Chain-of-thought (CoT) reasoning has become a default paradigm across reasoning-intensive tasks and is widely assumed to be, at worst, harmless. Recent efficient-reasoning work treats unnecessary CoT purely as a cost to be reduced. We show that in boundary-dense classification tasks such as content moderation and toxicity detection, CoT is instead actively harmful. We identify the overthinking effect: extended reasoning amplifies weak surface signals into false evidence of violation, inflating false positive rates on ambiguous non-violating samples while its gains on ambiguous violating samples mask the harm in aggregate metrics. To address this, we propose Oreki, an adaptive CoT framework that uses K-fold cross-probing to label per-sample reasoning necessity and fine-tunes on a mixture of full-reasoning and reasoning-skipped formats. Across four base models on video moderation and multi-label toxicity detection, Oreki preserves CoT’s recall gains while suppressing its false positive inflation, outperforming both Always-CoT and Never-CoT. On toxicity detection, it cuts low-agreement false positive rate from 0.467 to 0.257 while raising overall F1 from 0.792 to 0.851.

Online video content moderation is critical for protecting minors. Violations are increasingly implicit and context-dependent: a swimwear is innocuous in a vlog but high-risk with suggestive posing. Yet, traditional binary systems fundamentally fail to capture this continuous severity spectrum, directly leading to both excessive false positives for context-heavy samples and missing deeply implicit violations. We introduce KUROKO, a content moderation system that acts as a behind-the-scenes assistant, driving a paradigm shift in content moderation from binary classification to severity-aware ranking. Our contributions are threefold: (1) Human-Centric Severity Ranking: KUROKO shifts from rigid binary divides to a continuous risk spectrum, producing nuanced, expert-level rationales closely aligning with human judgment. (2) Verifiable Alignment and Interpretability: By integrating a severity-aware data engine and a verifiable reinforcement learning approach, we replace black-box decisions with transparent, actionable diagnostics. (3) Cost-Efficient Deployment: We engineer a coarse-to-fine inference architecture equipped with a lightweight recall classifier, enabling compact models to rival proprietary VLMs at a fraction of the computational cost. Extensive offline and online A/B tests demonstrate KUROKO’s advantages over traditional binary baselines and its competitiveness with leading commercial APIs at a fraction of their scale. Online, KUROKO yields more human-confirmed enforcement actions under an equal human-review budget, while lowering violative exposure and user negative feedback.