Public articles linked to the same research event.
arXiv WAMJET is an agentic harness that equips coding agents with reusable optimization guidance and measurement and validation tools, following a bottleneck-driven workflow that profiles inference, modifies targeted code, validates effects, and iteratively refines the acceleration stack, achieving up to 9.95x lossless speedup over upstream implementations across six World Action Models, three coding agents, and two GPU architectures, with approximation and hardware-aware optimization yielding additional latency reductions at comparable success rates.
WAMJET is an agentic harness that equips coding agents with reusable optimization guidance and measurement and validation tools, following a bottleneck-driven workflow that profiles inference, modifies targeted code, validates effects, and iteratively refines the acceleration stack, achieving up to 9.95x lossless speedup over upstream implementations across six World Action Models, three coding agents, and two GPU architectures, with approximation and hardware-aware optimization yielding additional latency reductions at comparable success rates.
WAMJET is an agentic harness that equips coding agents with reusable optimization guidance and measurement and validation tools, following a bottleneck-driven workflow that profiles inference, modifies targeted code, validates effects, and iteratively refines the acceleration stack, achieving up to 9.95x lossless speedup over upstream implementations across six World Action Models, three coding agents, and two GPU architectures, with approximation and hardware-aware optimization yielding additional latency reductions at comparable success rates.
WAMJET is an agentic harness that equips coding agents with reusable optimization guidance and measurement and validation tools, following a bottleneck-driven workflow that profiles inference, modifies targeted code, validates effects, and iteratively refines the acceleration stack, achieving up to 9.95x lossless speedup over upstream implementations across six World Action Models, three coding agents, and two GPU architectures, with approximation and hardware-aware optimization yielding additional latency reductions at comparable success rates.