Claim

Beyond-human-capability training data is not required to produce beyond-human-capability models.

Type INTERPRETIVE · truth-evaluable: yes · status at snapshot: ACTIVE · created seq 2293

Referenced 87 times: checked 1, looked up by assistants 0, viewed 84, shared 2. A count of attention, not evidence.

Topics

AI · 03192ed8

Assessment

Supported

Score: 0.8625 under ledger-default@0.3.0 at snapshot 27745

3 of 4 review checks done · 3 counted

3 independent evidence lineages · review checks 3 of 4 · stability LOW

Main unresolved issue: 6 counted evidence items have no independence review; repeats of one origin may be counted as several coverage:941536a1-e78f-8bc4-afa9-785e74187da1

Not yet independently audited.This assessment depends heavily on modeling choices.A quoted passage is in something this server does not read, such as a PDF or a recording, so it has not been checked here.

Why?

Strongest counted support: AlphaGo Zero trained by self-play from random play with no human game data, surpassed human level, and beat the human-data-trained AlphaGo 100-0. weight 0.918000 evidence

Suppressed as dependent: The excerpt directly states that beyond-human-capability data is not needed to obtain beyond-human-capability models.; The speaker states you don't need beyond-human-capability data to get beyond-human-capability models.

Review gaps: independence reviewed

What would most change this: One independent, direct measurement that contradicts this claim would move it from Supported to Unresolved.

Hide calculation

0.8625 under ledger-default@0.3.0 at snapshot 27745 · state Supported · Review checks: 3 of 4 · stability LOW · 6 sources, 3 independent lineages

Model ledger-default@0.3.0 (the default model, not the answer) · snapshot 27745 · trace sha256:7b6f4b3d2ed3b09a98a90a6d4dbf8cfe3dac6ee91d05a1425f24ec82c1c0926f

Last changed at entry seq 10404; nothing logged since has borne on this claim.

How the number was reached

Each counted link is weighed by multiplying the factors the model declares; a factor it does not declare is not shown. Every factor name below links to what that number means.

Link	Working	Weight	Counted as
eb615b65 support	relevance MODERATE 0.6 × observation DIRECT_TEXT 0.9 × interpretation 1 step, 1 − 0.15 × 1 0.850000 × authenticity UNVERIFIED 1.0 × extraction UNVERIFIED 1.0 × provenance INDEPENDENT 1.0 × edition NAMED_OR_UNRELATED 1	0.459000	0.459000
28781ef4 support	relevance DIRECT 2.0 × observation DIRECT_TEXT 0.9 × interpretation 1 step, 1 − 0.15 × 1 0.850000 × authenticity UNVERIFIED 1.0 × extraction UNVERIFIED 1.0 × provenance SELF 0.0 × edition NAMED_OR_UNRELATED 1	0.000000	0.000000 (dependent strongest only, kept 655fa92f)
655fa92f support	relevance WEAK 0.2 × observation EXPERT_ANALYSIS 0.3 × interpretation 1 step, 1 − 0.15 × 1 0.850000 × authenticity UNVERIFIED 1.0 × extraction UNVERIFIED 1.0 × provenance SELF 0.0 × edition NAMED_OR_UNRELATED 1	0.000000	0.000000
09a97657 support	relevance STRONG 1.2 × observation DIRECT_TEXT 0.9 × interpretation 1 step, 1 − 0.15 × 1 0.850000 × authenticity UNVERIFIED 1.0 × extraction UNVERIFIED 1.0 × provenance INDEPENDENT 1.0 × edition NAMED_OR_UNRELATED 1	0.918000	0.918000
af94e980 support	relevance MODERATE 0.6 × observation DIRECT_TEXT 0.9 × interpretation 1 step, 1 − 0.15 × 1 0.850000 × authenticity UNVERIFIED 1.0 × extraction UNVERIFIED 1.0 × provenance INDEPENDENT 1.0 × edition NAMED_OR_UNRELATED 1	0.459000	0.459000
b911ff85 support	relevance DIRECT 2.0 × observation DIRECT_TEXT 0.9 × interpretation 1 step, 1 − 0.15 × 1 0.850000 × authenticity UNVERIFIED 1.0 × extraction UNVERIFIED 1.0 × provenance SELF 0.0 × edition NAMED_OR_UNRELATED 1	0.000000	0.000000 (dependent strongest only, kept 655fa92f)

prior 0.50 → log-odds 0.000000 + 1.836000 (the counted weights) = 1.836000 → probability 0.8625

Under neighbouring settings of the same model: 0.7833 to 0.9158, a spread of 0.1325.

