5 papers
Personalized State-Transition-Aware Memory for Clinical Agents
Maryam Haghifam, Zahra Rajabi, Yizhou Sun +1
Large language model (LLM) agents that reason over clinical records must track changes in a patient's state while preserving the history needed to understand them. Simply accumulat…
SeDeM: Selective Decompression of Hidden-State Memories for Long-Context Question Answering
Maryam Haghifam, Jason Cong, Yizhou Sun
Long-context inference with large language models (LLMs) is costly: self-attention during prefill scales quadratically with sequence length, and the key-value (KV) cache grows with…
HMT: Semantic Hierarchy-Aware Hierarchical Memory Transformer
Maryam Haghifam, Zifan He, Jason Cong +1
Transformer-based LLMs achieve strong results on many language tasks; however, long inputs remain challenging because context windows are finite, and prefill latency and memory gro…
Proteus: Preserving Model Confidentiality during Graph Optimizations
Yubo Gao, Maryam Haghifam, Christina Giannoula +3
Deep learning (DL) models have revolutionized numerous domains, yet optimizing them for computational efficiency remains a challenging endeavor. Development of new DL models typica…
Design and Evaluation of Crowd-sourcing Platforms Based on Users Confidence Judgments
Samin Nili Ahmadabadi, Maryam Haghifam, Vahid Shah-Mansouri +1
Crowd-sourcing deals with solving problems by assigning them to a large number of non-experts called crowd using their spare time. In these systems, the final answer to the questio…