Agent objective
- Interact with the supplied stateful environment.
- Produce verifier-checkable actions or artifacts.
- Maximise scalar reward under the package contract.
Five deep-frontier mathematical investigations across polyhedral homotopy, symplectic capacities, graphon stability, microlocal propagation, and motivic regulators. Each environment has a distinct scientific state machine: agents must route observation-dependent diagnostics, manage nuisance variables and irreversible choices, preserve future options, and satisfy separate posterior, prediction, decision, and path-quality gates.
Five deep-frontier mathematical investigations across polyhedral homotopy, symplectic capacities, graphon stability, microlocal propagation, and motivic regulators. Each environment has a distinct scientific state machine: agents must route observation-dependent diagnostics, manage nuisance variables and irreversible choices, preserve future options, and satisfy separate posterior, prediction, decision, and path-quality gates.
Enough detail to understand the intellectual terrain; generated instances, hidden mechanisms, and solution paths remain inside the private package.
| Environment | Mathematical or technical frontier | Adaptive research problem |
|---|---|---|
| PolyhedralHomotopyLab | Sparse polynomial systems and continuation | Choose mixed-cell, path, endgame, and certification operations while preserving viable continuation branches. |
| SymplecticCapacityLab | Symplectic embeddings and capacities | Coordinate capacity obstructions, embeddings, and normalization evidence under limited geometric interventions. |
| GraphonStabilityLab | Graph limits and extremal stability | Distinguish structural graphon explanations through adaptive densities, perturbations, and stability tests. |
| MicrolocalPropagationLab | Wavefront propagation and PDE diagnostics | Route phase-space observations and interventions to recover hidden propagation mechanisms and nuisance structure. |
| MotivicRegulatorLab | Motivic cohomology and regulator evidence | Assemble compatible arithmetic and period evidence while retaining paths to sealed regulator predictions. |
A recomputed 40-episode calibration exactly matched the packaged records and passed 132/132 tests. The strongest continuation-aware control averaged 0.6817 and passed only one of ten hard/expert cases. One non-passing episode scored 0.9614 but failed the path-quality gate, showing that excellent belief and prediction cannot excuse a wasteful or scientifically invalid investigation path.
We publish aggregate behavior and task structure, while withholding generated instances, hidden labels, exact successful probes, private checks, and solution trajectories.
Shown with its provenance and limitations; it is not a performance guarantee.
Reported result from the evaluation artifact supplied with this package.
As identified by the supplied artifact.
10 reported runs.
Ulam_RLVR_run_report.md
Machine-readable provenance and the exact displayed metric are available in results.json.
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Package SHA-256
4085aeecb8f39c3603580764027ca0f660306596cb1e872ed4ab48bb58eeed5eOne purchase licenses this identified item to one legal organisation for worldwide, perpetual commercial model training, evaluation, research and development. Redistribution and resale of the package are not permitted.