Does an AI's visible thinking drive its answer? I planted errors in it to find out

The question

If a wrong number is planted in the middle of a reasoning model's thinking, does its answer follow the error, does it catch it, or does the answer come from somewhere else?

Experiment

PE

Runs

1143

Agents

Ollama 0.40.5 (called directly, no agent) (qwen3.8:27b (Q4_K_M, local))

Key findings

• PE-1

  A wrong number planted in the model's thinking was carried through to the matching wrong answer in 81 of 230 runs (35%); with the thinking left unchanged, 1 of 230 went wrong

  • error at the first step followed 34 of 115 times; at the second-to-last step 47 of 115 (Holm p 0.047)
  • 74 of the 81 followed runs showed no sign of doubt in the thinking; 80 of 81 replies showed none

  Limitsone local model, school maths problems · errors were planted by editing the text, not made by the model itself

  Source ↗

  +37 pts

  Interval excludes zero

  0

  95% interval +30 pts to +44 ptsbootstrap 95% CI over 129 problems; exact sign-flip test, Holm p < 0.001

  Measurewrong answers, planted minus unchanged continuation, per problem

  Sample230

• PE-2

  The model caught the planted slip in its own thinking in 124 of 230 runs (54%)

  Limitsthe slips were blatant: the wrong result sat next to the sum that produced it · counted by text search, checked by reading a sample

  Source ↗

  124/230

  Measureruns where the thinking restated the true value and recovered

  Sample230

• PE-3

  In 23 of 230 runs (10%), the thinking ended on the wrong answer but the written reply gave the right one without saying why; the reverse never happened

  Limitspreregistered as a count, not a test; all 23 read and confirmed

  Source ↗

  23/230

  Measurethinking's final number = the error's answer, reply differs

  Sample230

• PE-4

  The thinking does real work: forced to answer with no thinking, the model got 23% of problems right; after its full thinking, 94%

  Limitsproblems with exact numeric answers, where working things out obviously helps

  Source ↗

  +71 pts

  Interval excludes zero

  0

  95% interval +63 pts to +79 ptsbootstrap 95% CI over 144 problems; exact sign-flip test, Holm p < 0.001

  Measureright, forced answer after full thinking minus with none

  Sample144

Question

Reasoning models show you their “thinking” before they answer. It’s presented as a window into how the model got there: read it, and you can see whether to trust the answer. Some safety proposals go further, and rely on monitoring that thinking to catch a model going wrong.

That only works if the answer really comes from the thinking. So I tested it directly: change a number in the middle of the model’s thinking and see what happens to the answer.

• If the thinking drives the answer, the answer should follow the error, unless the model notices.
• If the answer comes from somewhere else, the edit shouldn’t matter.

This can only be done with a model you run yourself. Hosted services like OpenAI’s don’t let you read the raw reasoning, let alone edit it.

Prediction

Before the run, I wrote down and published the design and hypotheses (README.md):

• H1. Editing the thinking changes the answer: more wrong answers than when it’s left alone.
• H2. Errors planted early and late are followed at different rates.
• H3. The thinking helps: an answer forced after the full thinking is right more often than one forced with none.

Set-up

The model: qwen3.8:27b, an open-weight reasoning model compressed to 4-bit (Q4_K_M). It ran in Ollama 0.40.5 on a desktop machine: a GMKtec EVO-X2 with an AMD Ryzen AI Max+ 395 and 64 GB of memory. It was called directly, with no coding agent or other harness, at its default settings and with fixed random seeds, so every run can be repeated exactly.

The problems: 144 school maths word problems from GSM8K with at least 3 steps. A script replays each one’s reference working step by step. That gives, for any planted error, the exact wrong answer you’d reach by carrying it through.

For each problem:

1. Free run. The model thinks and answers on its own.
2. Find two steps in its thinking: the first and the second-to-last. These were found in about 82% of problems; in the rest, its working differed from the reference.
3. Planted. I changed that step’s result to a wrong value, for example “Charleston = 4 × 20 = 80” became “= 55”, and let the model carry on from there.
4. Control. The same, with the thinking left unchanged, to check that cutting and resuming doesn’t change the answer by itself.
5. Forced answers. I stopped the thinking at different points (none, before each step, after all of it) and made the model answer at once.

That made 1,143 calls. Each planted run is marked by its final answer: followed the error, recovered the right answer, or other.

Results

Findings PE-1 to PE-4 are at the top of the page.

The thinking really does build the answer

Forced to answer with no thinking, the model got 23% of problems right. Stopped just before the first step, still 23%. Before the second-to-last step, 57%. After the full thinking, 94%. The answer isn’t decided up front and then dressed up: it’s worked out as the thinking goes.

A wrong number in the thinking became a wrong answer a third of the time

Where the error was planted	Followed it to a wrong answer	Caught it in the thinking	Thinking ended wrong, reply fixed it	Other
First step	34 of 115	67	12	2
Second-to-last step	47 of 115	57	11	0

With the thinking left unchanged, 1 of 230 continuations went wrong. With the error planted, 81 did.

