RESEARCH BEYOND ASSUMPTIONS.
Independent research and engineering in intelligent systems.
Adaptive Compute · Reasoning & Learning · Scientific Computing · AI Infrastructure
Shinraya Tech Labs is an independent research and engineering initiative exploring how intelligent software can reason more reliably, allocate computation more efficiently, and operate as measurable systems.
We build prototypes, run controlled experiments, and publish what the evidence supports—including results that challenge our initial hypotheses.
Our principle: A compelling idea is a starting point. A reproducible result is progress.
Our portfolio spans research frameworks, experimental systems, and open-source engineering. A project being listed here does not imply that its research hypothesis has been validated or that it is production-ready.
| Project | Focus | Current evidence / status |
|---|---|---|
| Robust Reasoning RL | Verifier robustness, reward corruption, and reasoning-policy training | Framework and pilots completed; definitive GPU study pending |
| Quantum Advantage Simulator | Reproducible evaluation of quantum simulation and variational methods | v0.1.0 released; no general quantum advantage established |
| ATLAS | Geometry-aware retrieval and local compute-to-data experiments | Evaluated approaches did not outperform the tested cosine baseline |
| AdaCompute | Adaptive inference-time compute budgeting | Prototype and simulator benchmark; real-model validation pending |
| MYCELIUM | Evolutionary software mutation, benchmarking, and selection | Experimental runtime; end-to-end performance not yet verified |
Repository links above point to the existing public project locations. Update them after any verified GitHub transfers. Projects may have different maintainers and contribution histories; moving a repository does not erase those histories.
| Research direction | Questions we care about |
|---|---|
| Reliable reasoning | When do reward models and verifiers encourage correctness—and when do they reward shortcuts? |
| Adaptive computation | Can systems allocate less compute to easy inputs while preserving quality on difficult ones? |
| Scientific simulation | Which algorithmic claims hold under common benchmarks, noise models, and reproducible evaluation? |
| Retrieval systems | When do geometric or structural signals genuinely improve retrieval beyond strong baselines? |
| Autonomous software systems | Can constrained code evolution improve measured performance without breaking correctness or safety? |
01 / Hypothesis first. Define the problem, expected improvement, baseline, and failure conditions before celebrating a result.
02 / Build the smallest testable system. Favor an executable prototype and controlled evaluation over an ambitious feature list.
03 / Compare against meaningful baselines. Measure quality, cost, runtime, robustness, and reproducibility where relevant.
04 / Publish the limits. Distinguish completed experiments, prototypes, negative results, and future work.
05 / Iterate in public when appropriate. Share code, reports, and reproducibility artifacts when they are ready and permitted for release.
Python · TypeScript · PyTorch · Distributed Systems · Evaluation Infrastructure · Optimization · Scientific Computing · Open Source
These are areas of work and interest, not a claim that every project uses every technology.
We welcome thoughtful discussions with researchers, engineers, students, and open-source contributors interested in rigorous experimental work.
Good ways to engage:
- Open a focused issue with a reproducible bug, experiment proposal, or baseline comparison.
- Propose a benchmark that could falsify a project's central claim.
- Improve documentation, tests, instrumentation, or reproducibility.
- Share constructive technical feedback—even when it challenges the approach.
For project-specific questions, use the relevant repository's Issues or Discussions section where enabled. Public contact details will be added when available.