Seunghwan Jeong
Author directory2026
ParentingLLM : Pacing Reasoning Maturity for Bidirectional Test-Time Scaling under Token Budgets
Seunghwan Jeong | Sugi Lee | WeiJin Park | Ikjun Yeom | Honguk Woo
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Seunghwan Jeong | Sugi Lee | WeiJin Park | Ikjun Yeom | Honguk Woo
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Reasoning LLMs under fixed token budgets often commit before verification finishes, or spend the budget on deliberation that never reaches a final answer. Existing inference-time controllers regulate reasoning length, which does not indicate whether the current prefix is ready to be finalized. We introduce ParentingLLM, an inference-time controller that paces reasoning maturity, defined as the prefix’s readiness to commit given the remaining budget, against a user-specified target schedule, without fine-tuning the underlying LLM. The maturity estimate combines consumed budget with a forked forced-commit probe; the controller injects Wait when the estimate runs ahead of the schedule and Hurry when it lags. On MATH-500, GPQA-Diamond, AIME 24+25, and GSM-Hard with three Gemma-4 models and Qwen3.5-9B, ParentingLLM improves over prior inference-time baselines, with the largest gains on AIME 24+25 under a tight budget (-22.6 burst rate, +4.0 to +7.3 accuracy at B=1024).