← 研踪 PaperTrailarXiv:2607.22595 · v2
OrigaMI: An LLM Serving System with First-Class Interpretability Apps
首次提交 2026-06-12 04:58
版本更新 2026-11-09 18:19
雷达观测 2026-11-10 15:22
摘要
Mechanistic interpretability (MI) enables monitoring and steering LLMs by operating on their internal state during inference. MI applications remain difficult to develop, expensive to execute, and challenging to manage at runtime. We present OrigaMI, a system that integrates MI applications as first-class components of LLM serving, enabling simplified development, efficient execution, and flexible deployment. Guided by a comprehensive analysis of modern MI applications, OrigaMI streamlines the MI applications development via a simple Python-based Domain Specific Language (DSL) for accessing intra- and inter-layer runtime model state at well-defined trigger points during inference. OrigaMI enables efficient access to ephemeral attention scores via a novel FlashInspect kernel that retains the performance benefits of FlashAttention. OrigaMI's compiler integrates multiple MI applications into the performance-optimized inference execution flow while ensuring conflict-free coexistence. OrigaMI's runtime supports dynamic, per-request activation and deactivation of MI applications without service interruption or performance degradation when inactive, leveraging OrigaMI's dynamic memory management and CUDA graph-compatible execution. We integrate OrigaMI with vLLM and evaluate ten MI applications, including sparse autoencoders, steering, and jailbreak detection, across four state-of-the-art LLMs. OrigaMI incurs negligible execution overhead, up to two orders of magnitude lower than existing frameworks, while enabling per-request activation and deactivation up to 1,000 times faster than alternatives. These results demonstrate that OrigaMI makes MI applications practical for deployment in production LLM serving systems.
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v2 · 2026-11-09 18:19 · 最新观测版本
OrigaMI: An LLM Serving System with First-Class Interpretability Apps
Mechanistic interpretability (MI) enables monitoring and steering LLMs by operating on their internal state during inference. MI applications remain difficult to develop, expensive to execute, and challenging to manage at runtime. We present OrigaMI, a system that integrates MI applications as first-class components of LLM serving, enabling simplified development, efficient execution, and flexible deployment. Guided by a comprehensive analysis of modern MI applications, OrigaMI streamlines the MI applications development via a simple Python-based Domain Specific Language (DSL) for accessing intra- and inter-layer runtime model state at well-defined trigger points during inference. OrigaMI enables efficient access to ephemeral attention scores via a novel FlashInspect kernel that retains the performance benefits of FlashAttention. OrigaMI's compiler integrates multiple MI applications into the performance-optimized inference execution flow while ensuring conflict-free coexistence. OrigaMI's runtime supports dynamic, per-request activation and deactivation of MI applications without service interruption or performance degradation when inactive, leveraging OrigaMI's dynamic memory management and CUDA graph-compatible execution. We integrate OrigaMI with vLLM and evaluate ten MI applications, including sparse autoencoders, steering, and jailbreak detection, across four state-of-the-art LLMs. OrigaMI incurs negligible execution overhead, up to two orders of magnitude lower than existing frameworks, while enabling per-request activation and deactivation up to 1,000 times faster than alternatives. These results demonstrate that OrigaMI makes MI applications practical for deployment in production LLM serving systems.
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