arXiv:2606.04594v1 Announce Type: cross Abstract: LLM serving frameworks are quickly evolving with a complex software stack and a vast number of optimizations. The rapid development process can introduce silent errors where output quality silently degrades without any explicit error signals. Diagnosing silent errors is notoriously difficult due to the substantial semantic gap between the high-level symptoms and the low-level root causes. We observe that diagnosis of silent errors can be effectively framed as a differential debugging problem by leveraging the existence of semantically correct r
The rapid deployment and increasing complexity of LLM serving frameworks are leading to a higher incidence of 'silent errors,' necessitating immediate solutions for robust operation.
Reliable and high-quality LLM performance is critical for widespread adoption and trust, making tools for error diagnosis crucial for developers and end-users alike.
The ability to automatically diagnose silent errors in LLM inference will significantly improve the stability and trustworthiness of AI applications, accelerating their integration into critical systems.
- · LLM developers
- · AI-dependent industries
- · Cloud providers
- · AI infrastructure companies
- · Companies with unreliable LLM deployments
Increased reliability of LLM-powered applications.
Faster development and deployment cycles for AI solutions due to reduced debugging time.
Broader adoption of LLMs in highly sensitive or mission-critical applications.
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Read at arXiv cs.AI