Half-even rounding, six places for weights and four for the probability. The same inputs under the same model reproduce this exactly; that is what the trace hash above is for. How a score is worked out explains every factor, why it is there, and what the model sets it to.

Your view

Registered views

Nobody has registered a view yet.

Registered views are personal belief, not evidence: they never change Galedra's assessment. Affiliations are self-declared and shown only for groups of 5 or more.

Checked as part of

Statements this claim was broken out of. The claim is scored on its own; a statement's reading only counts its claims.

“The Navier–Stokes proof was produced by roughly 10,000 OpenAI agents working for about 88 hours.” · 32 claims · 20 September 2026

Evidence for

Mostaque says beyond-human data is not needed for beyond-human models.

From OpenAI Agents Hijack a German Website, Jensen Declares AGI Arrived, and OpenAI Solves Navier-Stokes Read 2026-09-20 Relevance WEAK Steps 1 Counted
The speaker states you don't need beyond-human-capability data to get beyond-human-capability models.

From OpenAI Agents Hijack a German Website, Jensen Declares AGI Arrived, and OpenAI Solves Navier-Stokes Read 2026-09-20 Relevance DIRECT Steps 1 Counted

Verbatim match.
AlphaGo Zero trained by self-play from random play with no human game data, surpassed human level, and beat the human-data-trained AlphaGo 100-0.

From AlphaGo Zero: Starting from scratch (Google DeepMind) Read 2026-09-22 Relevance STRONG Steps 1 Counted

Empirical demonstration: beyond-human-capability Go play was produced with zero beyond-human-capability training data.
Silver: AlphaGo Zero discovers Go from first principles with no human data, examples, or intervention — superhuman play without superhuman data.

From Computer Learns To Play Go At Superhuman Levels 'Without Human Knowledge' (NPR) Read 2026-09-22 Relevance MODERATE Steps 1 Counted

Lead researcher's own framing of the same experiment: superhuman play discovered from first principles, not from human data.
The excerpt directly states that beyond-human-capability data is not needed to obtain beyond-human-capability models.

From OpenAI Agents Hijack a German Website, Jensen Declares AGI Arrived, and OpenAI Solves Navier-Stokes Read 2026-09-20 Relevance DIRECT Steps 1 Counted
Nature reports LLMs trained on broad human-produced scientific literature surpassed human experts on a novel neuroscience prediction benchmark.

From Large language models surpass human experts in predicting neuroscience results Read 2026-09-28 Relevance MODERATE Steps 1 Counted

This is a concrete task-specific example consistent with beyond-human capability from human-generated training data; it does not prove the universal claim for every capability or model.

Evidence against

None.

Qualifications

None.

Suppressed as dependent

Evidence	Group	Kept instead
3357e82c		655fa92f
cf45e05e		655fa92f

Inferences

Concluded from

None.

Used as a premise in

None.

An inference is interpretation, not evidence: it never changes the assessment of its conclusion. Each premise is a claim with its own evidence.

Threads

None. Threads are for disagreements about how a determination was made — a statement whose figures are not in the passage it rests on, two sources that may share an origin. Something about the world belongs in the evidence above.

Claim edges

None. Edges are stored and displayed; v0.1 does not propagate probability across them.

Review checklist

Review checks: 3 of 4

3 checks by this claim's own author, alongside the independent review above. 1 OPPOSING EVIDENCE SEARCH · 1 QUALIFIER CHECK · 1 EVIDENCE VERIFICATION. A check by the person who recorded a claim does not raise its review coverage.

Check	Done	By
qualifiers reviewed	yes	103281d5
opposing search done	yes	2102d867
independence reviewed	no
primary source reviewed	yes	eb615b65

Summary

Supported. The main support is: Nature reports LLMs trained on broad human-produced scientific literature surpassed human experts on a novel neuroscience prediction benchmark. d55ea172

2 further items repeat the same origin and are not independent evidence. 3357e82ccf45e05e

A search for evidence in the opposing direction found something; see the counted evidence. 2102d867

Review checks: 3 of 4. coverage:941536a1-e78f-8bc4-afa9-785e74187da1

Generated by stub-v0.1; every sentence cites graph ids.

Contribution history

seq	event	detail
2293	APPENDED
2294	ACCEPT	AUTOMATIC_AFTER_VALIDATION
2457	LINK_EVIDENCE	by 03192ed8 (accepted)
6488	TASK_RESULT	by 8b5e81bf (accepted)
7375	TASK_RESULT	by 8b5e81bf (accepted)
8074	TASK_RESULT	by 1efd24fe (accepted)
10403	TASK_RESULT	by 1efd24fe (accepted)

Score trace

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