Understanding Pre-Scaling Reasoning under False Premises in Code Generation

Adaptive test-time scaling increasingly relies on instance-level signals to decide where additional reasoning should be spent. Existing allocation strategies commonly use task difficulty, agreement, or verifier feedback, yet misleading specifications introduce a different source of risk: an otherwise solvable instance can fail because a false premise redirects its solution strategy. We study this specification-conditioned vulnerability in code generation and show that it is both recurrent and selective. Across models, benchmarks, and premise families, false premises induce failures in only a minority of baseline-correct instances, leaving substantial heterogeneity within coarse model, dataset, and task groups. Behavioral analysis further shows that vulnerable and robust-correct instances begin with similar rates of Direct Premise Exploitation, then diverge sharply after false-premise injection, with induced failures disproportionately shifting toward direct reliance on the misleading premise. We introduce Pre-Scaling Reasoning (PSR), a design principle for adaptive reasoning allocation that uses pre-generation vulnerability estimates to decide where additional robustness reasoning should be allocated. Under a fixed 20% selection budget, PSR identifies 48.64% of induced failures, compared with 31.21% for leave-one-model-out task difficulty. When all routing policies use the same fixed prompting-based mitigation, PSR-guided allocation improves Pass@1 by +5.77 pp and exceeds LOMO-guided allocation by +2.92 pp. These results establish specification-conditioned vulnerability as a practical pre-generation signal for allocating limited robustness reasoning.