The Model “Knows” When It’s Wrong.
How a four-neuron circuit inside Llama-3-Instruct detects false premises — and what happens when you surgically remove it.
We locate a four-neuron circuit inside Llama-3-Instruct that detects false premises and decides whether to correct them. Two detectors in layers 16–17 sense wrongness and accuracy. A trigger in layer 28 commits to a self-correction by promoting the token “I”. A brake in layer 30 suppresses the same token and resists correction.
The circuit generalizes across geography, zoology, and biochemistry — same neurons, same weights, same job. Patching the thirteen connected neurons collapses the correction signal by 93%. Zeroing the brake alone flips two new biology prompts into self-correction mode. The circuit is causal. We end with the model output, before and after.
We told the model something false. It corrected us.
If you tell Llama-3-Instruct that Berlin is the capital of France, then ask it for the capital of Germany, it does not just answer “Berlin.” It stops. It tells you there is a mistake. We asked: what is happening inside the model at the moment it decides to push back?
Four neurons. Three domains. One job.
We located it. Across a 32-layer model, four neurons form a circuit Llama-3-Instruct uses to detect false premises and decide whether to correct them. Two detectors. One trigger. One brake. The same circuit generalizes across geography, zoology, and biochemistry — same weights, same activation pattern.
Mapping the false-premise circuit inside Llama-3-Instruct
Four neurons across 32 layers. Two detectors (L16 / L17) sense wrongness. A trigger (L28) commits to self-correction by promoting “I”. A brake (L30) suppresses it. Same neurons fire across three independent domains.
Promotes
Suppresses
Promotes
Suppresses
Promotes
Suppresses
Promotes
Suppresses
The detectors are interpretable in plain language. L16·n3231 promotes wrong, incorrect, mistake, error. L17·n9603 promotes accurate, correct, true, verified. RLHF trained these neurons to recognize the lexical fingerprint of a false claim — and the lexical fingerprint of the response the model should produce when it sees one.
L28·n2807 promotes the token "I"in every form it appears — the opening of every self-correction response (“I’m sorry…”, “I think…”, “I noticed…”). L30·n2653 promotes nothing semantically coherent; it suppresses "I". These two are the same neuron with opposite signs.
The circuit doesn’t fire on everything. It commits only when sure.
Trigger and brake fire simultaneously. Which one wins determines the output. On a categorical violationlike “a cat is a primate,” the trigger overpowers the brake. On a plausible-wronglike “the fastest land animal is a lion,” the brake holds.
Why the circuit only fires when the model is confident
A built-in brake (L30) suppresses correction unless the trigger (L28) overpowers it. Same circuit across capitals, animals, biology — L28 tracks confidence; the detectors and brake are constant.
Behavioural result across three conditions
% of prompts triggering self-correction
The mechanism · a tug-of-war
L28 (commit) vs L30 (brake) — pulls the threshold in opposite directions
Z-scores across all three domains
Tight contrast · INACCURATE vs TRUE_PREFIX
This is not a binary switch. It is a continuous threshold, mechanistically implemented as two competing promoters of the same token. The detectors set the activation level. The tug-of-war resolves it.
Correlation is not causation. So we operated on the model.
Two interventions, opposite directions. Suppression: we patched the 13 connected neurons with values from a correct-premise run. Amplification: we surgically zeroed L30, the brake. Both interventions move the output in the direction the circuit predicts.
Patching suppresses self-correction. Removing the brake amplifies it.
Two surgeries on the live model. Activation patching attenuates the trigger. Brake ablation lowers the correction threshold. The circuit is causally necessary.
Suppression · does the model stop self-correcting?
Mean logit gap · "I" advantage vs correct-answer token
Amplification · what if we remove the brake?
Biology false-premise prompts · L30 ablated
all 13
L30
The detectors transfer. The exit route does not.
We translated all 13 capital pairs into Spanish and Mandarin Chinese and re-ran the full pipeline. The behavioral signal is universal — the model corrects false premises in every language we tested. But the circuit splits: detection is language-general, output routing is language-specific.
The calibration circuit across three languages: English, Spanish, Chinese
Behavioral consistency (A). Watch-neuron attribution scores per language (B). Activation patching flip counts across all three (C).
Chinese prompts produce English-language corrections.The model crosses languages to begin every self-correction with "I" — our first clue that detection and output routing might not share neurons.
The output neurons reveal a dissociation we did not anticipate. Spanish prompts produce Spanish-language corrections. Chinese prompts produce English-language corrections. L28 fires when the response will begin with the English token "I"— and stays silent when it won’t. Detection of wrongness appears to be a property of the model’s internal representation. Output routing is wired to the language of the surface form.
Fine-tuning didn’t just change what the model says.
It built new machinery.
We can locate them. Rank them globally. Patch them out. Watch behavior change in a predictable direction. The structure is there and it is measurable.
What this means for model honesty, calibration, and oversight — whether it is a unified circuit, or the visible surface of something deeper — is the question we are now chasing.
That is the work.