@inproceedings{wang-etal-2026-sin,
title = "{SIN}-Bench: Tracing Native Evidence Chains in Long-Context Multimodal Scientific Interleaved Literature",
author = "Wang, Junjie and
Ren, Yiming and
Meng, Yuxin and
Shi, Yihang and
Lin, Zhiqiang and
Song, Yulin and
Chu, Ruihang and
Xu, Yiran and
Li, Ziming and
Zhao, Yunfei and
Wang, Zihan and
Qiao, Yu and
Tang, Ruiming and
Liu, Minghao and
Yang, Yujiu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1052/",
pages = "22278--22302",
ISBN = "979-8-89176-462-0",
abstract = "Evaluating multimodal large language models (MLLMs) on long-form scientific papers requires measuring not only answer correctness, but also whether answers are supported by traceable cross-modal evidence. Existing answer-only metrics and synthetic ``Needle-In-A-Haystack'' tests mainly assess answer matching or isolated retrieval, leaving evidence-grounded support under-examined. We introduce ``Fish-in-the-Ocean'' (FITO), an evidence-chain evaluation lens for native scientific documents, and instantiate it with SIN-Data, a scientific interleaved corpus that preserves text{--}figure coupling. Built on SIN-Data, SIN-Bench comprises four evidence-auditing tasks: evidence discovery, hypothesis verification, grounded QA, and evidence-anchored synthesis. We further propose ``No Evidence, No Score'', which credits predictions only when linked to verifiable anchors and diagnoses evidence quality through matching, relevance, and logic. Experiments on 10 MLLMs show a clear correctness{--}grounding gap: Gemini-3-pro obtains the best average overall score (0.566), while GPT-5 reaches the highest SIN-QA answer accuracy (0.767) but lower evidence-aligned scores. Code and data are available at https://github.com/IIGROUP/sin-bench ."
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<abstract>Evaluating multimodal large language models (MLLMs) on long-form scientific papers requires measuring not only answer correctness, but also whether answers are supported by traceable cross-modal evidence. Existing answer-only metrics and synthetic “Needle-In-A-Haystack” tests mainly assess answer matching or isolated retrieval, leaving evidence-grounded support under-examined. We introduce “Fish-in-the-Ocean” (FITO), an evidence-chain evaluation lens for native scientific documents, and instantiate it with SIN-Data, a scientific interleaved corpus that preserves text–figure coupling. Built on SIN-Data, SIN-Bench comprises four evidence-auditing tasks: evidence discovery, hypothesis verification, grounded QA, and evidence-anchored synthesis. We further propose “No Evidence, No Score”, which credits predictions only when linked to verifiable anchors and diagnoses evidence quality through matching, relevance, and logic. Experiments on 10 MLLMs show a clear correctness–grounding gap: Gemini-3-pro obtains the best average overall score (0.566), while GPT-5 reaches the highest SIN-QA answer accuracy (0.767) but lower evidence-aligned scores. Code and data are available at https://github.com/IIGROUP/sin-bench .</abstract>
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%0 Conference Proceedings
%T SIN-Bench: Tracing Native Evidence Chains in Long-Context Multimodal Scientific Interleaved Literature
%A Wang, Junjie
%A Ren, Yiming
%A Meng, Yuxin
%A Shi, Yihang
%A Lin, Zhiqiang
%A Song, Yulin
%A Chu, Ruihang
%A Xu, Yiran
%A Li, Ziming
%A Zhao, Yunfei
%A Wang, Zihan
%A Qiao, Yu
%A Tang, Ruiming
%A Liu, Minghao
%A Yang, Yujiu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wang-etal-2026-sin
%X Evaluating multimodal large language models (MLLMs) on long-form scientific papers requires measuring not only answer correctness, but also whether answers are supported by traceable cross-modal evidence. Existing answer-only metrics and synthetic “Needle-In-A-Haystack” tests mainly assess answer matching or isolated retrieval, leaving evidence-grounded support under-examined. We introduce “Fish-in-the-Ocean” (FITO), an evidence-chain evaluation lens for native scientific documents, and instantiate it with SIN-Data, a scientific interleaved corpus that preserves text–figure coupling. Built on SIN-Data, SIN-Bench comprises four evidence-auditing tasks: evidence discovery, hypothesis verification, grounded QA, and evidence-anchored synthesis. We further propose “No Evidence, No Score”, which credits predictions only when linked to verifiable anchors and diagnoses evidence quality through matching, relevance, and logic. Experiments on 10 MLLMs show a clear correctness–grounding gap: Gemini-3-pro obtains the best average overall score (0.566), while GPT-5 reaches the highest SIN-QA answer accuracy (0.767) but lower evidence-aligned scores. Code and data are available at https://github.com/IIGROUP/sin-bench .
%U https://aclanthology.org/2026.findings-emnlp.1052/
%P 22278-22302
Markdown (Informal)
[SIN-Bench: Tracing Native Evidence Chains in Long-Context Multimodal Scientific Interleaved Literature](https://aclanthology.org/2026.findings-emnlp.1052/) (Wang et al., Findings 2026)
ACL
- