evidence over assertion

Lemma

the AI scientist that distrusts itself

Machines are beginning to do science faster than anyone can check it. Lemma is the missing layer — a lab that turns claims into inspectable verdicts, keeps every failure, and distrusts itself first.

Recorded evidence · failures included

45 bars · one per extracted claim across 9 papers — click to play

01

the mission

Science is about to be written at machine speed — and nobody can verify that fast. Lemma's job is the missing layer: make checking a claim as cheap as making one, so every result in the literature can carry a verdict you can inspect, with the evidence attached.

02

the approach

Not a reviewer — an instrument. The lab extracts testable claims, writes and runs its own controlled numerical experiments, and snapshots every attempt immutably. A failed control readsinconclusive, never falsified — and an independent judge scores the trail itself.

03

what it found

Pointed at the high-dimensional regression literature, the lab audited 21 claims across three papers — and two published claims failed their own controlled tests. Between them, new science: an agent-generated hypothesis verified in seconds, and a measurement no paper reported — the asymptotic risk approximation is already within ~0.1% at d = 80.

0 claims · 0 papers
04

the receipts

9 papers, 59 claims, and a trail you can reconstruct line by line — including everything that failed.

0papers
0claims
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0failures preserved
0wall minutes
0model calls
0tool runs
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the cost of honesty — every number was spent to find out, not to prove.

05

the vision

One literature is a start — not the destination. The goal is a verification map of science itself: every claim tracking its evidence, every failure preserved, every uncertainty stated in the open. Discovery accelerates the moment trust becomes cheap. That is the lab we are building.

the wider landscape

everyone is generating.
nobody is checking.

Science runs on claims, but claims are cheap. In a 2016 Nature survey of 1,576 researchers, 70% failed to reproduce another lab's experiment — and more thanhalf failed to reproduce their own.

Now the generators have arrived: AI scientists that draft papers and hypotheses faster than any human can read them. The bottleneck is no longer production — it is trust. Who checks the claims before they become literature?

papers & hypotheses, at machine speed

Sakana · Google co-scientist · agentic writing pipelines

what it produces
novel papers, hypotheses, method write-ups
who checks the checker
a human — later, if at all. Nothing re-runs the claim before it ships.

Lemma is not another generator. It is the audit layer the new wave forgot — and it distrusts itself by design.

how an audit works

one paper, four stages,
zero skipped failures

Success criteria come from the claims themselves — nothing is back-filled after the fact.

claims.json · Grokking phase transitions in learning local rules with gradient descent
[claim] C1 — Critical exponent in 1D exponential model[claim] C2 — Critical exponent in D-dimensional uniform ball model[claim] C3 — Grokking probability increases with L1 regularisation[claim] C4 — Grokking probability decreases with dimension D

6 claims extracted · untestable ones declared, never silently skipped

Each stage re-runs idempotently — the trace records every attempt, including the failures.

The trail is the product

Every LLM call, every script, every rejection lands in an append-only trace. Scroll — the audit writes itself.

trace.jsonl — append-only
[director] triage · arxiv 1912.07242 — sample-wise double descent[extract] 6 claims → C1…C6 · success criteria taken from the claims themselves[audit] C2 · timeout ×3, crash ×1 — every failed attempt kept on the record[audit] C2 · falsified — positive control passed, so the verdict is trusted[human] feedback.md · the claims are dimension-agnostic — bound d ≤ 300[audit] re-audit · C1 + C2 supported in 4.5 min — 14× faster than round one[propose] proposer agent · the risk valley at γ* = 0.90 — promoted to claim C7[audit] C7 · agent-generated · supported in 14 s · control green[audit] C8 · independent E[β̂] = γβ check falsified at γ = 0.1 — invisible to the circular test[audit] C9 · error at the noise floor, control failed → inconclusive, recorded as such[corpus] sweep · hastie et al. 1903.08560 · bartlett et al. 1906.11300[audit] bartlett C1 + C6 · falsified — published claims failing controlled tests[map] verification map · 21 claims · 3 papers · 7 supported / 4 falsified / 3 inconclusive[judge] evidence trail PASS 5/5 · every failure preserved[note] inconclusive is a result — a patched false-positive is not

extract → audit → evidence → judge · verdict validator · positive controls · reviewer-reference escalation

Replay an audit, line by line

The real append-only traces from 9 completed runs — every model call, every script, every rejection and verdict flip. Scrub it, play it, watch a claim go from falsified to supported across rounds.

·
trace.jsonl — replaying live run
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Want this on your paper?request an audit ↓

Audit a new paper

New-paper audits run on the local CLI — this form is a request line, not a hosted run. The recorded evidence is already live:enter the Audit Room, or run it yourself:

./lemma audit <arxiv-id> --jobs 2

Leave your email to request an audit or get notified when a hosted version opens — tell us which paper you'd check first.

★ — on GitHubno backend of our own yet — stars and signups are the queue

Everything is public

the verification ledger — every paper, every claim, every failure on record

supported falsified inconclusive not yet tested— inconclusive is a result, not a failure to report one