Mingxuan Yuan
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The paper reframes context distillation as a latent memory management problem, proposing a modular framework using LoRA adapters and a Self-Gating mechanism for efficient, selective memory retrieval and activation.
The paper introduces Opt-Verifier, a novel LLM-based framework that significantly improves the accuracy of automated optimization model generation by implementing dual-side verification from both structural and solution perspectives.
A co-designed hardware-software approach for task-conditional sparsity in multi-task inference models, reducing FLOPs, latency, and energy.
Papers
Sparse by Command: Task-Conditional Compute Skipping for Multi-Task Inference Accelerators
Afzal Ahmad, Gaoyu Mao, Shoubo Hu, Hui-Ling Zhen +3 more
A co-designed hardware-software approach for task-conditional sparsity in multi-task inference models, reducing FLOPs, latency, and energy.