@inproceedings{song-etal-2026-dr,
title = "Dr. Claw: An {AI} Scientist Workspace for Vibe Research",
author = "Song, Dingjie and
Zhang, Hanrong and
Liu, Dawei and
Liu, Yixin and
Li, Zongxia and
Yuan, Zhengqing and
Zhang, Siqi and
Zou, Henry Peng and
Yan, Zhiling and
Zhang, Yuxuan and
Ye, Yanfang and
Yu, Philip S. and
Sun, Lichao",
editor = "Agrawal, Sweta and
Abad, Alberto",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-demos.82/",
pages = "868--879",
ISBN = "979-8-89176-461-3",
abstract = "Command-line coding agents (e.g., Claude Code, Gemini CLI) can already read and write files and sustain long sessions, yet end-to-end research still fragments across chat tools, IDEs, terminals, and writing environments, and the decisions that make it auditable are rarely preserved. We present **Dr. Claw**, an open-source workspace that wraps existing coding-agent executors in a *controllable* and *auditable* human-in-the-loop workflow rather than introducing another autonomous agent. Persistent state objects, a reusable skill library, and multi-executor coordination link human decisions to AI execution, turning planning, execution, and writing into one traceable, recoverable loop. We demonstrate Dr. Claw through an interactive three-view scenario and a failure-recovery walkthrough, and evaluate it against a bare command-line agent sharing the same backend executor, so the comparison contrasts the whole orchestration layer (task graph, state objects, and skill library) with the agent it wraps. Holding the executor fixed, Dr. Claw scores higher on research completeness while persisting an auditable, recoverable process trail. Demo access: repository https://github.com/OpenLAIR/dr-claw, released under AGPL-3.0 with GPL-3.0 upstream components."
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<abstract>Command-line coding agents (e.g., Claude Code, Gemini CLI) can already read and write files and sustain long sessions, yet end-to-end research still fragments across chat tools, IDEs, terminals, and writing environments, and the decisions that make it auditable are rarely preserved. We present **Dr. Claw**, an open-source workspace that wraps existing coding-agent executors in a *controllable* and *auditable* human-in-the-loop workflow rather than introducing another autonomous agent. Persistent state objects, a reusable skill library, and multi-executor coordination link human decisions to AI execution, turning planning, execution, and writing into one traceable, recoverable loop. We demonstrate Dr. Claw through an interactive three-view scenario and a failure-recovery walkthrough, and evaluate it against a bare command-line agent sharing the same backend executor, so the comparison contrasts the whole orchestration layer (task graph, state objects, and skill library) with the agent it wraps. Holding the executor fixed, Dr. Claw scores higher on research completeness while persisting an auditable, recoverable process trail. Demo access: repository https://github.com/OpenLAIR/dr-claw, released under AGPL-3.0 with GPL-3.0 upstream components.</abstract>
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%0 Conference Proceedings
%T Dr. Claw: An AI Scientist Workspace for Vibe Research
%A Song, Dingjie
%A Zhang, Hanrong
%A Liu, Dawei
%A Liu, Yixin
%A Li, Zongxia
%A Yuan, Zhengqing
%A Zhang, Siqi
%A Zou, Henry Peng
%A Yan, Zhiling
%A Zhang, Yuxuan
%A Ye, Yanfang
%A Yu, Philip S.
%A Sun, Lichao
%Y Agrawal, Sweta
%Y Abad, Alberto
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-461-3
%F song-etal-2026-dr
%X Command-line coding agents (e.g., Claude Code, Gemini CLI) can already read and write files and sustain long sessions, yet end-to-end research still fragments across chat tools, IDEs, terminals, and writing environments, and the decisions that make it auditable are rarely preserved. We present **Dr. Claw**, an open-source workspace that wraps existing coding-agent executors in a *controllable* and *auditable* human-in-the-loop workflow rather than introducing another autonomous agent. Persistent state objects, a reusable skill library, and multi-executor coordination link human decisions to AI execution, turning planning, execution, and writing into one traceable, recoverable loop. We demonstrate Dr. Claw through an interactive three-view scenario and a failure-recovery walkthrough, and evaluate it against a bare command-line agent sharing the same backend executor, so the comparison contrasts the whole orchestration layer (task graph, state objects, and skill library) with the agent it wraps. Holding the executor fixed, Dr. Claw scores higher on research completeness while persisting an auditable, recoverable process trail. Demo access: repository https://github.com/OpenLAIR/dr-claw, released under AGPL-3.0 with GPL-3.0 upstream components.
%U https://aclanthology.org/2026.emnlp-demos.82/
%P 868-879
Markdown (Informal)
[Dr. Claw: An AI Scientist Workspace for Vibe Research](https://aclanthology.org/2026.emnlp-demos.82/) (Song et al., EMNLP 2026)
ACL
- Dingjie Song, Hanrong Zhang, Dawei Liu, Yixin Liu, Zongxia Li, Zhengqing Yuan, Siqi Zhang, Henry Peng Zou, Zhiling Yan, Yuxuan Zhang, Yanfang Ye, Philip S. Yu, and Lichao Sun. 2026. Dr. Claw: An AI Scientist Workspace for Vibe Research. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 868–879, Budapest, Hungary. Association for Computational Linguistics.