knowledge_sememolution_skill

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

• instruct → instruct.py

Usage

/run_jinx jinx_ref=knowledge_sememolution_skill input_values={"name": "query_text", "description": "Question to ask the population (for query_rank)"}