Ali Asgarov — accepted AI-conference papers

2026

Immunizing Models Against Harmful Long-Horizon Fine-Tuning via Contractive Optimization Dynamics

Najibul Haque Sarker, Zaber Ibn Abdul Hakim, Ali Asgarov, Chia-Wei Tang, Alvi Md Ishmam, Chris Thomas

CVPR 2026

Fine-tuning has become the default way to adapt powerful foundation models, but this also enables low-cost repurposing for harmful objectives. Existing immunization methods try to optimize local geometry or simulate short attacker horizons, and penalize observed loss drops. However, in practice, dow

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2026

Lenses: Toward Polysemous Vision-Language Understanding

Hani Alomari, Ali Asgarov, Chris Thomas

CVPR 2026

Most vision-language models assume images have a single literal meaning, even though images are inherently polysemous. We propose a retrieval paradigm that models many-to-many relationships between images and text using interpretive lenses and introduce Lenses, a multi-prompt embedding model and dat

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2025

Benchmarking and Mitigating MCQA Selection Bias of Large Vision-Language Models

Md. Atabuzzaman, Ali Asgarov, Chris Thomas

EMNLP 2025

Large Vision-Language Models (LVLMs) have achieved strong performance on vision-language tasks, particularly Visual Question Answering (VQA). While prior work has explored unimodal biases in VQA, the problem of selection bias in Multiple-Choice Question Answering (MCQA), where models may favor speci

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