@inproceedings{zheng-etal-2026-scilens,
title = "{S}ci{LENS}: {RL}-Driven Autonomous Agents for Scientific Localized Evidence Navigation and Synthesis",
author = "Zheng, Leqi and
Su, Jinbo and
Li, Yuying and
Wang, Chaokun and
Wang, Weiping and
Li, Haitao and
Zhang, Jiajun and
Yan, Shannan and
Kang, Zhaolu and
Fu, Rong and
Wu, Jie and
Niu, Fang and
Zhang, Hang",
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.589/",
pages = "13482--13526",
ISBN = "979-8-89176-459-0",
abstract = "The application of autonomous agents to scientific literature review is often constrained by the reliability of online search infrastructures and linear text generation. To address these limitations, we introduce SciLENS (Scientific Localized Evidence Navigation and Synthesis), an autonomous agent framework operating entirely upon a localized dual-tier data infrastructure for deterministic access to massive academic corpora. To mitigate online dependency, SciLENS utilizes a comprehensive offline toolbox featuring topological graph traversal and structural visualization. This empowers the agent to navigate deep citation networks and organize macro-level trends into structured visual representations, mitigating context exhaustion. To train this system without human annotation, we develop an automated multi-hop data synthesis pipeline validated by cross-model consensus, followed by rubric-based reinforcement learning to align the logical planning of the agent. Evaluations across standard tasks and newly constructed complex synthesis benchmarks demonstrate that SciLENS achieves robust factual precision, outperforming existing open-source baselines and achieving performance comparable to frontier proprietary models. Our source code and data are released at \url{ https://anonymous.4open.science/r/SciLENS-48DB }."
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<abstract>The application of autonomous agents to scientific literature review is often constrained by the reliability of online search infrastructures and linear text generation. To address these limitations, we introduce SciLENS (Scientific Localized Evidence Navigation and Synthesis), an autonomous agent framework operating entirely upon a localized dual-tier data infrastructure for deterministic access to massive academic corpora. To mitigate online dependency, SciLENS utilizes a comprehensive offline toolbox featuring topological graph traversal and structural visualization. This empowers the agent to navigate deep citation networks and organize macro-level trends into structured visual representations, mitigating context exhaustion. To train this system without human annotation, we develop an automated multi-hop data synthesis pipeline validated by cross-model consensus, followed by rubric-based reinforcement learning to align the logical planning of the agent. Evaluations across standard tasks and newly constructed complex synthesis benchmarks demonstrate that SciLENS achieves robust factual precision, outperforming existing open-source baselines and achieving performance comparable to frontier proprietary models. Our source code and data are released at https://anonymous.4open.science/r/SciLENS-48DB .</abstract>
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%0 Conference Proceedings
%T SciLENS: RL-Driven Autonomous Agents for Scientific Localized Evidence Navigation and Synthesis
%A Zheng, Leqi
%A Su, Jinbo
%A Li, Yuying
%A Wang, Chaokun
%A Wang, Weiping
%A Li, Haitao
%A Zhang, Jiajun
%A Yan, Shannan
%A Kang, Zhaolu
%A Fu, Rong
%A Wu, Jie
%A Niu, Fang
%A Zhang, Hang
%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 zheng-etal-2026-scilens
%X The application of autonomous agents to scientific literature review is often constrained by the reliability of online search infrastructures and linear text generation. To address these limitations, we introduce SciLENS (Scientific Localized Evidence Navigation and Synthesis), an autonomous agent framework operating entirely upon a localized dual-tier data infrastructure for deterministic access to massive academic corpora. To mitigate online dependency, SciLENS utilizes a comprehensive offline toolbox featuring topological graph traversal and structural visualization. This empowers the agent to navigate deep citation networks and organize macro-level trends into structured visual representations, mitigating context exhaustion. To train this system without human annotation, we develop an automated multi-hop data synthesis pipeline validated by cross-model consensus, followed by rubric-based reinforcement learning to align the logical planning of the agent. Evaluations across standard tasks and newly constructed complex synthesis benchmarks demonstrate that SciLENS achieves robust factual precision, outperforming existing open-source baselines and achieving performance comparable to frontier proprietary models. Our source code and data are released at https://anonymous.4open.science/r/SciLENS-48DB .
%U https://aclanthology.org/2026.emnlp-main.589/
%P 13482-13526
Markdown (Informal)
[SciLENS: RL-Driven Autonomous Agents for Scientific Localized Evidence Navigation and Synthesis](https://aclanthology.org/2026.emnlp-main.589/) (Zheng et al., EMNLP 2026)
ACL
- Leqi Zheng, Jinbo Su, Yuying Li, Chaokun Wang, Weiping Wang, Haitao Li, Jiajun Zhang, Shannan Yan, Zhaolu Kang, Rong Fu, Jie Wu, Fang Niu, and Hang Zhang. 2026. SciLENS: RL-Driven Autonomous Agents for Scientific Localized Evidence Navigation and Synthesis. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 13482–13526, Budapest, Hungary. Association for Computational Linguistics.