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I&#x27;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 &#x2F; 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&#x27;m curious if anyone here has implemented or seen similar patterns in: - prediction markets - fintech &#x2F; escrow platforms - marketplaces with disputes - gaming &#x2F; 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&#x27;_I implies Opt(s) = Opt(s&#x27;). 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&#x2F;BIC&#x2F;CV), absence of &quot;find minimal config&quot; 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:&#x2F;&#x2F;medium.com&#x2F;@roger_gale&#x2F;where-mistakes-go-to-learn-51a82a6f1187<p>If you enjoyed this, I&#x27;m writing a series on AI limitations and learning.