🤖 AI Summary
This work addresses the challenges of fusing heterogeneous modalities—such as time-series metrics and textual logs—and the inherent difficulty large language models face in processing continuous temporal data for root cause analysis in cloud infrastructure failures. To this end, the authors propose a multimodal diagnostic framework that aligns time-series performance indicators with the embedding space of pretrained language models through temporal semantic compression, a gated cross-attention alignment encoder, and a retrieval-augmented generation mechanism. This integration enables automated root cause localization informed by historical knowledge. Experimental evaluation across six cloud system benchmarks demonstrates that the proposed method achieves a diagnosis accuracy of 48.75%, significantly outperforming existing approaches, particularly in complex, multi-fault scenarios.
📝 Abstract
Root cause analysis in modern cloud infrastructure demands sophisticated understanding of heterogeneous data sources, particularly time-series performance metrics that involve core failure signatures. While large language models demonstrate remarkable capabilities in textual reasoning, their discrete token-based architecture creates fundamental incompatibilities with continuous numerical sequences exhibiting temporal dependencies. Current methodologies inadequately address this modality mismatch, constraining the potential of language model-driven automation in incident management workflows. This paper presents a multimodal diagnostic framework that harmonizes time-series representations with pretrained language model embedding spaces. Our approach contributes three technical advances: (1) a semantic compression technique that distills temporal segments into single-token abstractions while preserving pattern semantics, (2) an alignment encoder utilizing gated cross-attention to project time-series features into language model latent space, and (3) a retrieval-augmented diagnostic pipeline that synthesizes aligned embeddings with historical incident knowledge for expert-level failure attribution. Comprehensive evaluation across six cloud system benchmarks demonstrates that our framework achieves leading performance, reaching 48.75% diagnostic accuracy with notable improvements on scenarios involving compound failure modes. The results validate embedding-space alignment as an effective strategy for enabling language models to reason over multimodal telemetry data in production incident response contexts.