Latent Tools

Expand concepts into rich conceptual landscapes for research, context priming, and cross-domain exploration.

Each model interaction is a big bang into a dark forest — every concept in the context window illuminates paths the model can explore. Latent tools give your agents a flashlight.

latent.expand@1

Expand a seed concept via depth, breadth, or bridge mode.

Parameters

Modes

Depth — follow one conceptual path deep. Hierarchical decomposition, domain-specific formalization, edge cases, failure modes, historical evolution. Narrow but thorough.

Breadth — shine light in all directions. Find isomorphic structures across domains, orthogonal dimensions, cross-disciplinary connections, contrasts and inversions. Wide and surprising.

Bridge — given two concepts, find shared abstractions, transformation paths, and unexpected connections. Where the analogy breaks is often as valuable as where it holds.

Example: Breadth Expansion

{
  "name": "data-grout@1/latent.expand@1",
  "arguments": {
    "seed": "cache invalidation",
    "mode": "breadth",
    "levels": 2
  }
}

Response (summary format):

{
  "seed": "cache invalidation",
  "mode": "breadth",
  "format": "summary",
  "expansion": "Cache invalidation is isomorphic to: (1) consensus problems in distributed systems — when does a replica know its state is stale? (2) memory reconsolidation in neuroscience — retrieved memories become labile and must be restabilized. (3) legal precedent override — when a new ruling invalidates cached interpretations of prior law...",
  "concepts": ["distributed consensus", "memory reconsolidation", "legal precedent", "eventual consistency", "signal propagation"],
  "dimensions_explored": 8,
  "llm_credits": 5,
  "model": "gpt-4.1",
  "levels": 2,
  "_meta": { "cache_ref": "rc_abc123" }
}

Example: Bridge

{
  "name": "data-grout@1/latent.expand@1",
  "arguments": {
    "seed": "version control",
    "target": "memory reconsolidation",
    "mode": "bridge"
  }
}

Example: Graph Format

{
  "name": "data-grout@1/latent.expand@1",
  "arguments": {
    "seed": "cache invalidation",
    "mode": "breadth",
    "format": "graph",
    "levels": 1
  }
}

Response (graph format):

{
  "seed": "cache invalidation",
  "mode": "breadth",
  "format": "graph",
  "nodes": [
    { "id": "n1", "label": "distributed consensus", "domain": "systems", "relation": "isomorphic", "weight": 0.9, "description": "Both solve 'when is local state stale?'" },
    { "id": "n2", "label": "memory reconsolidation", "domain": "neuroscience", "relation": "isomorphic", "weight": 0.7, "description": "Retrieved memories become labile" }
  ],
  "edges": [
    { "from": "seed", "to": "n1", "type": "shares_structure", "description": "Staleness detection" },
    { "from": "n1", "to": "n2", "type": "analogous", "description": "Digital and biological freshness" }
  ],
  "summary": "Cache invalidation maps structurally to consensus and memory reconsolidation.",
  "node_count": 2,
  "edge_count": 2,
  "_meta": { "cache_ref": "rc_def456" }
}

Cost

Tips

• For context priming: use summary format and inject the expansion text into your agent’s system prompt or context window.
• For programmatic use: use graph format to get structured nodes and edges you can filter, traverse, or visualize.
• Start with level 1 for quick exploration, then increase levels if you need more depth.
• Use domain_hint to steer the expansion toward a specific field when you know where you want to look.
• Bridge mode is powerful for finding connections between seemingly unrelated domains — useful for research, design, and lateral thinking.

When to use Latent