3 papers
cs.DC2026
SPORK: Self-Speculative Forking to Accelerate Agentic LLM Inference
Huajun Bai, Weiwei Lv, Huichuan Zheng +2
LLM agents are becoming a common interface for research, coding, and question answering, yet their Thought-Action-Observation loop is often serial: the model reasons, emits a tool…
cs.LG2025
PARD: Accelerating LLM Inference with Low-Cost PARallel Draft Model Adaptation
Zihao An, Huajun Bai, Ziqiong Liu +2
The autoregressive nature of large language models (LLMs) fundamentally limits inference speed, as each forward pass generates only a single token and is often bottlenecked by memo…
cs.AI2025
SpecExit: Accelerating Large Reasoning Model via Speculative Exit
Rubing Yang, Huajun Bai, Song Liu +7
Despite their strong performance on reasoning tasks, large reasoning models (LRMs) often suffer from overthinking, producing unnecessarily long outputs and incurring high end-to-en…