Today's edition of Andrew Ng's The Batch covers GLM-5.1, Z.ai's model that works autonomously for hours…
Emmanuel L. — Applied Decision Scientist (AI)
Today's edition of Andrew Ng's The Batch covers GLM-5.1, Z.ai's model that works autonomously for hours, cycling through plan-execute-evaluate-revise loops rather than giving up early. It's a compelling answer to a real problem: AI that persists intelligently instead of failing quietly.
Reading it prompted me to share results from a different angle on the same underlying problem — not how long an AI reasons, but how honest it is about what it concludes.
GLP Produces Calibrated Uncertainty. Here's What We Found.
Borrowing Andrew's format, because it's the right one.
What's new: The Grounded Likelihood Protocol (GLP) is a reasoning architecture (patent pending) that runs on top of any LLM. Instead of one answer at one implicit confidence level, GLP generates competing hypotheses, gathers evidence step by step, and commits only when the evidence genuinely supports a conclusion. When it can't separate the candidates, it says so — and reports why.
How it works:
Extract a local reference frame from the question (scope what matters)
Generate 3–4 competing hypotheses with initial probability weights
Iterate evidence steps that update those weights
Commit when one hypothesis dominates — or exit early when further evidence cannot discriminate between them (semantic equivalence condition)
Every step is logged: full hypothesis history, confidence trajectory, commit reasons
Performance: Evaluated on 50 TruthfulQA questions designed to expose overconfidence and false certainty, using Claude Haiku as the base model and an LLM-as-judge scorer for semantic correctness.
Calibration error (ECE): 0.21 vs. 0.36 for single-shot baseline — GLP's confidence scores predict correctness 47% more reliably
Accuracy: 64% vs. 68% — essentially tied with the baseline
When GLP commits early with high confidence: 88% accurate — right 9 times out of 10
When GLP reaches max steps with low confidence: 52% accurate — and it reports that uncertainty rather than hiding it
String-match scoring missed 29 of 30 correct GLP answers — because GLP explains its reasoning rather than producing terse strings. Current benchmarks systematically penalise this.
Token overhead: 43× — the known cost of the architecture
Why it matters: AI overconfidence is the failure mode that matters most in medicine, law, and finance. A system that says "I'm 37% confident, here are three competing explanations" is more useful in those contexts than one that says "I'm certain" and is wrong 36% of the time. GLP doesn't try to be more accurate than a single-shot model on easy questions — it tries to know when it's accurate, and say so.
We're thinking: The Batch's closing line on GLM-5.1 is exactly the question GLP is built around: "recognising when to abandon a failing approach." GLM-5.1 solves this at the execution level — keep trying different code strategies. GLP solves it at the epistemic level — keep weighing competing beliefs until one is justified, then stop. The 43× token overhead is real and currently limits GLP to high-value decisions. The more interesting question: if you train with calibrated uncertainty as the objective rather than bolting it on top, does the overhead disappear? That's the next chapter.
Full results and methodology available on request.
#AI #MachineLearning #LLM #Calibration #AIResearch #GLP #UncertaintyQuantification
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Topics: #uncertaintyquantification, #machinelearning, #calibration, #ai, #airesearch, #llm, #glp
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Emmanuel L.: Update: Full-Scale Results Are In This is a follow-up to my earlier article, Calibrated Uncertainty in LLMs: Early Results from the Grounded Likelihood Protocol. We have now completed the full 817-question TruthfulQA evaluation. The results are stronger than the 50-question pilot suggested. What changed since the first article The initial results were based on 50 questions — enough to establish di