Papers by Aurick Qiao
TAGQuant: Token-Aware Clustering for Group-Wise Quantization (2026.eacl-industry)
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| Challenge: | Existing work clusters channels using token dimension, which is suboptimal for grouping . a common challenge in LLM quantization is supporting "group-wise" quantization . |
| Approach: | They propose a method to group channels with similar activation distributions using tokens . they propose shuffle operation that reduces relative GSM8K error by 86% . |
| Outcome: | The proposed method reduces GSM8K error by 86% in both INT4 and MXFP4 formats compared to baselines . |
STUN: Structured-Then-Unstructured Pruning for Scalable MoE Pruning (2025.acl-long)
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| Challenge: | Mixture-of-experts (MoEs) have been adopted for reducing inference costs by sparsely activating experts in large language models (LLMs). |
| Approach: | They propose a structured-then-unstructured approach outperforming both of structured and unstructured pruning for MoEs. |
| Outcome: | The proposed approach outperforms both of structured and unstructured pruning, especially for MoEs with hundreds of experts. |
SwiftKV: Fast Prefill-Optimized Inference with Knowledge-Preserving Model Transformation (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are an integral enabler of enterprise applications such as summarization, retrieval augmented generation, and agentic workflows. |
| Approach: | They propose a model transformation and distillation procedure that prefills later layers’ KV cache using an earlier layer’s output, allowing prompt tokens to skip those later layers. |
| Outcome: | The proposed procedure can reduce prefill computation by 25-50% across several LLM families while incurring minimum quality degradation. |