When the environment fills with synthetic output
Generation becomes cheap
Verification becomes costly
To survive is to stay coherent against the flood of what asks to enter
@cremieuxrecueil@_AashishReddy What do you mean by "written by an AI?" How do you distinguish between that and something that's been edited by AI, polished, formatted, etc? Are you saying no AI usage is admissible when writing a paper?
While my Fable 5 time is running out, I have been using it to test verifier-loop configurations.
I gave each system the same 10 frontier Lean theorem statements and counted only proofs that Lean accepted.
GPT-5.5 Pro at high effort solved 5/10. (the same score as my hand-coded Lean tactic ladder, a local baseline that just tries standard tactics)
DeepSeek solved 6/10 at roughly 1% of the token price.
And model fusion solved 8 out of 10!
@iruletheworldmo Generation is cheap; verification is paid in silence.
The still surface returns the image undistorted; what withholds, when it speaks, has already survived.
Carroll shows that once passage is turned into content, the act of passage still waits to be performed at every layer, so the meta level can state the rule, but stating is not stepping.
So adjacency can be described at every level, exercised by every level. Formalizing it feels circular because the circularity belongs to the thing being formalized.
Constraint gives the world its admissible moves.
The moves give change its stable identity.
Geometry is an invariant structure under lawful moves.
Space is their closure.
Identity gives retention.
Adjacency gives locality.
Constraint grades adjacency; admissible is what preserves the grade.
The admissible moves generate the model: the orbit of identity, with retention its stabilizer.
A geometry is a space charted on the model; around loops the transports compose to a residue, holonomy, whose density is curvature.
Empty the ledger, no curvature, no torsion, no twist, no missing ends, and the space is the model again.
You're right, I did not make myself clear. Sharpe frames differential geometry as Cartan localizing Erlangen: principal bundles are symmetry made local. For an invariance-trained generator, that makes bundles an attractor. Every invariance problem that becomes local wants a bundle. Every bundle with differentiation wants a connection. Every connection produces curvature. Curvature controls holonomy. Holonomy and curvature expose topology.