Author

Aditi Sarker

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#artificial intelligence Preprint Aug 2026

Partition-Aware Unlearning for Removing Spurious Correlations in Large Vision-Language Models

The results show that PURGE consistently reduces hallucinations and spurious-correlation-driven errors while maintaining or improving overall performance in most evaluated settings, providing both a reusable evaluation protocol and an effective mitigation framework for more reliable LVLMs.

Aditi Sarker, Nazreen Shah, Rafi Ibn Sultan et al. · 0 citations

#artificial intelligence Preprint Aug 2026

Hallucination Mitigation for Large Vision-Language Models via Implicit Feature Stabilization

The framework, INFUSE, first stabilizes visual and textual representations around perturbation-averaged and ground-truth anchors, then aligns the stabilized representations across modalities with bidirectional contrastive objectives.

Aditi Sarker, Rafi Ibn Sultan, Hui Zhu et al. · 0 citations