clearai-dsh

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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) — a disciplined seven-stage path: frame, hypothesize, plan, observe, verify, evaluate, record. 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.

Left: the Epistemic Loop — seven stages. 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.

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:

Inside the ring is the instrument's read-out: the L0–L4 axis, the pre-registered threshold as a dashed line, and five observations with error bars — the supported one filled, the inconclusive drawn as a dashed circle, the refuted left in place with a slash through it (nothing is deleted). The emerald dot at the opening is the one reading that crossed the threshold and settled as a fact.

At runtime, the seven stages compress into four beats — plan, execute, observe, reflect. State is derived from the session record with no second store; the tools the model holds contain no field in which it could declare a step complete.

ClearAI does not claim recursive self-improvement. It provides the epistemic substrate a self-improving system would need. See Positioning and the OpenRSI survey.

Install and use

npx clearai-dsh install

The installer's output follows your system language (--lang zh|en overrides it, doctor / seed / unseed take the same flag). Its only runtime dependency is zod; the graph stack is bundled into the client half at build time.

Restart dsh web afterwards (npx @deepseek-ai/dsh web), then create a session and switch to the ClearAI mode in the picker at the top:

1. Open dsh web and click "New session";
2. Click the current mode name at the top (default: Standard mode) to open the preset list;
3. Pick ClearAI — its card reads "利用认识论循环构建可信本体。Build a trustworthy ontology through the epistemic loop.";
4. Just ask your question. Ordinary Q&A runs as usual; once you set a goal and register hypotheses, the system enters knowledge mode by itself: known facts come to you, gaps stay visible, and conclusions earn their place.

The ontology graph in ClearAI mode — this real session grew 21 concepts and 9 predicates; the same ledger always yields the same picture. (UI shown is Chinese.)

If pnpm is not on PATH: npm install -g pnpm (do not corepack enable — it installs a version forwarder that may download a pnpm it cannot launch).

From the repository:

npm test                       # 15 suites
node tools/build-package.mjs   # assemble dist/ from source
node tools/verify-package.mjs  # rebuild on the spot, byte-compare
node docs/diagrams/build-hero.mjs   # redraw the product hero (needs google-chrome)

dist/ is generated and never committed. See DSH integration.

What it looks like

The middle column has two switchable views: Deliverables and Ontology. The right sidebar: Worldlines and External Brain.

Ontology — this view is your knowledge home. At the top, a graph band: the ontology graph (what your domain looks like) and the entity graph (what you have actually verified) toggle with one click; clicking a node or edge opens the knowledge inspector (definition / relations / assertions / evidence chain / history), and "filter by this" is an explicit action inside the detail view. Below that, the ontology shelf: established entries, each with assertion chips (click to see what the term means), boundary, and support level; contradictions surface automatically. The vocabulary maintenance block sits collapsed at the bottom — it auto-expands when a language exists before any sentence does.

The band — the ontology graph and the entity graph share one deterministic projection, so the same ledger always yields the same picture (captured from a real session: 21 concepts, 9 predicates).

Open any node or edge: definition, relations, assertions, evidence chain, registration and revision history, all in one place. (UI shown is Chinese.)

Worldlines — when two routes genuinely disagree, they run as separate branches with their own readings; the losing one stays on record, and adoption is a human press.

Deliverables — the middle column keeps "what the plan declared" and "what actually exists on disk" apart.

External Brain — skills and memory as DSH-native entries in one merged catalogue.

Cases

• Physical-world process experiment — sensor thermal drift: the full chain from raising terms to a conflict surfacing
• AI for Science — convergence order of WENO reconstructions, and what "we could not resolve it" honestly means
• Mathematics — keeping finite numerical evidence strictly separate from proof

Documentation

• Positioning · Domain ontology design
• Epistemic loop · Verification ontology · Loop philosophy
• Design principles · Soul map · Glossary
• Knowledge-native loop ledger (this round: triage / preflight / knowledge gate / graph / inspector; zh-CN)
• Development plan (with real-run evidence from the Hengtong project)
• Known gaps · Authority map · Release verification

Where it sits in DSH

ClearAI adds the epistemic layer on DSH's composition plane — one host package, one agent preset, one client module, with zero changes to the DSH engine. Working style is unrestricted, but nothing outside the governed path can write to the authoritative ledger (pinned by tests).

Work attribution

This project's work attribution unit is Jidian Qiyuan.

Star History

License

Apache-2.0, see LICENSE.

Status

A local-first ontology discovery and exploration platform delivered as a DSH plugin. What is not yet implemented, and what has not been verified in a real browser, is written in Known gaps.