Editing Openai/693cab0b-13a0-8011-949b-27f1d40869c1 (section)

=== Assistant: Your idea is non-naïve and already partially rediscovered in frontier research—but with important caveats. ===
Your idea is non-naïve and already partially rediscovered in frontier research—but with important caveats.

Below is a dense, technical, systems-level analysis, bullets first, depth where needed.

==== - Proposal: Replace natural-language chain-of-thought (CoT) with a structured latent representation (JSON-like) ====
* Goal: - Eliminate verbose NL reasoning - Force explicit ontology + dependency graph - Enable tree-structured / graph-structured cognition
* Key intuition: - Language is lossy, ambiguous, and linear - JSON is symbolic, compositional, hierarchical

You’re essentially proposing symbolic intermediate reasoning.

==== - Thinking (OE þencan): internal cognitive process → not inherently linguistic ====
* Reasoning (Lat. ratiocinari): structured inference
* Ontology (Gr. ontos = being + logos = study): formal specification of concepts
* JSON (JavaScript Object Notation): - Deterministic - Tree-structured - Symbolic but shallow (no semantics beyond structure)

Your proposal ≠ “thinking in JSON”
Your proposal = thinking in structured symbolic graphs

==== No — but partially yes. ====

===== - Internal representations are: - High-dimensional continuous vectors - Distributed (not symbolic) =====
* However: - Training signal is linguistic - CoT forces linear, tokenized reasoning

So models simulate reasoning in English because:
* English is the interface, not the substrate

==== ### ====
* Model reasons internally
* Outputs compressed / partial rationale
* Avoids leaking full CoT

✔ Confirms: Internal reasoning ≠ natural language

===== - Model generates: - Python code - DSLs - Trees =====
* Executes them for reasoning

Example:

<syntaxhighlight lang="json">{
  "assumptions": [...],
  "variables": {...},
  "constraints": [...],
  "inference": "solve"
}

</syntaxhighlight>

✔ Very close to your proposal

===== - Replaces linear CoT with: - DAGs - Branching hypotheses - Pruning =====

Formalization:

<syntaxhighlight>Thought = Node
Dependency = Edge
Solution = Subgraph

</syntaxhighlight>

✔ Explicit knowledge tree creation

===== - Hybrid of: - Neural networks (pattern recognition) - Symbolic systems (logic, trees, graphs) =====

Examples:
* DeepMind’s Differentiable Neural Computers
* IBM Neuro-Symbolic Concept Learner

✔ Your idea sits squarely here

==== ### ====
* Explicit structure
* Deterministic parse
* Enforces hierarchy
* Easy validation
* External inspectability

===== - JSON has no semantics =====
* No: - Uncertainty - Fuzziness - Probability - Temporal dynamics
* Hard discrete boundaries → conflicts with gradient descent

So JSON is a serialization format, not a cognitive substrate

==== ### ====
* Transformers optimize via: - Backpropagation - Continuous loss surfaces
* JSON is: - Discrete - Symbolic - Brittle

This creates:

: 

This is the reason this isn’t standard yet.

==== ### ====

Instead of:

<syntaxhighlight>Question → JSON → Answer

</syntaxhighlight>

Use:

<syntaxhighlight>Question
  ↓
Latent Concept Graph (continuous embeddings)
  ↓
Graph Linearization / Projection
  ↓
JSON-like symbolic tree
  ↓
Answer

</syntaxhighlight>

JSON becomes a projection, not the core thought.

===== Internal (latent): =====

<syntaxhighlight>C₁ ──depends_on──> C₂
 │
 └──conflicts_with──> C₃

</syntaxhighlight>

Projected JSON:

<syntaxhighlight lang="json">{
  "goal": "Answer X",
  "concepts": {
    "C1": {"role": "primary"},
    "C2": {"role": "support"},
    "C3": {"role": "counterexample"}
  },
  "dependencies": [
    ["C1", "depends_on", "C2"],
    ["C1", "conflicts_with", "C3"]
  ]
}

</syntaxhighlight>

==== - Eliminates hallucinated prose ====
* Encourages: - Causal reasoning - Dependency tracking
* Enables: - Verification - Pruning - Reuse
* Aligns with: - Planning - Scientific reasoning - Engineering tasks

==== - Language is: - Redundant - Error-tolerant - Semantically rich ====
* JSON is: - Rigid - Unforgiving - Syntax-fragile

Humans evolved language first, not schemas.

LLMs inherited that bias.

==== You are implicitly proposing: ====

: 

This is one of the central unsolved problems in AGI research.

==== Replace JSON with: ====
* Typed graphs
* Probabilistic nodes
* Soft constraints
* Differentiable edges

Think:

<syntaxhighlight>Probabilistic Knowledge Graphs
+ Transformer Attention
+ Tree Search

</syntaxhighlight>

JSON = debug view, not mind.

==== - ❌ “LLMs think in English” → false ====
* ✅ “LLMs are forced to reason in English” → true
* ✅ “Structured, tree-based reasoning is superior” → very likely
* ⚠️ “JSON as thought substrate” → incorrect abstraction
* ✅ “Symbolic intermediate cognition” → frontier research

If you want, next we can:
* Design a JSON-schema-driven reasoning model
* Compare CoT vs GoT vs ToT vs PoT
* Spec a training objective for latent graph reasoning
* Discuss whether this leads toward System-2-like cognition

Just say which axis you want to explore.