11 papers
SmoothAgent: Efficient Long-Horizon LLM-Based Agent Serving with Lookahead Context Engineering
Zaifeng Pan, Qianxu Wang, Zhengding Hu +5
LLM-based agents execute multi-turn workflows with continuously growing contexts, where LLM calls are interleaved with tool invocations and environment feedback. To maintain model…
FlashCP: Load-Balanced Communication-Efficient Context Parallelism for LLM Training
Zheng Wang, Eric Liu, Linan Jiang +5
Context parallelism (CP) is essential for training large-scale, long-context language models, as it partitions sequences to reduce memory overhead. However, existing CP methods suf…
TLX: Hardware-Native, Evolvable MIMW GPU Compiler for Large-scale Production Environments
Yue Guan, Hongtao Yu, Peng Chen +10
Modern GPUs increasingly rely on specialized hardware units and asynchronous coordination mechanisms, so performance depends on orchestrating data movement, tensor-core computation…
ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation
Zhongkai Yu, Yichen Lin, Chenyang Zhou +12
Existing API-based agentic systems for RTL code generation are fundamentally misaligned with industrial practice: they assume a golden testbench is available at generation time, re…
FlashEvolve: Accelerating Agent Self-Evolution with Asynchronous Stage Orchestration
Zhengding Hu, Mingge Lu, Zhen Wang +8
LLM-based evolution has emerged as a promising way to improve agents by refining non-parametric artifacts, but its wall-clock cost remains a major bottleneck. We identify that this…
AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving
Zhongkai Yu, Haotian Ye, Chenyang Zhou +9
All current LLM serving systems place the GPU at the center, from production-level attention-FFN disaggregation to NVIDIA's Rubin GPU-LPU heterogeneous platform. Even academic PIM/…