EAGLE 3.1 Fixes Attention Drift in LLM Inference

The 'EAGLE 3.1' algorithm addresses 'attention drift' in LLM inference using FC normalization and post-norm hidden-state feedback, applicable to vLLM.

Wed May 27 2026

Key Functionality of 'EAGLE 3.1'

EAGLE 3.1 is a speculative decoding algorithm developed to resolve the 'attention drift' phenomenon occurring during large language model (LLM) inference. This algorithm primarily utilizes FC normalization and post-norm hidden-state feedback techniques. Its aim is to enhance the stability and efficiency of LLM inference.

Application to vLLM and Expected Impact

This technology is slated for application in vLLM. 'Attention drift' refers to the degradation of a model's attention to prior information when processing long sequences, which can lead to performance issues in LLMs. By improving these problems, EAGLE 3.1 is expected to increase the inference accuracy of vLLM-based LLMs and contribute to providing more stable services.

*Source: MarkTechPost (2026-05-27)*

Tags: #ai #LLM #speculative-decoding #attention-drift #tech