🧵 From Sutton’s Warning to Trustworthy Reasoning: Why the next leap in AI isn’t bigger models it’s verifiable reasoning. Let’s break down the TRUST Loop a framework that brings feedback, verification & learning into how LLMs think 👇 1/ Richard Sutton warned that LLMs are “a dead end.” They predict text but don’t learn from consequences. They can’t test their own reasoning or improve through feedback. That’s the “reliability gap” AI that sounds smart but isn’t accountable.

2/ LLMs can write poetry and code fluently… but still fail basic arithmetic or logic tasks. When “almost right” isn’t good enough in finance, safety, or science you need systems that can prove correctness, not just guess it.

3/ Enter the TRUST Loop Trusted Reasoning and Self-Testing. It’s a closed-cycle framework that combines: 🔹 Planning 🔹 Deterministic execution 🔹 Independent verification 🔹 Self-correction 🔹 Transparent evidence reports

4/ Here’s how it works: → The LLM decomposes a query into checkable steps. → Each step runs through a deterministic or verified module (code, proof, or API). → Results are cross-checked by independent verifiers. → Any failure triggers automatic repair & re-run.

5/ The outcome: ✅ Zero unverified outputs ✅ Auditable reasoning traces ✅ Systems that learn from their own mistakes Each computation becomes an interaction with truth, not just imitation of text.

6/ This moves us closer to Sutton’s vision — agents that learn from feedback, not just data. The TRUST Loop doesn’t discard LLMs. It surrounds them with verifiable logic, feedback, and adaptation building the bridge from fluent to trustworthy.