DSH Scholar

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DSH Scholar is an AI research workspace for computational research. It keeps project conversations, research materials, code and data, controlled experiment runs, evidence, and TeX manuscripts in one recoverable project. You can start from a new question or continue work that already exists elsewhere.

DSH Scholar is still under active development. Use it for supervised computational-research workflows, review every approval and research claim, and keep independent backups of important source material and results.

What it provides

• Stage-aware research guidance: Chat supports natural conversation, Grill Me intake, file upload, visual-model input, explicit slash commands, and an authoritative next-step prompt for the current research stage.
• Governed research workflow: Scope, Idea, Contract, Evidence, Direction, and Release decisions remain explicit, revision-bound, and auditable.
• Controlled execution: Runner Profiles describe local, local-Docker, or remote-SSH environments, including pinned container images and declared NVIDIA GPU capability.
• Integrated workspace: project-scoped Chat, editable files, session-bound Web terminals, run logs, artifacts, TeX source, compilation diagnostics, and PDF preview share the same context.
• Traceable methodology: Protocol revisions, run classifications, synthesis requests, assurance results, reviewer findings, knowledge-pack activation, and claim-to-evidence links are recorded as durable research state.
• Visible collaboration: Trajectory and Topology expose subagent parent-child relationships, status, follow-ups, and outputs.

Intended use and boundaries

• DSH Scholar assists researchers; it does not assume responsibility for scientific judgment, approval, authorship, or publication.
• gate-only is the normal mode. Agents cannot impersonate a Human principal, fabricate accepted Evidence, or bypass a research Gate.
• full-auto means automatic approval only for the allowlisted Scope, Idea, Contract, and Budget Gates of an exact registered FixtureProfile. Its only canonical action executor is currently survey_run. Release, Direction, Intake, Evidence, and unsupported actions remain Human-controlled or are parked with a typed reason.
• A name-only /new <name> project always starts as gate-only and collects its Brief through Grill Me; it does not silently inherit full-auto.
• Formal experiments must bind immutable code and data snapshots, a frozen Protocol where required, and an explicit Runner Profile. Chat text, ordinary stdout, and Interactive Terminal output do not automatically become formal Evidence.
• Images sent to a visual model are untrusted, current-turn Chat context. They do not automatically become OCR output, Brief answers, Evidence, Claims, Gate decisions, or proof that a command ran.
• The product focuses on computational research such as machine learning, data science, and bioinformatics. It is not intended for clinical decisions, human studies, wet-lab work, or other high-risk research.

Quick start

The local workspace requires Linux, Node.js 24, pnpm 11.20.0, and Docker Engine for controlled experiments, TeX compilation, and clean-room reproduction.

1. Install and build

pnpm install --frozen-lockfile
pnpm run build

2. Start the standalone workspace

bash scripts/start-standalone-ui.sh

Open http://127.0.0.1:18610 and paste the token from:

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