How Replication Could Teach Machines What Good Science Looks Like — Edward Hughes

Can a machine learn the judgement that separates a plausible-looking result from a faithful experiment? Edward Hughes, Chief Scientist and co-founder of Inherent, joins Tim Scarfe to argue that creativity is not optimisation, and that the missing capability in AI is choosing which questions are worth asking.

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Edward makes the case that Move 37 was innovative rather than creative, and that the field, not the individual, decides what counts as a discovery. That reframing runs through Csikszentmihalyi, Deutsch and exaptation into open-endedness, where deceptive goals and imperfect world models turn out to be the point rather than the problem. The second half turns to the paper: Replica, a task space built by redacting figures from real papers, and Faraday, a 27-billion-parameter model trained to steer a frontier coding agent that then beats the frontier on held-out replications.

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TIMESTAMPS:
00:00:00 Cold open: Move 37, Faraday and collective intelligence
00:01:08 Sponsor: CyberFund
00:01:46 Inherent's $50M raise and the road from string theory
00:09:14 Three timescales of learning: weights, context, culture
00:13:47 Move 37 was innovative, not creative: the field decides
00:20:39 Creativity as satisficing: the urinal and evolution
00:25:06 Exaptation and the Tristan chord: creativity in context
00:30:56 Coherence for whom? Deutsch's hard-to-vary explanations
00:35:53 Why copying is creative: Deutsch and the constraint engineer
00:42:27 Societies of agents and the strong Moravec paradox
00:45:51 Evaluate in hindsight: from Lean proofs to climate change
00:51:56 Picbreeder, local goals and why discovery needs deception
00:57:21 Spaghetti proofs, translation layers and superhuman Go
01:00:37 Does nature compress? Naturalness and real patterns
01:07:36 Why replicate? Replica's redacted figures and Faraday
01:12:31 Faraday beats Codex, Claude and GLM 5.2 on held-out tasks
01:15:31 Replication to innovation: how the Transformer happened
01:18:26 Deep replication: what Faraday learns from Voyager and GNoME
01:23:37 Can the AI scientist cheat? Goodharting the judge
01:29:09 Inside Replica: scale-down, 8xB300 runs, per-task rubrics
01:34:11 The RL crisis: getting GRPO to work with per-turn credit
01:39:43 Weights vs harnesses: AlphaEvolve, DGM and EvoTune
01:45:45 The recursive company: agents cross a phase transition
01:50:35 Collective intelligence and the electric dynamo
01:55:46 What replaces OKRs? Incumbents and the burden of knowledge

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REFERENCES:
MLST Creativity Article:
archive.mlst.ai/read/why-creativity-cannot-be-…

organization:
[00:01:47] Inherent
inherentlabs.ai
other:
[00:20:51] Marcel Duchamp, Fountain
tate.org.uk/art/artworks/duchamp-fountain-t07573
[00:05:19] Human-Timescale Adaptation in an Open-Ended Task Space (Adaptive Agent)
arxiv.org/abs/2301.07608
[00:06:05] The AI Scientist
arxiv.org/abs/2408.06292
[00:12:13] Training AI Scientists to Replicate Research (Replica and Faraday)
arxiv.org/abs/2608.13331
[01:44:46] Evolutionary Principles in Self-Referential Learning
people.idsia.ch/~juergen/diploma.html
[01:59:33] Are Ideas Getting Harder to Find?
nber.org/papers/w23782
book:
[00:16:04] Creativity: Flow
search.worldcat.org/title/254487436
[00:26:22] Why Greatness Cannot Be Planned
link.springer.com/book/10.1007/978-3-319-15524-1
[00:33:03] The Beginning of Infinity
penguinrandomhouse.com/books/293575/the-beginn…
[01:55:47] Laws of Knowledge
penguin.co.nz/books/the-infinite-alphabet-9780…

(Full list refs on YT/rescript)
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RESCRIPT:
app.rescript.info/session/670296ba913761d0?sha…