@inproceedings{qian-etal-2026-actor,
title = "{ACTOR}: Agentic {CT} Reasoning with Radiologist-Aligned Actions",
author = "Qian, Xingyu and
Sun, Yu and
Li, Songyou and
Li, Zongzhao and
Cen, Jiacheng and
Wu, Wenqian and
Xu, Tingyang and
Rong, Yu and
Zhao, Deli and
Huang, Wenbing",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2402/",
pages = "54593--54611",
ISBN = "979-8-89176-459-0",
abstract = "Radiologists read CT as a sequential decision process, actively scrolling, zooming, adjusting windows, comparing with prior scans, and measuring structures, where each observation guides the next action. Current VLMs instead answer from passively sampled views, missing the multi-step reasoning that clinical reading demands. We address this by aligning the agent{'}s action space to the radiologists' operations within the Picture Archiving and Communication System (PACS), enabling active, multi-step CT reading with inherently transparent reasoning for clinical decision support. In particular, we build **ACTOR**, a unified framework comprising: (1) a PACS-like interactive CT environment with five radiologist-aligned tools; (2) **ActiveCTR-Bench**, spanning 7 tasks across a Perception{--}Interpretation{--}Reasoning hierarchy (7,112 items with visual ground truth and verified vision necessity); and (3) **ActiveCTReasoner**, a 7B model trained on 108K synthesized trajectories via SFT and reinforcement learning. ActiveCTReasoner achieves 73.2{\%} close-ended accuracy, outperforming GPT-5.4 (47.3{\%}) remarkably. In open-ended evaluations, it scores 44.2{\%} compared to 23.8{\%} for the best baseline. A radiologist collaboration study provides preliminary evidence for the decision-support utility of radiologist-aligned agentic reasoning."
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<abstract>Radiologists read CT as a sequential decision process, actively scrolling, zooming, adjusting windows, comparing with prior scans, and measuring structures, where each observation guides the next action. Current VLMs instead answer from passively sampled views, missing the multi-step reasoning that clinical reading demands. We address this by aligning the agent’s action space to the radiologists’ operations within the Picture Archiving and Communication System (PACS), enabling active, multi-step CT reading with inherently transparent reasoning for clinical decision support. In particular, we build **ACTOR**, a unified framework comprising: (1) a PACS-like interactive CT environment with five radiologist-aligned tools; (2) **ActiveCTR-Bench**, spanning 7 tasks across a Perception–Interpretation–Reasoning hierarchy (7,112 items with visual ground truth and verified vision necessity); and (3) **ActiveCTReasoner**, a 7B model trained on 108K synthesized trajectories via SFT and reinforcement learning. ActiveCTReasoner achieves 73.2% close-ended accuracy, outperforming GPT-5.4 (47.3%) remarkably. In open-ended evaluations, it scores 44.2% compared to 23.8% for the best baseline. A radiologist collaboration study provides preliminary evidence for the decision-support utility of radiologist-aligned agentic reasoning.</abstract>
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%0 Conference Proceedings
%T ACTOR: Agentic CT Reasoning with Radiologist-Aligned Actions
%A Qian, Xingyu
%A Sun, Yu
%A Li, Songyou
%A Li, Zongzhao
%A Cen, Jiacheng
%A Wu, Wenqian
%A Xu, Tingyang
%A Rong, Yu
%A Zhao, Deli
%A Huang, Wenbing
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F qian-etal-2026-actor
%X Radiologists read CT as a sequential decision process, actively scrolling, zooming, adjusting windows, comparing with prior scans, and measuring structures, where each observation guides the next action. Current VLMs instead answer from passively sampled views, missing the multi-step reasoning that clinical reading demands. We address this by aligning the agent’s action space to the radiologists’ operations within the Picture Archiving and Communication System (PACS), enabling active, multi-step CT reading with inherently transparent reasoning for clinical decision support. In particular, we build **ACTOR**, a unified framework comprising: (1) a PACS-like interactive CT environment with five radiologist-aligned tools; (2) **ActiveCTR-Bench**, spanning 7 tasks across a Perception–Interpretation–Reasoning hierarchy (7,112 items with visual ground truth and verified vision necessity); and (3) **ActiveCTReasoner**, a 7B model trained on 108K synthesized trajectories via SFT and reinforcement learning. ActiveCTReasoner achieves 73.2% close-ended accuracy, outperforming GPT-5.4 (47.3%) remarkably. In open-ended evaluations, it scores 44.2% compared to 23.8% for the best baseline. A radiologist collaboration study provides preliminary evidence for the decision-support utility of radiologist-aligned agentic reasoning.
%U https://aclanthology.org/2026.emnlp-main.2402/
%P 54593-54611
Markdown (Informal)
[ACTOR: Agentic CT Reasoning with Radiologist-Aligned Actions](https://aclanthology.org/2026.emnlp-main.2402/) (Qian et al., EMNLP 2026)
ACL
- Xingyu Qian, Yu Sun, Songyou Li, Zongzhao Li, Jiacheng Cen, Wenqian Wu, Tingyang Xu, Yu Rong, Deli Zhao, and Wenbing Huang. 2026. ACTOR: Agentic CT Reasoning with Radiologist-Aligned Actions. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 54593–54611, Budapest, Hungary. Association for Computational Linguistics.