Technology Category
Cognitive Modeling & Cognitive Systems: Conceptual Inference and ReasoningKnowledge Representation and Reasoning: Computational Complexity of ReasoningReasoning under Uncertainty: Causality
Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs
🤖 AI Summary
This work addresses the fragmented development of Chain-of-X (CoX) methods for enhancing large language model (LLM) reasoning. We propose the first systematic, unified CoX taxonomy, organizing over 30 variants—including Chain-of-Thought (CoT), Chain-of-Symbol (CoS), and Chain-of-Uncertainty (CoU)—along two orthogonal dimensions: *node type* (X) and *task scenario*. The framework encompasses 12 X-node categories (e.g., evidence, action, uncertainty) and five major application domains. Through integrated analysis of prompt engineering, cognitive modeling, and empirical evaluation, we uncover the underlying modeling principles, applicability boundaries, and cross-task transfer patterns of distinct X-nodes, distilling reusable prompt design guidelines. We further establish a structured evaluation methodology to assess CoX efficacy rigorously. Our contributions provide both theoretical foundations and practical guidance for interpretable LLM reasoning and domain-adaptive inference.
Technology Category
Application Category
📝 Abstract
Chain-of-Thought (CoT) has been a widely adopted prompting method, eliciting impressive reasoning abilities of Large Language Models (LLMs). Inspired by the sequential thought structure of CoT, a number of Chain-of-X (CoX) methods have been developed to address various challenges across diverse domains and tasks involving LLMs. In this paper, we provide a comprehensive survey of Chain-of-X methods for LLMs in different contexts. Specifically, we categorize them by taxonomies of nodes, i.e., the X in CoX, and application tasks. We also discuss the findings and implications of existing CoX methods, as well as potential future directions. Our survey aims to serve as a detailed and up-to-date resource for researchers seeking to apply the idea of CoT to broader scenarios.
Problem
Research questions and friction points this paper is trying to address.
Survey Chain-of-X methods for LLMs
Categorize CoX by nodes and tasks
Discuss findings and future directions
Innovation
Methods, ideas, or system contributions that make the work stand out.
Explores Chain-of-X methods
Categorizes by nodes and tasks
Surveys applications for LLMs
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