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I'm working on (and hold IP around) an architecture pattern for P2P contest and oracle-resolved systems that focuses on deterministic settlement, dispute containment, and exactly-once execution between outcome resolution and payout.<p>The goal is to eliminate:
- replay / double-settlement conditions
- ambiguous resolution states
- arbitration loops caused by partial failures or conflicting outcomes<p>The pattern introduces a reconciliation layer that gates settlement, enforces finality, and holds contested states for resolution before funds move.<p>I'm curious if anyone here has implemented or seen similar patterns in:
- prediction markets
- fintech / escrow platforms
- marketplaces with disputes
- gaming / contest systems<p>Interested in architectural feedback, pitfalls, or pointers to teams working on this class of problem.
We prove that identifying decision-relevant coordinates in a decision problem is coNP-complete. Finding the minimum sufficient coordinate set is also coNP-complete.<p>Formally: given state space S = X_1 × ... × X_n and utility U : A × S → Q, a coordinate set I is sufficient if s_I = s'_I implies Opt(s) = Opt(s'). Checking whether I is sufficient reduces to TAUTOLOGY. Finding minimum I reduces to the same.<p>Main results:<p>SUFFICIENCY-CHECK is coNP-complete
MINIMUM-SUFFICIENT-SET is coNP-complete (Sigma_2^P structure collapses)
ANCHOR-SUFFICIENCY (fixed coordinates) is Sigma_2^P-complete
Dichotomy: polynomial when |minimal set| = O(log |S|), exponential when Omega(n)
Tractable cases: bounded |A|, separable U(a,s) = f(a) + g(s), tree-structured coordinates
Engineering consequence: over-modeling is not laziness. Determining which configuration parameters matter requires solving coNP-complete problems. Including everything costs O(n). Minimizing costs Omega(2^n). For large n, over-specification is optimal.<p>This explains: config files that grow forever, heuristic feature selection (AIC/BIC/CV), absence of "find minimal config" tools. These are not tooling failures. They are optimal responses to intractability.<p>2760 lines of Lean 4 proofs. 106 theorems. Zero sorry.
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I’ve always been amazed by children.<p>They are sponges.<p>Give them something to learn and they learn it quickly.
Too quickly.<p>Psychologists call this memory plasticity.<p>A child can absorb sensory information,
hold it together,
and make sense of it
almost immediately.<p>Learning doesn’t arrive one piece at a time.
It happens in parallel.<p>Many impressions,
held at once,
until patterns begin to stand out on their own.<p>As we grow older, that plasticity fades.
We stop absorbing so easily.<p>We carry more.
But we change less.<p>In 2017, a Google research paper helped ignite the current wave of AI.
Its title was simple:<p>All You Need Is Attention.<p>The idea was not to hand-build understanding.
Not to carefully specify every connection in advance.<p>Instead:
turn experience into tokens,
examine their relationships all at once,
and let structure emerge.<p>Up to that point, much of AI had tried to design intelligence explicitly.
Representations.
Connections.
Rules.<p>It worked.
But slowly.
At enormous cost.<p>The new proposal was different.
Just throw everything at it.
Let the system figure it out.<p>In other words:
teach the system the way a baby learns.<p>But the environments are not the same.<p>Children learn by being immersed in the world.
Large language models learn by being immersed in the internet.<p>One of these environments contains playgrounds,
stories,
and banged knees.<p>The other contains comment sections.
At scale.<p>And then there is a hard boundary.<p>At some point, the learning must stop.<p>The figuring-out is frozen into place—
for better or worse—
so the system can be used.<p>An LLM may have learned a great deal.
But it has learned only what was present in its training.<p>This is what developers mean when they say a model is stateless.<p>It does not progress.
It does not accumulate.<p>It resets.<p>Each time you use it,
you are meeting the same frozen system again.<p>It may be intelligent.
But it cannot learn more than it already knows—
except for what you place in the prompt.<p>And when the session ends,
that too disappears.<p>This has become a quiet frustration for many users.<p>Because the question isn’t whether these systems are intelligent.<p>It’s whether intelligence
without the ability to change
is learning at all.<p>---<p>Also on Medium: https://medium.com/@roger_gale/where-mistakes-go-to-learn-51a82a6f1187<p>If you enjoyed this, I'm writing a series on AI limitations and learning.