2 citations · 5 across the 17 of their papers we have counts for
9 papers · 1 filter
Learnable Low-Rank Polynomial Sketch for Effective Linear Attention
Zongyue Qin, Haoran Deng, Jason Cong +1
Softmax attention in Transformers suffers from quadratic complexity in sequence length, making it impractical for long-context applications. Linear attention alleviates this issue…
ACLArena: Agent Continue Learning in Multi-stage Post-training
Haixin Wang, Xiaoxuan Wang, Junkai Zhang +9
Building general-purpose agents for industrial deployment requires integrating multiple capabilities, each typically acquired at a distinct stage of training. Yet there is currentl…
SeDeM: Selective Decompression of Hidden-State Memories for Long-Context Question Answering
Maryam Haghifam, Jason Cong, Yizhou Sun
Long-context inference with large language models (LLMs) is costly: self-attention during prefill scales quadratically with sequence length, and the key-value (KV) cache grows with…
HarnessBridge: Learnable Bidirectional Controller for LLM Agent Harness
Xiaoxuan Wang, Haixin Wang, Alexander Taylor +3
Large language models are increasingly deployed as agents for long-horizon tasks, yet their performance is shaped not only by model capability and environment design, but also by t…
HMT: Semantic Hierarchy-Aware Hierarchical Memory Transformer
Maryam Haghifam, Zifan He, Jason Cong +1
Transformer-based LLMs achieve strong results on many language tasks; however, long inputs remain challenging because context windows are finite, and prefill latency and memory gro…
Understand and Accelerate Memory Processing Pipeline for Large Language Model Inference
Zifan He, Rui Ma, Yizhou Sun +1
Modern large language models (LLMs) increasingly depends on efficient long-context processing and generation mechanisms, including sparse attention, retrieval-augmented generation…
ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning
Xiaoxuan Wang, Han Zhang, Haixin Wang +11
Agentic reinforcement learning (ARL) has rapidly gained attention as a promising paradigm for training agents to solve complex, multi-step interactive tasks. Despite encouraging ea…
FlexLLM: Composable HLS Library for Flexible Hybrid LLM Accelerator Design
Jiahao Zhang, Zifan He, Nicholas Fraser +3
We present FlexLLM, a composable High-Level Synthesis (HLS) library for rapid development of domain-specific LLM accelerators. FlexLLM exposes key architectural degrees of freedom…
Report for NSF Workshop on AI for Electronic Design Automation
Deming Chen, Vijay Ganesh, Weikai Li +7
This report distills the discussions and recommendations from the NSF Workshop on AI for Electronic Design Automation (EDA), held on December 10, 2024 in Vancouver alongside NeurIP…