Dr. Claw: An AI Scientist Workspace for Vibe Research

Key points

• Problem: Command-line (CLI) coding agents such as Claude Code and Gemini CLI read and write files and sustain long sessions, yet end-to-end research still splits across chat tools, IDEs (integrated development environments), terminals and writing environments. These agents optimize execution rather than control, so the plan, intermediate decisions and artifacts that make a research process reviewable get scattered or lost, and humans have few explicit takeover points.
• Input and output: Given goals, constraints and acceptance criteria in natural language, the system produces a structured research brief and a dependency-aware task plan, writes execution outputs into the Artifact Store, updates task states in the Task Graph and keeps all interventions in the Decision Log and Execution Trace. This is a system paper: the workspace keeps an existing command-line coding agent as the executor and adds state, control and audit layers on top. The authors call this split, where humans own research direction, evaluation criteria and final acceptance while AI handles retrieval, coding, running, summarizing and drafting, Vibe Research.
• Method: Four state objects (Task Graph, Artifact Store, Decision Log, Execution Trace) form the workflow state, and each interaction is formalized as a state transition from the current state, an action and the observed feedback. The system has three layers (Interaction, Orchestration, Execution) so the executor can be switched without changing workflow semantics or the audit view, and each task goes through Plan, Execute, Verify and Write-back. Skills reach a task through a stage-skill map keyed on the task's stage and type, keyword detection over user instructions, or manual invocation, and every action must belong to the action set allowed by the current permission policy.
• Implementation: The deployed skill catalogue holds 171 skills, of which 58 are mapped to five research stages (survey, ideation, experiment, publication, promotion). Moving to a new domain means editing one JSON map instead of code, backends attach through Claude and Codex SDK (software development kit) adapters, and pipeline mutations are written to persistent state first and then broadcast to the interface over WebSocket. Per the official README, the release includes a React and Vite frontend, a Node.js and Express backend, executor integrations for Claude Code, Gemini CLI, Codex and OpenRouter, a Chat flow that generates the research brief and task list with a skill, a Research Lab to review them, and Auto Research for one-click sequential task execution.
• Results: Against a bare agent using the same executor (codex provider, gpt-5.4) on three medical tasks (melanoma and nevus classification on Derm7pt and a clinical-note risk baseline), pooled completeness, the share of 21 research best-practice elements present in the output files, was 0.873 for the bare agent and 0.952 for Dr. Claw (two wins, one tie), and the whole gap came from research hygiene elements (basic parts of a research report such as a limitations section, subgroup analysis and real literature citations), whose pooled pass rates rose from 0.33 to 1.00, 0.33 to 1.00 and 0.00 to 0.67. In a demonstration that injected a wrong-path error into one Derm7pt mini-project, the project recovered to a result with 0.892 accuracy while keeping all 5 earlier files. In a retrospective study of seven AI PhD researchers, Dr. Claw was associated with shorter completion-time bands and the highest output-quality ratings of the three conditions compared with working without AI or with general web and desktop AI assistants, and the experience score difference was significant in every pairwise comparison.
• Limits: The authors call the evaluation a small exploratory pilot with few tasks and participants: with one run per task the pooled completeness gain of +0.079 has a 95% bootstrap CI (confidence interval) of [-0.00, +0.14] that includes zero, and the human study collected the control conditions as retrospective reports in a hybrid design. Holding the executor fixed adds the task graph, state objects and skill library as one bundle, so no single component's effect can be isolated; the comparison target is the bare executor Dr. Claw wraps instead of a state-of-the-art orchestration framework; and completeness counts which of 21 elements are present, so correctness needs separate review. Tasks come from one domain (medical), Dr. Claw was slower than the bare agent, and skill invocation is guaranteed only up to a suggestion in the task prompt, so the reference audit did not fire on the clinical-note task.

Paper links

External research summaries. These are not HDATF publications or measured product results.