clearai-dsh

<p align="center"><b>English</b> · <a href="README.zh-CN.md">中文</a></p>

Your research, grown into an ontology.

ClearAI is an ontology discovery and exploration platform, built on two core concepts:

• Domain ontology (what you get) — your project's own vocabulary, the knowledge entries established through the loop, and their graphs. At the end of a research session you hold a continuously growing knowledge structure, retrievable next round by concept.
• Epistemic loop (how you get it) — question → ontology (how the relevant quantities are measured and how they affect each other) → candidate hypotheses with their predictions → a test that could fail → evidence → bounded conclusion → grows into the ontology. Every edge is tested by evidence and independent evaluation.

Other knowledge graphs pile up edges by extraction and assertion; here every edge has to be earned through the loop.

# Install (npm package, prebuilt — no build step, no allowBuilds prompt)
dsh plugin --profile web add [email protected]
# or in the app: Plugins → Add plugin → [email protected]

Restart dsh web, then pick ClearAI in the preset picker at the top of a new session. That is the whole setup. Full install notes ↓

Left: the Epistemic Loop. Its emerald fact dot is also the first node of the domain ontology on the right. Right: the ontology graph — dark is a concept, light is a value form, emerald an instance; the instance carries two contradictory assertions — the two readings are tinted amber, marking that they do not agree. The system reports the conflict; retracting or keeping is a human decision.

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What you get: a domain ontology

A domain ontology that grows as you research:

• Vocabulary — the language your project speaks: concepts, predicates, value forms, units. Conventions themselves carry no truth value; sentences written with them do.
• Established entries — knowledge that passed the loop: each with its boundary, support level, and evidence chain. Each entry states its boundary explicitly, so it can be cited safely.
• Ontology graph and entity graph — what your domain looks like (structure), and what you have actually verified (the state of play).
• Conflict readings — contradictory conclusions surface automatically; the system reports them, and retracting or keeping is your decision.

How you get it: the Epistemic Loop

Most agent loops track one thing: whether the task is done. The Epistemic Loop also tracks what makes a conclusion trustworthy:

	Task loop	Epistemic loop
Driving question	What next?	What do we know, and on what grounds?
Completion	The model declares it	The system computes it from delivered evidence
Verdict	Whoever did it, says so	Separated — above a level, the doer cannot judge themselves
Failure	Deleted, retried, forgotten	Kept: a refuted hypothesis is a result, not noise
Stopping	The model feels it is done	Every candidate and anomaly that could change the answer is handled, or written into the open points
What accumulates	A chat transcript	An ontology: every edge earned through the loop

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