6 papers
Arbor: Tree Search as a Cognition Layer for Autonomous Agents
Neha Prakriya, Chaojun Hou, Zheng Gong +5
Arbor is a multi-agent framework that introduces structured tree search as a cognition layer for autonomous agents operating in large, stateful action spaces. Prior autonomous opti…
LIFT: LLM-Based Pragma Insertion for HLS via GNN Supervised Fine-Tuning
Neha Prakriya, Zijian Ding, Yizhou Sun +1
FPGAs are increasingly adopted in datacenter environments for their reconfigurability and energy efficiency. High-Level Synthesis (HLS) tools have eased FPGA programming by raising…
Optimized Multi-Token Joint Decoding with Auxiliary Model for LLM Inference
Zongyue Qin, Ziniu Hu, Zifan He +3
Large language models (LLMs) have achieved remarkable success across diverse tasks, yet their inference processes are hindered by substantial time and energy demands due to single-…
Dynamic-Width Speculative Beam Decoding for Efficient LLM Inference
Zongyue Qin, Zifan He, Neha Prakriya +2
Large language models (LLMs) have shown outstanding performance across numerous real-world tasks. However, the autoregressive nature of these models makes the inference process slo…
HMT: Hierarchical Memory Transformer for Efficient Long Context Language Processing
Zifan He, Yingqi Cao, Zongyue Qin +3
Transformer-based large language models (LLM) have been widely used in language processing applications. However, due to the memory constraints of the devices, most of them restric…
Accelerating Large Language Model Pretraining via LFR Pedagogy: Learn, Focus, and Review
Neha Prakriya, Jui-Nan Yen, Cho-Jui Hsieh +1
Traditional Large Language Model (LLM) pretraining relies on autoregressive language modeling with randomly sampled data from web-scale datasets. Inspired by human learning techniq…