collaborators

7 papers

cs.LG2026

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding

Shuang Liang, Hao Mark Chen, Zhiwen Mo +4

Speculative decoding verifies a tree of draft tokens in one target-model forward pass. For a mixture-of-experts (MoE) target, however, parallel verification can activate the union…

cs.AR2026

Coset Ensemble Decoder for Quantum Error Correction with Algorithm-Hardware Co-Design

Shuang Liang, Jubo Xu, Giulio Bassanino +8

Reliable large-scale quantum computation relies on fault-tolerant architectures, where quantum error correction (QEC) continuously extracts and decodes error syndromes in real time…

cs.AR2026

DeepStack: Facilitating Co-Design Exploration of 3D DRAM-Stacked Accelerators for Distributed LLM Inference

Zhiwen Mo, Guoyu Li, Hao Mark Chen +11

Advances in hybrid bonding and packaging have driven growing interest in 3D DRAM-stacked AI accelerators. As large language models (LLMs) scale to hundreds of billions or trillions…

cs.LG2026

Dynamic Expert Sharing: Decoupling Memory from Parallelism in Mixture-of-Experts Diffusion LLMs

Hao Mark Chen, Zhiwen Mo, Royson Lee +6

Among parallel decoding paradigms, diffusion large language models (dLLMs) have emerged as a promising candidate that balances generation quality and throughput. However, their int…

cs.AR2025

Combating the Memory Walls: Optimization Pathways for Long-Context Agentic LLM Inference

Haoran Wu, Can Xiao, Jiayi Nie +15

LLMs now form the backbone of AI agents across a diverse range of applications, including tool use, command-line interfaces, and web or computer interaction. These agentic LLM infe…

cs.LG2025

FastTTS: Accelerating Test-Time Scaling for Edge LLM Reasoning

Hao Mark Chen, Zhiwen Mo, Guanxi Lu +4

Recent advances in reasoning Large Language Models (LLMs) are driving the emergence of agentic AI systems. Edge deployment of LLM agents near end users is increasingly necessary to…

cs.AI2025

Rethinking Optimal Verification Granularity for Compute-Efficient Test-Time Scaling

Hao Mark Chen, Guanxi Lu, Yasuyuki Okoshi +3

Test-time scaling (TTS) has proven effective in enhancing the reasoning capabilities of large language models (LLMs). Verification plays a key role in TTS, simultaneously influenci…