specific method · filed under data curation
Knowledge-graph-guided task synthesis
A self-evolving hierarchical knowledge graph, expanded through web-scale exploration, guides task synthesis.
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How sources treat it
One count per evidence span, weakest treatment to strongest.
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Evidence
1 span quoted from the sources, strongest treatment first.
To control both granularity and coverage at scale, we build a self-evolving, hierarchically organized knowledge graph that agents continuously expand through web-scale exploration across knowledge-intensive and coding domains.
usedunclearin Kimi K3Moonshot AI
Filed alongside
Other methods under data curation :: synthetic data.
Agentic data synthesis pipelineKnowledge distillation for synthetic dataAutomated data synthesis and environment-construction pipelinesAutomatic synthesis of task-oriented RL environmentsContainerized coding-environment construction with self-testing and trace removalCounterfactual data augmentationRecycling filtered documents into image-text pairsAutomated batch synthesis of RL training dataCoding environments from GitHub issue–PR pairsContainer buildability and verifiability checkCorpus rephrasing with fidelity verificationDynamic multi-agent synthetic-data generation loopEnd-to-end synthetic environment generationEnvironment, toolset, task, and solution synthesis pipelineFailure-case and negative-feedback-driven environment generationHeterogeneous answer-generation agentsIterative task-difficulty escalationLarge-scale environment synthesis and curationLong-context data synthesis by permutation and concatenationMatching synthesis-pipeline complexity to teacher capabilityMetadata-conditioned synthetic generationMocked tools for agent environmentsModular synthetic-data pipeline compositionMulti-agent collaborative environment construction