./research

Questions we're working on.

Running notes from the lab. These are open problems, not finished papers; we publish the questions first and the results when they survive contact with production.

• jul 2026

  A verifiable audit layer for AI

  A tamper-evident audit layer for model inference and agent execution: canonical serialization, domain-separated hashing, DSSE signatures, a per-run Merkle DAG, and a cross-run Merkle Mountain Range that proves no run was deleted. Every primitive was chosen from a head-to-head benchmark against the real alternatives, with the raw data committed. This note is the design and the experiments behind it.

  auditcryptographybenchmarksshipped
• jul 2026

  Reducing the token cost of long-running agents

  Agents burn most of their tokens re-reading context they've already seen: the same instructions, the same company facts, the same history, on every call. We're studying how far caching, context compression and precompiled knowledge can cut the marginal cost of an agent step without degrading its decisions.

  efficiencyagentsactive