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cs.AI2026
LoopAhead: Speculating Ahead for Faster Decoding in Looped Language Models
Zixuan Liu, Nan Xue, Rui Bao +4
Looped Transformers improve parameter efficiency by repeatedly applying the same Transformer layers. However, generating each token still involves sequential computation and repeat…
cs.IT2026
CR^2: Cost-Aware Risk-Controlled Routing for Wireless Device-Edge LLM Inference
Nan Xue, Shengkang Chen, Zhiyong Chen +4
As large language models (LLMs) move from centralized clouds to mobile edge environments, efficient serving must balance latency, energy consumption, and accuracy under constrained…
cs.IT2026
WISV: Wireless-Informed Semantic Verification for Distributed Speculative Decoding in Device-Edge LLM Inference
Zixuan Liu, Zhiyong Chen, Nan Xue +4
While distributed device-edge speculative decoding enhances resource utilization across heterogeneous nodes, its performance is often bottlenecked by conventional token-level verif…