3 papers
cs.LG2026
DARTS: Decoder-Aware Representation Tuning via Surgery for Model Merging
Aaryan Ajay Sharma, Sai Nishanth Padala, Seganrasan Subramanian
Model merging combines multiple task-specific fine-tuned LLMs into a single multi-task model without additional training. However, merged models are known to suffer from representa…
cs.LG2025
Merge Now, Regret Later: The Hidden Cost of Model Merging Is Adversarial Transferability
Mauro Conti, Ankit Gangwal, Aaryan Ajay Sharma
Model Merging (MM) has proven to be an effective alternative to multi-task learning, where several fine-tuned models are merged, without access to the tasks' training data, into on…
cs.CR2024
GENIE: Watermarking Graph Neural Networks for Link Prediction
Venkata Sai Pranav Bachina, Aaryan Ajay Sharma, Ankit Gangwal +1
The rapid adoption, usefulness, and resource-intensive training of Graph Neural Network (GNN) models have made them an invaluable intellectual property in graph-based machine learn…