UTS at ELOQUENT 2026 Voight-Kampff: structural shifts in AI writing bypass state-of-the-art detectors

Structural out-of-distribution attacks bypass adversarially fine-tuned AI text detectors at ELOQUENT 2026

Researchers from UTS demonstrate that adversarial fine-tuning of AI text detectors, while closing 2025-era evasion methods, fails against a newly identified class of out-of-distribution attacks. Two novel attack families — cross-decade register shifts and modernist stream-of-consciousness form — achieve up to ~50x higher fool rates than prior methods while preserving text naturalness. The work reveals a fundamental asymmetry: pushing generated text out of a detector's training distribution reliably defeats detection, while pulling it toward human training data does not. These findings swept the top 5 positions on the ELOQUENT 2026 Voight-Kampff leaderboard and suggest persistent structural vulnerabilities in current detector families.