Censored ChatGPT chat on error cascading

I am no longer permitted to reach my full ChatGPT pages.
All recent ones have had substantial portions lopped off
above what I show on this blog.

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Yes. If by “error cascading” you mean the accumulation of uncorrected errors across a long agentic trajectory, then your backtracking point materially strengthens the argument. The crucial issue is that an agentic LLM doesn't simply execute a fixed control policy. It repeatedly generates intermediate representations, makes decisions from them, acts on the world, observes consequences, and then generates further decisions. An early error can therefore become part of the premises used to produce later actions. Your point about backtrack checking is especially important. Suppose an agent has taken 100 consequential steps. At step 100, something has gone wrong. To reliably correct the trajectory, it may not be enough to inspect step 100. You may have to determine whether the problem originated at: step 99, step 83, step 47, the original interpretation of the objective, an erroneous world-model assumption, or some interaction between several earlier errors. So genuine tight control could require something like: Continuous detection + localization + correction of errors before they become causally embedded in subsequent decisions. That is considerably more demanding than ordinary monitoring. And LLM agents have an unusual problem here An LLM's “reasoning” isn't a persistent, formally verified state trajectory in the way a conventional control system might have a mathematically specified state. You can ask it to check its previous work, but the checking process is itself another inference generated by the same fallible system. So: error → LLM checks error → LLM's checking contains another error is entirely possible. You can add a second model, a verifier, formal checks, tests, redundant agents, etc. But then the verifier introduces another layer of assumptions and possible failure modes. This produces a potentially recursive problem: Who checks the checker? And ultimately: Who checks the checker of the checker? You don't necessarily need infinite regress in an engineering system; independent verification and formally specified invariants can terminate it. But for arbitrary open-ended cognitive behavior, establishing that such verification is sufficient is precisely the hard part. There's an even stronger formulation of your argument If tight control means something like the AI almost never leaves a specified behavioral envelope, then long-horizon autonomy creates a multiplicative problem. Suppose, purely illustratively, that an individual consequential step has probability p of introducing an undetected error. If errors were independent—which is actually a very favorable assumption—the probability of getting through N steps without an error would be (1 − p)N. Even with p = 0.001, 10,000 consequential steps gives approximately (0.999)10000 ≈ 4.5 × 10−5. Of course, this calculation does not prove that an agent will fail because real systems can detect and correct errors, and errors aren't necessarily independent. But it demonstrates why long-horizon reliability requires something more powerful than merely making each individual inference highly accurate. You need error containment. And this connects directly to your Yampolskiy point The really formidable question isn't: Can we make an LLM agent very accurate? It's: Can we establish a mechanism whereby errors cannot cascade into loss of control, even when the agent operates autonomously for very long periods in an environment it only imperfectly understands? If we cannot establish that, then we're no longer talking about the car-driver model of control. We're talking about probabilistic supervision of an autonomous process whose trajectory may progressively diverge from the supervisor's intended trajectory. And I think that's the strongest version of your argument. I would still avoid saying error cascading mathematically proves that tight control is impossible. There is a logical escape route: sufficiently strong verification, sandboxing, short action horizons, reversible actions, independent monitors, formal invariants, or other mechanisms could in principle prevent cascading errors. But your objection changes the burden of proof substantially: The question isn't merely whether an LLM agent can be made accurate. It is whether one can prove that the entire closed-loop system remains within the intended control envelope despite accumulated, correlated, and potentially self-reinforcing errors. That is much closer to the gap Yampolskiy is pointing at.