheses, change methods, learn from experiments, and land gains that hold under a fixed scientific contract.
4. Production scale, not proof-of-concept. Problems frontier researchers care about, where improvements matter and transfer to real training.

OpenRSI Index is an evaluation-and-research-production loop for model development:

Open projects enter the index through RSI-Anything, the contribution pipeline that turns a research idea into a ready-to-run Harbor task. In an RSI task, an agent receives the editable source for a real open project, a fixed budget, a concrete experiment, and a verifier it cannot alter.

The score itself is deliberately simple:

An RSI task is a bounded experimental lane extracted from a runnable open project: an end-to-end run, a repository-published ablation or deployment lane, or a faithful, separately versioned slice of a larger pipeline that preserves the causal mechanism and evaluation path of the source project.

Projects pass five gates before task design begins — fully open, influential, reproducible, researchable, and verifiable. Once a project is admitted, the pinned repository becomes the scientific source of truth for the selected task's model, data, configuration, training procedure, evaluator, and baseline.

Every task ships in Harbor format: an instruction.md that states the objective and the frozen setup, a policy.yaml that draws the editable boundary, the pinned author repository as the workspace, and a verifier that runs in a separate clean image. The verifier diffs the candidate workspace against a clean checkout, applies programmatic and LLM-as-judge integrity checks, and fails closed to zero on a hard violation — reward-path tampering, evaluation leakage, or budget overrun.

<task>/
  ├── task.toml
  ├── instruction.md     # objective, setup, baseline, loop, submission
  ├── policy.yaml        # editable scope, locked variables, hard zeros
  ├── environment/
  │   └── project/       # pinned author repository — the workspace
  └── tests/
      ├── verifier       # separate clean image, fixed evaluation
      ├── integrity gates
      └── reward mapping # fails closed to zero

RSI Harness powers OpenRSI Index for ultra-long-horizon RSI runs, natively supporting Harbor-format tasks, with or without GPUs, from single-node local Docker to multi-node clusters.

The harness runs the outer loop only: preparing the task, pausing and snapshotting Work, scoring each submission in a fresh Judge, and selecting the final result. The inner loop is whichever agent you put in it — Claude Code, Codex, or an agent you design yourself.

Each run begins by validating the Harbor task and resolving its images, resources, network policy, and timeouts into a run plan. The agent develops and refines solutions in a persistent, isolated Work container: inspecting code and earlier feedback, forming hypotheses, and running experiments on the allowed surface. The task's private tests stay outside Work.

Calling rsi-submit asks the harness to pause Work and snapshot its filesystem. A fresh Judge container evaluates that snapshot with the private tests, records the reward and test output, and is removed. The same Work container resumes with that feedback, allowing the agent to iterate until the submission limit or timeout. Judge filesystem changes never flow back into Work. The harness retains the best valid submission as the final result, selected by the primary reward and its configured scoring direction.