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Articles by Haz Sameen
Computer Science > Information Retrieval
Chain-of-thought (CoT) distillation in the recommendation domain is a necessary precursor to RL training, but raw teacher traces are ill-suited to this task. Large teachers approach the recommendation task with unusually high reasoning uncertainty, repeatedly rechecking their answers without revising them; supervised fine-tuning on such traces produces verbose students that never revise their initial guess.
Computer Science > Machine Learning
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Computer Science > Computer Vision and Pattern Recognition
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[2602.08874] Is Reasoning Capability Enough for Safety in Long-Context Language Models?
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
[2512.22457] A Real-Time System to Populate FRA Form 57 from News
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
AbFlowNet: Optimizing Antibody-Antigen Binding Energy via Diffusion-GFlowNet Fusion
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LELANTE: LEveraging LLM for Automated ANdroid TEsting
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
[2312.14440] Asymmetric Bias in Text-to-Image Generation with Adversarial Attacks
[Submitted on 22 Dec 2023 ( v1 ), last revised 14 Feb 2024 (this version, v2)] Title:Asymmetric Bias in Text-to-Image Generation with Adversarial Attacks Download a PDF of the paper titled Asymmetric Bias in Text-to-Image Generation with Adversarial Attacks, by Haz Sameen Shahgir and 3 other authors Download PDF HTML (experimental) Abstract:The widespread use of Text-to-Image (T2I) models in content generation requires careful examination of their safety, including their robustness to...
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