AI / ML

MoE²-LoRA introduces a dual-channel Routing-Conditioned Projection module to fine-tune Mixture-of-Experts models. It reuses base router activations to guide LoRA routing, avoiding the efficiency loss of uniform adapters. This approach preserves pretrained expert specialization while enabling dynamic, per-token task adaptation.

• Introduces MoE-style low-rank adaptation for LLMs, a previously underexplored area.
• Uses Routing-Conditioned Projection to reuse base router activations for LoRA routing.
• Avoids uniform adapters that ignore router priors and risk catastrophic forgetting.
• Enables dynamic expert selection rather than relying on static routing mechanisms.

Expedia Group has launched STAR, an internal platform that leverages LLMs and service telemetry to accelerate production incident investigations. The system utilizes FastAPI, Datadog, Celery, Redis, and Langfuse to structure workflows for root cause assessment while ensuring engineers remain actively involved in the process. This approach aims to reduce mean time to resolution by automating initial telemetry analysis.

• STAR integrates telemetry data with LLMs to generate root cause assessments automatically.
• The architecture relies on FastAPI, Datadog, Celery, Redis, and Langfuse for execution.
• Engineers stay in the loop, using AI outputs to guide manual investigation steps.
• Structured workflows ensure consistent analysis and support faster incident response.
• The tool is designed to handle complex service dependencies via structured AI workflows.