@inproceedings{ruan-etal-2026-tofu,
title = "{T}o{F}u: A White-Box, Token-Efficient Agent Harness for Researchers",
author = "Ruan, Junhao and
Ge, Yuan and
Li, Bei and
Yin, Yongjing and
Fan, Yuchun and
Chen, Xin and
Wang, Jingang and
Wang, Chenglong and
Zhu, JingBo and
Xiao, Tong",
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.22/",
pages = "225--234",
ISBN = "979-8-89176-461-3",
abstract = "Agentic coding tools present new opportunities to transform research workflows. The performance of agent systems built depends on both large language models (LLMs) and the \textit{harness} around LLMs, which is the orchestration code that determines an agent{'}s behavior. We present \textsc{ToFu}, an agentic harness for researchers that reads your codebase, edits files, runs commands, and integrates with your development tools.\par \textsc{ToFu} plays a dual role in research. As a \textit{research assistant}, it supports practical research workflows with superior token efficiency, lower cost, and multilingual capability compared with existing agentic harnesses. Its release under the \texttt{MIT License} further enables local deployment for privacy-sensitive users. As a \textit{research object}, \textsc{ToFu} provides a white-box agentic harness that allows researchers to inspect, modify, and evaluate its orchestration logic, tool-use behavior, and harness design, while retaining strong benchmark performance and an application-level user experience."
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<abstract>Agentic coding tools present new opportunities to transform research workflows. The performance of agent systems built depends on both large language models (LLMs) and the harness around LLMs, which is the orchestration code that determines an agent’s behavior. We present ToFu, an agentic harness for researchers that reads your codebase, edits files, runs commands, and integrates with your development tools.\par ToFu plays a dual role in research. As a research assistant, it supports practical research workflows with superior token efficiency, lower cost, and multilingual capability compared with existing agentic harnesses. Its release under the MIT License further enables local deployment for privacy-sensitive users. As a research object, ToFu provides a white-box agentic harness that allows researchers to inspect, modify, and evaluate its orchestration logic, tool-use behavior, and harness design, while retaining strong benchmark performance and an application-level user experience.</abstract>
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%0 Conference Proceedings
%T ToFu: A White-Box, Token-Efficient Agent Harness for Researchers
%A Ruan, Junhao
%A Ge, Yuan
%A Li, Bei
%A Yin, Yongjing
%A Fan, Yuchun
%A Chen, Xin
%A Wang, Jingang
%A Wang, Chenglong
%A Zhu, JingBo
%A Xiao, Tong
%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 ruan-etal-2026-tofu
%X Agentic coding tools present new opportunities to transform research workflows. The performance of agent systems built depends on both large language models (LLMs) and the harness around LLMs, which is the orchestration code that determines an agent’s behavior. We present ToFu, an agentic harness for researchers that reads your codebase, edits files, runs commands, and integrates with your development tools.\par ToFu plays a dual role in research. As a research assistant, it supports practical research workflows with superior token efficiency, lower cost, and multilingual capability compared with existing agentic harnesses. Its release under the MIT License further enables local deployment for privacy-sensitive users. As a research object, ToFu provides a white-box agentic harness that allows researchers to inspect, modify, and evaluate its orchestration logic, tool-use behavior, and harness design, while retaining strong benchmark performance and an application-level user experience.
%U https://aclanthology.org/2026.emnlp-demos.22/
%P 225-234
Markdown (Informal)
[ToFu: A White-Box, Token-Efficient Agent Harness for Researchers](https://aclanthology.org/2026.emnlp-demos.22/) (Ruan et al., EMNLP 2026)
ACL
- Junhao Ruan, Yuan Ge, Bei Li, Yongjing Yin, Yuchun Fan, Xin Chen, Jingang Wang, Chenglong Wang, JingBo Zhu, and Tong Xiao. 2026. ToFu: A White-Box, Token-Efficient Agent Harness for Researchers. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 225–234, Budapest, Hungary. Association for Computational Linguistics.