dsh-scholar

DSH Scholar

简体中文 | English

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, author

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

2. Start the standalone workspace

Open and paste the token from:

The standalone workspace and DSH use the same Research Kernel at 127.0.0.1:7412 and the same canonical project data directory at ~/.dsh/research-kernel. Upgrading either surface must keep that directory unchanged so existing projects remain accessible. Browser tokens and display preferences live separately in the standalone BFF directory. Use --no-token only on an isolated, supervised, loopback-only development instance. 3. Configure an execution environment

Open Settings → Execution environment and select an explicit Runner Profile: local machine for trusted development and smoke checks; local Docker with a pinned image, optionally requiring the NVIDIA runtime and GPU capability; remote SSH with server-side endpoint, credential, k