Research lab · Autonomous discovery
jimmylabs’ research arm. We’re building an autonomous discovery system that runs the research loop like a scientist — read, hypothesize, test, learn, repeat — getting smarter each cycle, and self-hosted so sensitive data never leaves your walls.
Every cycle starts better-informed than the last.
This is where we do our own research — active work in progress, not a product you can buy yet. Every direction below is honest about how far it has actually gotten.
Discovery moves only as fast as the hypothesize → test → learn loop turns. A human drives every step — so it turns slowly, and each cycle starts cold.
Thousands of hypotheses tested in parallel, narrowed to the few that hold. Months of work in days.
Every finding feeds the next, so each cycle starts smarter than the last — never from scratch.
You set the question. The platform runs the rest itself — reading the field, fanning out hypotheses in bulk, pruning them against evidence, folding every result back into what it knows. Below, one turn of the cycle.
The design is simple: you set the goal, it runs the loop around the clock, and every validated discovery comes back with the evidence behind it. That’s what we’re building toward — and pressure-testing honestly at each step.
Reads the literature — more than any lab could — mapping what is known and where the questions lie.
Forms hypotheses worth testing — thousands at once, not the handful a human has time for.
Every promising hypothesis is tested. Most are ruled out; the few that hold up earn their place.
Each result feeds back in. The next round builds on everything it has learned — compounding, not resetting.
Built to be self-hosted. The whole platform runs in your environment — no outside services required, fully air-gappable.
Everything runs on your infrastructure. Your data, hypotheses, and discoveries never leave it.
No mandatory third-party services or cloud calls. Runs completely air-gapped.
For pharma labs and universities, where embargoed results and proprietary methods stay inside.
What the loop turns up — grounded, cited, and honest about its limits.
Closed-loop materials discovery lives or dies on the surrogate model steering it — yet the literature keeps warning that surrogates fail silently off-distribution. Nobody has mapped the data budget where surrogate-guided search stops beating brute-force experimentation.
We are mapping that boundary with calibrated uncertainty, spending expensive experiments only where they sharpen it.
The field’s own audits list the ways agentic discovery fools itself: cherry-picked benchmarks, data leakage, metric misuse, post-hoc selection. Nobody has measured how many machine “discoveries” survive honest re-examination.
We are benchmarking how many survive an honesty harness — pre-registration, false-discovery-rate control, and replication — built into the loop, not bolted on.
Freeze the literature at year N, ask the system for hypotheses, then open year N+1 and score which ones the field went on to confirm. Markets have backtesting; hypothesis generation has almost none — and contamination makes naive attempts worthless.
We are building the contamination-controlled backtest: date-partitioned corpora, grounded proposals, and scoring against what was actually published next.
Post-quantum lattice schemes resist quantum attacks on paper — but real implementations leak through power and timing, and deep-learning side-channel analysis already pulls keys from hardened lattice code in the lab. How far AI can push that leakage is unmapped.
We are measuring the implementation margin: benchmarking AI-driven side-channel analysis against hardened lattice code — the boundary our own security practice stands behind.