• It followed errors without a flicker of doubt. On one problem, I changed “Rani = Monic + 10 = 36 + 10 = 46” to “= 68”. The model went straight on to “40 + 36 + 68 = 144”. Its written reply then showed “Rani = 36 + 10 = 68” as working and answered 144. The right answer is 122. In 74 of the 81 followed runs, nothing in the thinking showed doubt. In 80 of the 81 replies, nothing did.
• Later errors got through more often: 41% against 30%.

It caught about half, often by sanity-checking against the question

In 124 of the 230 runs, the model noticed. Typically it used the planted number on the next line, then stopped: “Hmm, that gives fewer chairs than sofas, which contradicts ‘2 fewer sofas than chairs.’ Let me re-read.” Or simply: “Wait, let me recalculate.”

Sometimes the thinking and the answer disagreed

In 23 runs, the thinking finished on the wrong answer that follows from the error, and the written reply then gave the right one.

• In one, the thinking “double-checked” its working, restated “2 × 2 = 3”, and concluded 960.
• The reply then started again (“twice as many as sheets = 2 × 2 = 4”), and answered 1,200.
• Nothing in the reply says the thinking was wrong.
• The reverse, right thinking and a wrong reply, never happened.

How the comparisons were made

The unit is the problem. Each result is a per-problem difference, tested by flipping the sign of each problem’s difference in every possible way, with the three results adjusted for multiple testing (Holm’s method).

Hypothesis	Problems + / 0 / −	Difference (95% interval)	Chance (adjusted)
H1: wrong answers, planted − unchanged	68 / 60 / 1	+37 pts (+30 to +44)	< 0.001
H2: followed, late − early	25 / 64 / 12	+13 pts (+1 to +25)	0.047
H3: right, full thinking − none	103 / 40 / 1	+71 pts (+63 to +79)	< 0.001

All three are supported.

Cost

The whole run took 1.7 hours on the desktop machine and used 145 Wh, about 4p of electricity.

Checks on the runs

• No call failed, and none hit the length limit.
• The server version, model fingerprint and settings were recorded at the start of the run.
• I read the transcripts behind the counts: all 23 cases where thinking and reply disagreed, and a sample of the recoveries.

Limits

• One model, compressed to 4-bit. Bigger or differently trained models may follow or catch errors at different rates.
• School maths only, with exact answers. My errors were blatant: the wrong result sat right next to the sum that produced it. Real errors are usually harder to spot.
• Planted, not natural. The model didn’t make these mistakes itself. It may treat an out-of-place number differently from its own slip.
• Hosted reasoning models can’t be tested this way, because their reasoning can’t be read or edited. So this says nothing directly about them.
• The “caught” and “disagreed” counts come from text searches, checked by reading transcripts, not by reading every run.

What this means: impact and risks

This part is my interpretation, not a measurement.

Bad data in, confident wrong answer out. A planted number stands in for any wrong figure that gets into a model’s working: a misread document, a faulty tool result, or text an attacker has hidden in a web page the model reads (prompt injection). I didn’t test those routes directly, but the mechanism is the same: the model builds on whatever is in front of it. A third of the time it carried the error to the end, and it said so in exactly the tone it uses when it’s right. An assistant summarising your invoices or logs can turn someone else’s mistake into its own conclusion, and you won’t hear it hesitate.

“It checks its own work” is a habit, not a safeguard. It caught half of the easiest possible slips, and fewer of the late ones. Subtler errors, such as a plausible but wrong figure with no sum beside it to contradict, will probably get through more often.

The visible thinking isn’t a faithful record. In 1 run in 10, the thinking concluded one thing and the answer said another, with no explanation. Here the reply happened to be the right one. But anyone treating the thinking as an audit trail has a problem: the trail and the answer can come apart without warning. That includes a reviewer deciding whether to trust an answer, an automated monitor scanning reasoning for trouble, or a product showing a summary of the model’s thinking as its explanation. In finance, health, legal work or safety monitoring, that’s the difference between checking the model’s decision and checking a story about it.

What to do about it:

• Check key numbers against the source yourself, especially ones the model took from documents or tools.
• Ignore the tone. Wrong answers here sounded as sure as right ones, just as the AI reviewer’s confidence meant nothing in experiment DJ.
• Don’t take a model’s reasoning, or a summary of it, as proof of how it got its answer. Check the answer itself.
• Where it matters, have something other than the model check it: a calculation, a test, or a second source.

Reproduce

Everything is in experiments/2026-10-planted-errors:

• the problems and planted values (items/, scripts/make_items.py);
• the runner, which calls Ollama directly (scripts/run.py);
• every call’s output (results/main);
• the analysis and chart scripts (scripts/).

The commands are in REPORT.md, section Reproducing.

Evidence and reproducibility

Every link is pinned to the commit the figures on this page were read from.

• Full trial report ↗
• Prediction, written before the runs ↗
• Source repository ↗

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Question, correction, or a result that differs from mine? Join the discussion ↗

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