One more pass. That's the entire logic of a repair loop: whatever failed, better context and another attempt will eventually fix it. For most failures, that logic works — which is exactly what makes it a trap for the failures it doesn't. A recent complexity-theoretic result proves a specific class of task cannot be solved or verified within a bounded forward pass, no matter how many passes are chained together. A retry budget spent chasing that class buys nothing back — not a partial refund, nothing. Closing out Theorems Out of Warranty.
A single word — passed — sounds like a verdict. It's actually doing the work of several different claims at once: checked and confirmed true, checked and confirmed false, and never checked at all because no method existed to check it, quietly rounded up to a pass anyway. Treating that boolean as if it always means the same thing is the boolean fallacy, and Rice's theorem gives the honest reason no execution-based check can fully cure it. Still inside Theorems Out of Warranty.
A judge should catch what a writer misses — that's the whole point of a second opinion. But if the judge finds an argument convincing mainly because it sounds like something the judge itself would write, the second opinion isn't independent, it's a compliment. Auditors from different adapter families converged on the same verdict for a proposal neither of them actually executed — full agreement, and not one shred of independent confirmation in it. Continuing Theorems Out of Warranty.
More voters should mean more truth — that's the promise behind the jury theorem, Byzantine fault tolerance, and the scalability law alike. All three need the voters to disagree, when they're wrong, for genuinely different reasons. Stochastic agents pulled from overlapping training data don't fail that way: they agree, confidently, for the same reason, and a headcount can't tell independent judgment from an echo. Opening Theorems Out of Warranty.
Eight agents complete a benchmark worse than four, at 2x the token cost. The equation that predicts this was written in 1993 for parallel databases — and it governs CPU caches, engineering teams, and AI swarms with identical math. This post proves it at all three layers, then hands you the instrument: given your measured alpha, kappa, and role error weights, compute the topology before you spawn the first agent.
AI expands the achievable region on new axes — accuracy, explainability, privacy — and automates navigation along them. It does not escape the frontier. Compression moves along the accuracy/latency trade-off; it does not dissolve it. A multi-objective RL navigator learns to find Pareto-optimal operating points; it does not create them. The stochastic tax prices what learning costs: fidelity gap between model and explanation, exploration budget spent acquiring policy knowledge, privacy budget that degrades accuracy under formal data-use constraints. All three stack on top of the physics and logical taxes already owed.
How to engineer resilient decision-making in multi-agent AI systems. Explores weighted voting, robust aggregation, and governance architectures with mathematical frameworks and practical implementation ideas.
How engineers can develop frameworks for decision-making that become stronger when LLM systems fail, building cognitive resilience through adversarial thinking and dynamic trust calibration.