Open, rigorous research into trustworthy reasoning systems.

We study how to make machine reasoning stable, transparent, and reliable.
Our goals:

• Robustness: mitigate brittleness, distribution shift, and adversarial quirks in chain-of-thought and tool use.
• Evaluation: build principled benchmarks and analyses for reasoning quality, not just accuracy.
• Interpretability: probe intermediate representations and step-by-step computations.
• Open science: release data, code, and write-ups for reproducibility.

• Empirical research: experiments on reasoning traces, tool-augmented inference, and verification.
• Method work: training/decoding strategies (self-verification, deliberation, debate, search).
• Evaluation assets: datasets, metrics, and harnesses tailored to reasoning stability.
• Community artifacts: baselines, dashboards, and concise reports.

• Preprints / whitepapers will be listed here with DOIs or arXiv IDs.
• Short technical notes live in repo’s /reports folder.