| name | knowledge_sememolution_skill |
| description | Skill for population-based Knowledge Graph evolution via Sememolution. Use this when the user wants creative cross-domain synthesis, speculative reasoning, or when a single KG search might be too narrow.
Sememolution maintains a population of KG "individuals". Each individual has its own graph (different facts, concepts, links) and its own genome controlling how it searches and evolves.
Core genome parameters: - `lambda_depth` — Poisson rate for search traversal depth - `lambda_breadth` — Poisson rate for search breadth per step - `sleep_ops` — which refinement ops to apply during sleep - `dream_probability` — chance of speculative synthesis per cycle
Workflow: 1. Create a population: `SememolutionPopulation(model, provider, population_size=100, sample_size=10)` 2. Initialize: `pop.initialize()` 3. Assimilate text: `pop.assimilate_text(chunk)` — each individual absorbs it differently 4. Sleep cycle: `pop.sleep_cycle()` — each individual prunes/deepens independently 5. Query and rank: `pop.query_and_rank(question)` — sample individuals, each searches
its own graph with Poisson-sampled depth/breadth, generates a response,
and responses are ranked. Winners get fitness bumps.
6. Evolve: `pop.evolve_generation()` — tournament selection, crossover, mutation.
When to use this: - The user asks open-ended "what if" or "how might X relate to Y" questions - You need diverse perspectives on the same knowledge corpus - You want to discover non-obvious connections across domains - Standard KG search returns shallow or overly literal results
Important: this is computationally expensive. Only invoke after checking whether standard keyword/embedding/hybrid search is sufficient. |
knowledge_sememolution_skill
Skill for population-based Knowledge Graph evolution via Sememolution. Use this when the user wants creative cross-domain synthesis, speculative reasoning, or when a single KG search might be too narrow.
Sememolution maintains a population of KG "individuals". Each individual has its own graph (different facts, concepts, links) and its own genome controlling how it searches and evolves.
Core genome parameters: - lambda_depth — Poisson rate for search traversal depth - lambda_breadth — Poisson rate for search breadth per step - sleep_ops — which refinement ops to apply during sleep - dream_probability — chance of speculative synthesis per cycle
Workflow: 1. Create a population: SememolutionPopulation(model, provider, population_size=100, sample_size=10) 2. Initialize: pop.initialize() 3. Assimilate text: pop.assimilate_text(chunk) — each individual absorbs it differently 4. Sleep cycle: pop.sleep_cycle() — each individual prunes/deepens independently 5. Query and rank: pop.query_and_rank(question) — sample individuals, each searches
its own graph with Poisson-sampled depth/breadth, generates a response,
and responses are ranked. Winners get fitness bumps.
6. Evolve: pop.evolve_generation() — tournament selection, crossover, mutation.
When to use this: - The user asks open-ended "what if" or "how might X relate to Y" questions - You need diverse perspectives on the same knowledge corpus - You want to discover non-obvious connections across domains - Standard KG search returns shallow or overly literal results
Important: this is computationally expensive. Only invoke after checking whether standard keyword/embedding/hybrid search is sufficient.
Inputs
name (default: 'task')description (default: 'initialize | assimilate | query_rank | evolve | sleep')name (default: 'population_size')description (default: 'Number of individuals (default 100)')name (default: 'query_text')description (default: 'Question to ask the population (for query_rank)')
Steps
Usage
/run_jinx jinx_ref=knowledge_sememolution_skill input_values={"name": "query_text", "description": "Question to ask the population (for query_rank)"}
