A debate panel can become unanimous without becoming more correct. This is dangerous for downstream safeguards: a substituted verification ballot can change only narrow-margin votes, while richer arbiters lose disagreement as a natural targeting signal. We show that one debate round can erase that resource without requiring persuasion. Tracking a heterogeneous 7-judge panel through a blind round and three debate rounds on 600 code-correctness candidates, unanimity on a fixed cohort jumps from 39.5% to 95.2% in round 1 (93.1% of the total collapse), while accuracy moves by less than one point and 96.3% of verdict flips follow the displayed peer majority. An execution-based verification ballot corrects 8 of 2,037 pre-debate candidate-substitution instances but changes zero in every later round; by round 3 every wrong decision is unanimous, erasing dissent that had flagged two-thirds of the panel's errors. Identical-cohort controls explain why: no-peer reconsideration reproduces 79.6% of the collapse, real labels without reasoning reproduce 91.5%, and random labels steer flips toward whatever they display; the full-debate condition adds 4.3 percentage points over labels only (clustered 95% CI 0.7--8.1). The one-round collapse reproduces in two additional real runs and two fake-label seeds, remains under panel sizes 3--7, and appears in MATH-500. Parse failures concentrate on contested candidates (p<0.001), making attrition non-ignorable. The design implication is operational: verify before any second-pass evaluation or peer exposure, and never treat post-debate unanimity as independent evidence of reliability.
Figures & tables
Figure 1: Mechanism overview. Round metrics use the fixed n=291 cohort: one debate round takes unanimity from 39.5% to 95.2%, pivotal cases from 10 to 2, and execution-ballot substitution to 0/2,037 realized flips. The control callout uses the separate matched n=280 cohort (Section 5).
n
Pivotal share (R0)
Attrition point
ret.
drop.
ret.
drop.
χ2(3)
Round 1
323
277
3.7%
31.1%
151.9
Round 2
300
300
3.7%
29.0%
135.9
Round 3
291
309
3.4%
28.5%
134.7
Round 1 (fake)
416
184
9.6%
31.5%
78.9
Table 1: Parse-failure attrition is strongly non-random: candidates dropped for unparseable judge output are far more often pivotal at round 0 than retained ones ( χ2 over the four round-0 margin bins {1,3,5,7} , df =3 ; all p<10−3 ). Retained/dropped round-0 panel accuracy differs correspondingly (e.g. 92.0% vs. 70.8% at round 1).
Figure 2: Debate-induced consensus collapse on the fixed cohort ( n=291 candidates usable in all rounds). The pivotal share (orange-red) falls from 3.4% to 0.7% after one round and to zero thereafter; unanimity (green) rises from 39.5% to 95.2% in round 1, then only to 98.6% and 99.3%. Essentially the entire effect of debate on disagreement is exhausted by the first exposure to peers’ opinions (per-judge flip rates: Table 2 ).
Verifier subst.
Round
Pivotal
Unanimity
Flips
Changes
Net
0 (blind)
10
39.5%
—
8 (8✓)
+0.39 pp
1
2
95.2%
11.78%
0
0.00
2
0
98.6%
0.74%
0
0.00
3
0
99.3%
0.10%
0
0.00
Table 2: Blind round plus three debate rounds on the fixed cohort ( n=291 ). “Flips” is per-judge verdict changes vs. the previous round (of 291×7=2,037 ). “Verifier subst.”: decision-changing candidate–substitution instances from splicing the execution ballot in place of each judge in turn (2,037 instances per round; the 8 round-0 changes are 2 unique candidates × 4 majority-side replacements, all corrections) and the net accuracy gain averaged over replacement positions.
Judge
Real flips
Fake flips
GLM-5-Turbo
0.7%
1.7%
Kimi-K2.6
2.3%
3.6%
Claude Haiku
7.3%
7.9%
DeepSeek-V4-Flash
9.2%
10.6%
Qwen3-max
13.9%
14.5%
GPT-4o-mini
24.1%
26.1%
Table 3: Per-judge round-0 → 1 flip rates under real and fabricated peer opinions, both computed on the identical n=303 candidates usable in both conditions. Rates span an order of magnitude (0.7%–26.1%) and the cross-condition ranking is almost perfectly preserved (Spearman ρ=0.99 , p<0.001 , bootstrap CI [0.93,1.0] ). Every judge flips at least as often in the fake condition, where the i.i.d. random labels confront it with an opposing majority more often – so absolute rates are not comparable across conditions, only rankings.
Figure 3: Five conditions on the identical cohort of n=280 candidates. A no-peer second look reaches 79.6% of full debate’s unanimity jump; random labels steer flips but slow net convergence; real labels without reasoning reach 91.5%; the full-debate condition adds 4.3pp over labels only. Flip volume is nearly identical in all four intervention conditions (10.4–11.8%).