Author

Feng Xia

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#artificial intelligence Preprint Sep 2026

The Commit-Abstain Circuit: Why Language Models Hallucinate Instead of Abstaining

Language models (LMs) often hallucinate by committing to confident answers rather than abstaining, even when they do not have enough information to answer reliably. A large body of existing work mitigates hallucination through detection or abstention mechanisms, but leaves open how models internally arrive at the decis...

Vy Nguyen, Zi-Qi Xu, Jeffrey Chan et al. · 0 citations

#artificial intelligence Preprint Aug 2026

Twin Worlds: Equivariance-Based Abstention for Evidence-Grounded Reasoning

Twin Worlds (TW), a framework for improving reliability in knowledge-intensive reasoning through equivariance-based abstention, is proposed, which identifies when answers are not reliably grounded in the provided evidence and outperforms uncertainty- and sufficiency-based baselines.

Vy Nguyen, Zi-Qi Xu, Jeffrey Chan et al. · 1 citation