Operationalizing LLM-style Context Engineering and Reasoning in Industrial Ranking

Establish practical methodologies to operationalize LLM-style context engineering and multi-step reasoning within industrial ranking systems that lack prompt-style contexts and chain-of-thought supervision, ensuring these mechanisms can be effectively applied in both retrieval and ranking stages of cascaded pipelines.

Background

LLMs benefit from prompt-style contexts and chain-of-thought supervision to elicit strong reasoning capabilities. In contrast, industrial ranking systems (e.g., search and recommender cascades) do not naturally provide such supervision or prompts, creating a gap in directly transferring LLM mechanisms to this domain.

The paper introduces OnePiece as a unified framework to integrate structured context engineering and block-wise latent reasoning, proposing specific tokenization and progressive multi-task training. This open question highlights the broader methodological challenge of adapting these LLM mechanisms to industrial ranking where conventional supervision and input formats differ substantially.