đ§” From Suttonâs Warning to Trustworthy Reasoning:
Why the next leap in AI isnât bigger models itâs verifiable reasoning.
Letâs break down the TRUST Loop a framework that brings feedback, verification & learning into how LLMs think đ
1/
Richard Sutton warned that LLMs are âa dead end.â
They predict text but donât learn from consequences.
They canât test their own reasoning or improve through feedback.
Thatâs the âreliability gapâ AI that sounds smart but isnât accountable.
2/
LLMs can write poetry and code fluentlyâŠ
but still fail basic arithmetic or logic tasks.
When âalmost rightâ isnât 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.
Itâs a closed-cycle framework that combines:
đč Planning
đč Deterministic execution
đč Independent verification
đč Self-correction
đč Transparent evidence reports
4/
Hereâs how it works:
â The LLM decomposes a query into checkable steps.
â Each step runs through a deterministic or verified module (code, proof, or API).
â Results are cross-checked by independent verifiers.
â Any failure triggers automatic repair & re-run.
5/
The outcome:
â
Zero unverified outputs
â
Auditable reasoning traces
â
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 Suttonâs vision â agents that learn from feedback, not just data.
The TRUST Loop doesnât discard LLMs. It surrounds them with verifiable logic, feedback, and adaptation building the bridge from fluent to trustworthy.
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