4 papers
Can LLMs Truly Forget? Revealing Unlearning Gaps Through Adversarial Evaluation
Ayush Gupta, Hima Varshini Surisetty, Sreevidya Bollineni +5
Machine unlearning aims to remove the influence of targeted training data from a model while preserving its remaining capabilities, but evaluating whether such information has trul…
Metag: A dataset to build agentic meta-reviewing capabilities
Anirudh Sundar, Min Chen, Divya Tadimeti +11
AI tools increasingly support tasks across the scientific research cycle, from experiment design and manuscript preparation to peer review. At the same time, the continuing growth…
High-Fidelity Synthetic ECG Generation via Mel-Spectrogram Informed Diffusion Training
Zhuoyi Huang, Nutan Sahoo, Anamika Kumari +13
The development of machine learning for cardiac care is severely hampered by privacy restrictions on sharing real patient electrocardiogram (ECG) data. Although generative AI offer…
One Head, Many Models: Cross-Attention Routing for Cost-Aware LLM Selection
Roshini Pulishetty, Mani Kishan Ghantasala, Keerthy Kaushik Dasoju +8
The proliferation of large language models (LLMs) with varying computational costs and performance profiles presents a critical challenge for scalable, cost-effective deployment in…