Auto-researchComing soon
The frontier research agents we run on Tensile — thesis exploration, paper→code, post-training, live-agent improvement. Today they run as our own internal agents; the self-serve product surface is in the works.
What auto-research will do
Explore & validate a thesis
Map a research space with a search-driven literature scan, then validate the thesis end-to-end — A/B variants with a judge model, or benchmark inference for latency and quality.
Build a repo wiki
autowiki maps a repository into a living wiki — architecture, modules, and a code graph. Deterministic, no LLMs required.
Run E2E research
autoresearch takes a thesis end-to-end: explore, experiment, synthesize, and optionally implement the result.
Improve a codebase
Run autocode against a repo to refactor, add tests, or apply a research recipe. Local by default; microVM isolation when it matters.
Paper → code
Hand paper2code a PDF or arXiv id. It paces through extraction, planning, drafting, and verification — pausing for approval at the gates that matter.
Code → paper
code2paper turns a thesis, experiments, and benchmarks into a publishable output — blog, whitepaper, research paper, or executable notebook.
Post-train a model
Point autotune at a base model and dataset and pick a method — SFT, RL, DPO, or LoRA. Get back a trained adapter plus metrics, scheduled through Tensile.
Improve a live agent
autoimprove reads a running agent's logs and synthesizes an optimal training loop — rubrics, verifiers, and a dataset — ready for autotune.