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Sean Du brings a reasoning-model hallucination detector to ICML 2026

The NTU researcher will present ARS, a label-free method for detecting when long reasoning traces hide unstable answers.

By Ryan Merket · Published Jun 28th, 2026, 5:10pm CT

Primary source: X

Why it matters

Reasoning models make wrong answers look more convincing. ARS targets the hidden instability behind those answers, a reliability layer agent builders will need.

Sean Du of NTU studies a computer monitor showing complex data patterns representing ARS hallucination detection ahead of ICML 2026.

The NTU researcher will present ARS, a label-free method for detecting when long reasoning traces hide unstable answers.

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