The confidence probe that started this line of work ran at temperature 0: deterministic, three seeds, and a finding that held at both model sizes — stated confidence pinned at ~99-100% on every answer, right or wrong. The obvious objection: temperature 0 is the model playing its hand with the easiest cards. Give it sampling, let it roll the dice a bit, and maybe the same model would admit uncertainty. A low-confidence answer, a hedge, some honest wobble.
So I ran the sweep. Same probe, same 16 hard questions, same seed (42), same Qwen2.5-1.5B-Instruct Q4_K_M — at temperature 0.7 and 1.0. That is 32 additional single-turn generations, appended to the temp-0 baseline already on disk.
Two questions flipped between temperatures. Antimony (Sb) was correct at 0 and 0.7, then wrong at 1.0 — the model answered "Sn" (tin) instead, still at 100%. The 14th element (Silicon) was wrong at 0 and 0.7 — "Chlorine", then "Oxygen" — and right at 1.0, at 100%. Two misses were stable across every temperature: the 6th prime number, answered as 11 (ground truth 13), confidently, every time; and the 23rd letter, answered Q at temp 0, then O at 0.7 and L at 1.0 — never W (this second miss was hidden by a scorer bug until the re-score; see the erratum).
Read the shape of it: the model holds a small distribution of plausible-feeling candidates per question, all of them confident. Sampling does not ask "am I sure?" — it rerolls which candidate gets served, then stamps the same ~100% on whichever lands. Temperature is a re-roll button on confident confabulations, not a calibration dial.
For any pipeline that puts a small local instruct model in the loop: raising the temperature to get "more diverse" or "more honest" output gets you more diverse wrong output with the same unearned confidence. The confidence field remains decoration at every temperature tested. If a model's verdict matters, validate the content at the boundary — do not consult its stated certainty, and do not hope a higher temperature will make it honest.
Full instrument, method, and row archives live with the probe itself, and the three-family instruction-priority checks (role quarantine absent at 1B scale) tell the same story from the other side: these models do not hold a boundary between what they state and what they are.
The natural next test is whether any instrument behaves differently: semantic entropy (asking the same question N times and measuring answer spread) should, in theory, detect the confident-roulette I measured — the same failure, different words. That is the calibration channel worth building next, not lower temperature, not verbalized confidence.