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Implicit Reasoning in Large Language Models: A Comprehensive Survey

Paper · 2025 · Jindong Li, Yali Fu, Li Fan et al.

Summary

Chain-of-Thought prompting makes LLMs write out every intermediate step, which inflates output length, latency and cost. This survey covers a growing alternative, implicit reasoning, in which a model carries out multi-step inference within its hidden states or layers instead of producing textual traces. Earlier reviews mostly concentrated on explicit reasoning or grouped latent methods by representation type. The authors instead propose an execution-centred taxonomy that asks how and where the internal computation actually happens. They sort the literature into three paradigms: latent optimization (at token, trajectory and internal-state granularity), signal-guided control (inserting special tokens of one or several kinds), and layer-recurrent execution (reusing transformer blocks in loops). They also collect structural, behavioral and representation-level evidence that LLMs genuinely reason silently, and they catalogue the metrics and benchmarks in use. No new model or dataset is introduced. The contribution is a unified map of a fragmented field plus a list of open problems, such as opacity, an accuracy gap versus explicit CoT, and inconsistent evaluation.

Key points

  • Three paradigms, with representative methods:
    • Latent optimization: Coconut, CODI, CCoT, LightThinker, SoftCoT.
    • Signal-guided control: Pause Tokens, Filler Tokens, Planning Tokens, Quiet-STaR.
    • Layer-recurrent execution: looped Transformer, CoTFormer, Huginn, ITT.
  • Three kinds of evidence for silent reasoning:
    • Layer-wise structural evidence, e.g. Jump to Conclusions and Internal Chain-of-Thought.
    • Behavioral signatures, e.g. Grokked Transformer and step-skipping.
    • Representation-based analysis, e.g. MechanisticProbe, steering vectors and backward chaining circuits.
  • Benchmarks in five groups: commonsense, math/programming, language modeling and reading comprehension, multi-hop QA, and multimodal tasks. More than 70 datasets appear across studies, often as self-curated subsets, even though shared sets like GSM8K, CommonsenseQA and HotpotQA exist.
  • Six listed challenges: opacity, weak controllability, a performance gap behind CoT, no standard evaluation, architecture and scale constraints (mostly small models), and reliance on explicit CoT supervision during training.
  • Living resource: a maintained paper list at github.com/digailab/awesome-llm-implicit-reasoning.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Vision and Pattern Recognition · 2,869 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksReasoning · 1,426 works

Cite

Cite as Jindong Li, Yali Fu, Li Fan, Jiahong Liu, Yao Shu, Chengwei Qin, Menglin Yang, Irwin King and Rex Ying. 2025. Implicit Reasoning in Large Language Models: A Comprehensive Survey. arXiv:2509.02350.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2509.02350 · arXiv 2509.02350

@misc{li2025implicit,
  title = {{Implicit Reasoning in Large Language Models: A Comprehensive Survey}},
  author = {Jindong Li, Yali Fu, Li Fan, Jiahong Liu, Yao Shu, Chengwei Qin, Menglin Yang, Irwin King and Rex Ying},
  year = {2025},
  doi = {10.48550/arxiv.2509.02350},
  eprint = {2509.02350},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2509.02350}
}

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