@inproceedings{ding-etal-2026-octobench,
title = "{O}cto{B}ench: Benchmarking Scaffold-Aware Instruction Following in Repository-Grounded Agentic Coding",
author = "Ding, Deming and
Liu, Shichun and
Yang, Enhui and
Lin, Jiahang and
Chen, Ziying and
Dou, Shihan and
Guo, Honglin and
Cheng, Weiyu and
Zhao, Pengyu and
Xiao, Chengjun and
Zeng, Qunhong and
Zhang, Qi and
Huang, Xuanjing and
Xu, Qidi and
Gui, Tao",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.269/",
pages = "5958--5978",
ISBN = "979-8-89176-390-6",
abstract = "Modern coding scaffolds turn LLMs into capable software agents, but their ability to follow scaffold-specified instructions remains under-examined, especially when constraints are heterogeneous and persist across interactions. To fill this gap, we introduce OctoBench, which benchmarks scaffold-aware instruction following in repository-grounded agentic coding. OctoBench includes 34 environments and 217 tasks instantiated under three scaffold types, and is paired with 7,098 objective checklist items. To disentangle solving the task from following the rules, we provide an automated observation-and-scoring toolkit that captures full trajectories and performs fine-grained checks. Experiments on eight representative models reveal a systematic gap between task-solving and scaffold-aware compliance, underscoring the need for training and evaluation that explicitly targets heterogeneous instruction following. We will release the benchmark to support reproducible benchmarking and to accelerate the development of more scaffold-aware coding agents."
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<abstract>Modern coding scaffolds turn LLMs into capable software agents, but their ability to follow scaffold-specified instructions remains under-examined, especially when constraints are heterogeneous and persist across interactions. To fill this gap, we introduce OctoBench, which benchmarks scaffold-aware instruction following in repository-grounded agentic coding. OctoBench includes 34 environments and 217 tasks instantiated under three scaffold types, and is paired with 7,098 objective checklist items. To disentangle solving the task from following the rules, we provide an automated observation-and-scoring toolkit that captures full trajectories and performs fine-grained checks. Experiments on eight representative models reveal a systematic gap between task-solving and scaffold-aware compliance, underscoring the need for training and evaluation that explicitly targets heterogeneous instruction following. We will release the benchmark to support reproducible benchmarking and to accelerate the development of more scaffold-aware coding agents.</abstract>
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%0 Conference Proceedings
%T OctoBench: Benchmarking Scaffold-Aware Instruction Following in Repository-Grounded Agentic Coding
%A Ding, Deming
%A Liu, Shichun
%A Yang, Enhui
%A Lin, Jiahang
%A Chen, Ziying
%A Dou, Shihan
%A Guo, Honglin
%A Cheng, Weiyu
%A Zhao, Pengyu
%A Xiao, Chengjun
%A Zeng, Qunhong
%A Zhang, Qi
%A Huang, Xuanjing
%A Xu, Qidi
%A Gui, Tao
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F ding-etal-2026-octobench
%X Modern coding scaffolds turn LLMs into capable software agents, but their ability to follow scaffold-specified instructions remains under-examined, especially when constraints are heterogeneous and persist across interactions. To fill this gap, we introduce OctoBench, which benchmarks scaffold-aware instruction following in repository-grounded agentic coding. OctoBench includes 34 environments and 217 tasks instantiated under three scaffold types, and is paired with 7,098 objective checklist items. To disentangle solving the task from following the rules, we provide an automated observation-and-scoring toolkit that captures full trajectories and performs fine-grained checks. Experiments on eight representative models reveal a systematic gap between task-solving and scaffold-aware compliance, underscoring the need for training and evaluation that explicitly targets heterogeneous instruction following. We will release the benchmark to support reproducible benchmarking and to accelerate the development of more scaffold-aware coding agents.
%U https://aclanthology.org/2026.acl-long.269/
%P 5958-5978
Markdown (Informal)
[OctoBench: Benchmarking Scaffold-Aware Instruction Following in Repository-Grounded Agentic Coding](https://aclanthology.org/2026.acl-long.269/) (Ding et al., ACL 2026)
ACL
- Deming Ding, Shichun Liu, Enhui Yang, Jiahang Lin, Ziying Chen, Shihan Dou, Honglin Guo, Weiyu Cheng, Pengyu Zhao, Chengjun Xiao, Qunhong Zeng, Qi Zhang, Xuanjing Huang, Qidi Xu, and Tao Gui. 2026. OctoBench: Benchmarking Scaffold-Aware Instruction Following in Repository-Grounded Agentic Coding. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5958–5978, San Diego, California, United States. Association for Computational Linguistics.