{
  "name": "Implicit Chain-of-Thought research ontology",
  "version": "0.9.4",
  "updated_at": "2026-10-18",
  "umbrella_term": {
    "id": "implicit-chain-of-thought",
    "label": "implicit chain-of-thought",
    "short_label": "implicit CoT",
    "definition": "Intermediate reasoning carried out without writing every step as natural language.",
    "related_terms": ["latent chain-of-thought", "latent reasoning", "continuous thought", "hidden-state reasoning", "recurrent reasoning"],
    "usage_note": "Related terms are not necessarily synonyms. Individual papers retain their original titles and terminology."
  },
  "scope": {
    "core_rule": "Include work that studies intermediate computation which substitutes for, compresses, internalizes, or changes the schedule of an explicit reasoning trace; general representation learning alone is insufficient.",
    "adjacent_rule": "Include a separately labeled precursor when it provides essential architectural or theoretical context without directly studying implicit chain-of-thought.",
    "coverage_claim": "Curated broad coverage, not exhaustive coverage.",
    "search_refreshed_at": "2026-10-18",
    "latest_included_first_public": "2026-10-15"
  },
  "date_policy": {
    "field": "first_public",
    "definition": "Date of the earliest public version verified during curation, usually the arXiv v1 submission date, not an independently verified announcement timestamp. Earlier conference presentations carry a source note. citation_date separately dates the linked arXiv version used by citation exports.",
    "sort_order": "ascending by first_public, then stable record identifier"
  },
  "research_lenses": [
    {"id": "internalized", "label": "Internalized reasoning", "definition": "Learn from worked solutions so that some or all of the reasoning can later happen without writing those steps. Training may use a separate teacher model or progressively remove written steps from the model being trained.", "guiding_question": "How can an explicit chain of thought be distilled or progressively removed?", "related_terms": ["implicit chain-of-thought","internalized chain-of-thought","reasoning distillation"], "url": "https://latent-implicit-thinking.github.io/topics/internalized-reasoning/"},{"id": "learned-tokens", "label": "Thinking tokens", "definition": "Give a model extra tokens for computation, such as pauses, filler symbols, or learned planning tokens. These tokens need not spell out the solution.", "guiding_question": "Can non-readable token positions provide useful computation or planning?", "related_terms": ["pause tokens","filler tokens","planning tokens","compressed tokens"], "url": "https://latent-implicit-thinking.github.io/topics/thinking-tokens/"},{"id": "continuous", "label": "Continuous-state reasoning", "definition": "Use vectors—lists of numbers—to carry intermediate reasoning from one step to the next, instead of selecting a text token at every step. Some methods use the model's hidden states; others combine several candidate token representations.", "guiding_question": "What changes when intermediate reasoning remains in a dense representation?", "related_terms": ["continuous chain-of-thought","continuous thought","hidden-state reasoning","latent-space reasoning"], "url": "https://latent-implicit-thinking.github.io/topics/continuous-states/"},{"id": "recurrent", "label": "Recurrent reasoning", "definition": "Repeat parts of the model or keep intermediate information in memory while working through a problem. Some methods also learn how many passes to run.", "guiding_question": "Can a model adapt how much internal depth it spends on each problem?", "related_terms": ["recurrent reasoning","looped transformers","adaptive computation","stateful reasoning"], "url": "https://latent-implicit-thinking.github.io/topics/recurrent-reasoning/"},{"id": "beyond-sequential", "label": "Alternative generation methods", "definition": "Change how intermediate steps are generated, revised, or searched—for example, by refining several steps in parallel. These are related approaches: some still produce written chains of thought.", "guiding_question": "What becomes possible beyond strictly left-to-right generation?", "related_terms": ["parallel reasoning","diffusion reasoning","non-autoregressive reasoning","next-block prediction"], "url": "https://latent-implicit-thinking.github.io/topics/alternative-reasoning-schedules/"},{"id": "analysis", "label": "Analysis and evaluation", "definition": "Measure how well these methods work, investigate what their internal computation does, and identify where they fail. This includes experiments, studies of model internals, and mathematical results.", "guiding_question": "What computation do these systems perform, and how can we tell?", "related_terms": ["reasoning evaluation","mechanistic interpretability","faithfulness","latent reasoning analysis"], "url": "https://latent-implicit-thinking.github.io/topics/implicit-reasoning-evaluation/"}
  ],
  "axes": {
    "representation": {
      "definition": "The form that carries intermediate information.",
      "values": ["cache-memory", "continuous-state", "discrete-token", "hidden-state", "multimodal-latent", "survey", "text-latent-hybrid"]
    },
    "computation": {
      "definition": "How intermediate computation is scheduled or repeated.",
      "values": ["depth-wise", "diffusion", "hierarchical", "parallel", "recurrent", "search", "stateful", "survey", "token-wise"]
    },
    "training": {
      "definition": "The principal supervision, adaptation, or inference procedure used in the paper.",
      "values": ["analysis-only", "curriculum", "distillation", "inference-only", "pretraining", "reinforcement-learning", "self-distillation", "self-training", "supervised", "test-time-optimization"]
    },
    "questions": {
      "definition": "The research questions directly investigated.",
      "values": ["architecture", "capability", "controllability", "efficiency", "evaluation", "generalization", "interpretability", "limitations", "scaling", "survey", "taxonomy", "theory"]
    },
    "evidence": {
      "definition": "The type of support offered for the paper's claims.",
      "values": ["boundary", "empirical", "formal", "mechanistic", "probing", "scaling", "survey"]
    },
    "modality": {
      "definition": "The problem or data modality studied.",
      "values": ["language", "multimodal", "symbolic", "vision"]
    }
  }
}