Implicit Reasoning in Large Language Models: A Comprehensive Survey

2025-09-02

AI Summary

Existing research lacks a mechanistic, system-level analysis of implicit reasoning in large language models (LLMs). This paper introduces the first taxonomy of implicit reasoning centered on *execution paradigms*, departing from conventional representation-based analyses and instead characterizing reasoning strategies computationally. We categorize implicit reasoning into three types: latent optimization, signal-guided control, and layer-wise recurrent execution. Leveraging intermediate-layer activation analysis, dynamic signal modulation, intra-layer recursive modeling, and behavioral interpretability experiments, we integrate structural, behavioral, and representational evidence to uncover how reasoning “silently unfolds” within internal representations. Furthermore, we establish a systematic evaluation framework covering mainstream benchmarks and low-latency inference metrics. All code and resources are publicly released and actively maintained.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Knowledge Representation and Reasoning: Reasoning with Beliefs

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Large Language Models (LLMs) have demonstrated strong generalization across a wide range of tasks. Reasoning with LLMs is central to solving multi-step problems and complex decision-making. To support efficient reasoning, recent studies have shifted attention from explicit chain-of-thought prompting toward implicit reasoning, where reasoning occurs silently via latent structures without emitting intermediate textual steps. Implicit reasoning brings advantages such as lower generation cost, faster inference, and better alignment with internal computation. Although prior surveys have discussed latent representations in the context of reasoning, a dedicated and mechanism-level examination of how reasoning unfolds internally within LLMs remains absent. This survey fills that gap by introducing a taxonomy centered on execution paradigms, shifting the focus from representational forms to computational strategies. We organize existing methods into three execution paradigms based on extbf{ extit{how and where internal computation unfolds}}: latent optimization, signal-guided control, and layer-recurrent execution. We also review structural, behavioral and representation-based evidence that supports the presence of implicit reasoning in LLMs. We further provide a structured overview of the evaluation metrics and benchmarks used in existing works to assess the effectiveness and reliability of implicit reasoning.We maintain a continuously updated project at: https://github.com/digailab/awesome-llm-implicit-reasoning.

Problem

• Examining how reasoning occurs internally within LLMs
• Introducing taxonomy focused on computational execution paradigms
• Reviewing evidence supporting implicit reasoning in LLMs

Innovation

• Latent optimization for silent reasoning
• Signal-guided control of internal computation
• Layer-recurrent execution paradigm implementation