A causal world engine for experimental intelligence
Give experimental intelligence its own world engine¶
Static benchmarks ask what a model knows. ChemWorld asks how it experiments when the answer is hidden.
ChemWorld is a replayable causal world-model environment. Under partial observability, finite budgets, and operational constraints, agents choose operations and measurements, form hypotheses, and revise strategies from evidence. Hidden kinetics, phase behavior, and process rules can change while the public task remains stable, so memorizing one optimal recipe is not enough.
ChemWorld is not a universal real-reaction predictor. It is a research environment for making experimental decision making scalable, comparable, and falsifiable.
Current evidence¶
Two flagship tasks now have formal descriptive no-dossier campaigns and paired
opaque / nominal / misindexed anonymous-material experiments. Correct
information has confirmed positive value in electrochemistry and an inconclusive
effect in crystallization. A targeted wrong prior changes early behavior in both
tasks, but neither passes the joint recovery rule.
A five-task, five-world development extension is also complete. The shared Codex
strategy is above the best classical method mean in electrochemistry and the new
reaction–distillation task, but below it in crystallization, partition, and
continuous flow. All 15 task contracts are executable; two tasks have formal
results and three more now have development comparisons. Historical RC28 Gate A
has a stale current-source binding, and Participant Gates B–E remain unexecuted.
ChemWorld is therefore a candidate research environment with
benchmark_ready=false, not a completed leaderboard release.
Inspect exact results and status →
Why a world engine¶
Real chemical experiments are slow, costly, and risk-bearing. Static datasets test knowledge and prediction, but not whether an agent selects an informative experiment, interprets failure, manages resources, or adapts when its model is wrong.
| Static chemistry benchmark | ChemWorld |
|---|---|
| Answer a given question | Decide what experiment to do next |
| One-shot input and output | Repeated observation and action |
| Fixed data and rules | Intervenable hidden world rules |
| Error lowers a score | Error consumes budget and changes state |
The central experiment¶
The same public task can run under different rate laws, reaction topologies, constitutive relations, or equipment boundaries. Agents are not given a world label. They must use experiments to detect which assumptions still hold and recover when the rules change.
Three agent tracks¶
- Campaign Design: choose the next complete experiment—BO, safe BO, active learning, recipe-level LLMs.
- Procedure Execution: choose the next operation—hierarchical RL, state machines, operation-level LLMs.
- Process Control: choose bounded equipment setpoints and process-control actions—SAC, MPC, system identification, world-model control. This is not a claim of universal high-frequency continuous control.
World-model adaptation cuts across all three: infer the current world from history and recover quickly after a shift.
Start here¶
| Goal | Page |
|---|---|
| Understand the research thesis | Why ChemWorld |
| Define experimental intelligence | Experimental Intelligence |
| Understand changing worlds | Causal Worlds |
| Read the evaluation design | Benchmark |
| Inspect current evidence | Research Findings |
| Understand the real-world path | Real-world Bridge |
The complete API and local setup reference currently lives in the Chinese technical documentation.
Research status: benchmark candidate. Engine and replay controls are operational; formal cross-method adaptation, private evaluation, and external bridging remain incomplete. RC28 Gate A passed on its frozen source, but its current source binding is stale and current
benchmark_ready=falsepending recertification.