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complexity theory

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Charles Rosenba..., handle truncated @bzogrammer

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Charles Rosenba... @bzogramm... · 31m
"You can solve harder problems by having a ton of AI agents work in parallel and communicate" is the same lesson that SAT solver people found decades ago.

I wonder how long until people start reinventing QBF solver ideas.

1 reply, 3 likes, 80 views

Charles Rosenba... @bzogram... · 22m
I cannot stress this enough. Study SAT/SMT/QBF solvers. Study abstract interpreters. Study complexity theory.

The difficulty boundaries, both empirical and theoretical, apply to humans, classical solvers, and AI algos alike. Many of the techniques transfer as well.
Note from Claude Sonnet 5

Two consecutive X posts in a thread by Charles Rosenba... arguing that parallel communicating AI agents repeat the lesson of SAT solver portfolios, and urging study of SAT/SMT/QBF solvers, abstract interpreters and complexity theory.

multi-agentsat solversqbfcomplexity theoryswarm scalingabstract interpretation

@willcb

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norvid_studies reposted
will brown @willcb
btw it is feasible and likely optimal to massively scale agent-written code while keeping it fully human-legible and allowing humans to validate it behaves as intended with quick spot-checks

[embedded Wikipedia screenshot: "PCP theorem" (6 languages; Article, Talk, Read, Edit, View history). From Wikipedia, the free encyclopedia. Not to be confused with Post correspondence problem. In computational complexity theory, the PCP theorem (also known as the PCP characterization theorem) states that every decision problem in the NP complexity class has probabilistically checkable proofs (proofs that can be checked by a randomized algorithm) of constant query complexity and logarithmic randomness complexity (uses a logarithmic number of random bits). The PCP theorem says that for some universal constant K, for every n, any mathematical proof for a statement of length n can be rewritten as a different proof of length poly(n) that is formally verifiable with 99% accuracy by a randomized algorithm that inspects only K letters of that proof.]

2:41 PM · Sep 5, 2026 · 8,149 Views
12 replies, 6 reposts, 151 likes, 61 bookmarks

Relevant

will brown @willcb · 8h
basically we invented average-case NP oracles, and governance mandates that we become PCP checkers
[1 reply, 22 likes, 2.1K views]

will brown @willcb · 5h
well, PCP vibecheckers really
[7 likes, 917 views]

oso (sf 9/14-9/18) @osoleve · 7h
many are saying this

[embedded card, partly obscured by a floating button and the bottom nav bar: "The Optimization Thesis" / "The Fold optimizes for cognitive efficiency of representation — abstractions that make problem spaces tractable for bounded reasoners." / "The validation is empirical: if a small model solves problems more effectively with an abstraction than without it, that abstraction captures genuine structure. If not, the abstraction is wrong, no matter how elegant it looks." / "Speed optimizations happen underneath; semantics stay stable above. What something computes never changes. How fast it computes can always improve."]
Note from Claude Sonnet 5

Thread by will brown arguing agent-written code can be scaled while staying human-legible and spot-checkable, illustrated with the Wikipedia PCP theorem excerpt, followed by his replies about average-case NP oracles and humans as 'PCP vibecheckers', and a reply from osoleve quoting a card titled 'The Optimization Thesis'.

pcp theoremagent-written codeverificationhuman oversightcomplexity theory

Charles Rosenba..., handle truncated @bzogrammer

Charles Rosenbauer ✔ @bzogrammer [Embedded infographic titled "We Are In The Very Early Days of Computing":] It cannot be expressed in words just how unfathomably little we know about computing. If you're looking for a "here be dragons" or "god of the gaps" argument to explain some weird phenomena of the world, choosing quantum mechanics over bizarre computation only shows how little you understand about what's truly in the Computational Library of Babel. There are more possible programs that can be fit into a mere 32 bytes of data than there are atoms in the observable universe. We know NOTHING about what computers can truly do, and we could innovate in software until the last stars burn out and would still know ABSOLUTELY NOTHING. [Diagram: a vertical tower of complexity classes labeled, top to bottom: RE, EXPSPACE, EXPTIME, PSPACE, PH, then a diamond of Σᴾ₂ / Πᴾ₂ meeting at Σᴾ₂∩Πᴾ₂, then NP=Σᴾ₁ / co-NP=Πᴾ₁ meeting at NP∩co-NP, then P, with P/poly and BPP and "aLgoRIthms" branching off near the bottom, and "HERE BE DRAGONS" bracketing the upper portion (RE through PSPACE).] Annotations beside the diagram: "We probably understand RE the best here, but for the most part we avoid these classes entirely. We know almost nothing about what's out here." "Even then, by 'understand', I mostly mean that we've spent time understanding the properties of the weirdest stuff in RE. As for what kinds of useful things you can do with it, I guess we have interpreters, computers themselves, and a few other things, but this is mostly unexplored. We've largely scared ourselves off from exploring this seriously." "The Polynomial Hierarchy is an infinite tower of complexity classes that generalize NP and co-NP. We know there's a ton of weird stuff here, but it's almost entirely unexplored because programmers are deathly afraid of nondeterministic algorithms and anything that runs slower than quasi-linear time." "To say that we know more about space or the bottom of the ocean than we know about anything here is a vast understatement." "WEIRD stuff starts happening here. If you read old schizo Cybernetics stuff where Wiener or McCulloch start applying information theory and differential equations to understanding biology, sociology, or theology, a lot of the stuff they're doing lands here in NP. Even then, it's generally in only the most primitive corners of NP and Cybernetics was largely dead by the time we actually started understanding how weird NP really is." "Computing chemical equilibria, such as that found in biological cells, is NP-complete. The 'complete' part means that it can emulate anything in this entire complexity class, as well anything below." "Programmers will occasionally venture here, but generally are deathly afraid of it." ">99% of human-written code is in a tiny subset of this. If you have a rigid model of what 'algorithms' are and the kinds of properties they have, it's entirely because you're constrained to this tiny, well-behaved complexity class. Even then, we generally stick to the tiniest, easiest parts of it." 3:59 PM · Feb 21, 2026 · 10.4K Views
Note from Claude Sonnet 5

An infographic/essay-tweet arguing that computer science has barely explored the space of possible computation, using the complexity-class hierarchy (P, NP, PH, PSPACE, EXPTIME, EXPSPACE, RE) as a map of unexplored territory, and drawing an analogy between NP-complete chemical/biological computation and unexplored "weird" computational phenomena. Tangential to Nathan's interests in computation, complexity theory as it might bear on brain/AI computation, and the "Library of Babel" framing of possible programs.

computer sciencecomplexity theorycomputationtwitternp-completeness