1 citations · 1 across the 5 of their papers we have counts for
5 papers
Not All Ranks Are Equal: Budget-Aware LoRA Merging Across Tasks
Avinash Amballa, Yashas Malur Saidutta, Wenbo Li +2
Merging low-rank adapters (LoRAs) promises to eliminate the overhead of swapping task-specific weights at inference time. However, existing merging methods assume every layer needs…
PROGRESS: Coverage-guided RL to Train Search-augmented LLM Agent
Sudipta Paul, Vijay Srinivasan, Vivek Kulkarni +4
Existing search-augmented LLM agents are trained using Reinforcement Learning to boost its reasoning capabilities. However, these approaches primarily rely on outcome-level rewards…
Latent Personal Memory: Represent personal memory as dynamic soft prompts
Debrup Das, Avinash Amballa, Yashas Malur Saidutta +3
Personalizing large language models (LLMs) requires encoding long-term, user-specific behavioral patterns in a way that is computationally efficient, scalable, and compatible with…
Doc-to-Atom: Learning to Compile and Compose Memory Atoms
Xingjian Diao, Wenbo Li, Yashas Malur Saidutta +3
Long input sequences are central to document understanding and multi-step reasoning in Large Language Models, yet the quadratic cost of attention makes inference both memory-intens…
RestoreGrad: Signal Restoration Using Conditional Denoising Diffusion Models with Jointly Learned Prior
Ching-Hua Lee, Chouchang Yang, Jaejin Cho +4
Denoising diffusion probabilistic models (DDPMs) can be utilized to recover a clean signal from its degraded observation(s) by conditioning the model on the degraded signal. The de…