Clearailhc/clearai-dsh
ClearAI:以本体为纲的原生 DSH 插件 —— 经证据核验的研究长成可浏览的领域本体。 · ClearAI: an ontology-first DSH agent preset — evidence-checked research that grows into a domain ontology.
What it solves
ClearAI is designed to move beyond simple chat transcripts in AI research. It prevents the common issue of "hallucinations" or unverified assertions by requiring every piece of knowledge to be earned through a rigorous verification process called the Epistemic Loop. Instead of just piling up edges in a knowledge graph, it ensures that every conclusion is backed by evidence and independent evaluation, creating a trustworthy domain ontology.
How it works
The system operates through two core mechanisms:
- The Epistemic Loop: A structured research process where a question leads to a judgment (defining what would prove the hypothesis wrong), followed by a test that could fail, the collection of evidence, and finally a bounded conclusion. This is distinct from a standard task loop that only tracks completion; the Epistemic Loop tracks trust and evidence.
- Domain Ontology: The result of the loop. It consists of a vocabulary (concepts, predicates, units), established entries with explicit boundaries and evidence chains, and graphs (ontology and entity graphs) that visualize the structure and state of the domain.
ClearAI functions as a plugin for the DSH framework, adding an epistemic layer to the agent's workflow without changing the DSH engine itself.
Who it’s for
Researchers and users who need a verifiable, structured knowledge base (ontology) grown from AI-driven research sessions, rather than than a linear chat history.
Highlights
- Evidence-Based Growth: Every edge in the knowledge graph must be earned through the loop; nothing is simply asserted.
- Conflict Detection: Contradictory conclusions surface automatically, requiring human decision-making to resolve them.
- Conflict-Aware Graphs: Visualizes the ontology and entity graphs, allowing users to filter by nodes and track the progress of conclusions.
- Independent Evaluation: Separates the "doer" (the agent performing the task) and the "judge" (the evaluator) to ensure conclusions are trustworthy.
- Local-First: Delivered as a DSH plugin that manages knowledge as files under a
clear/directory.
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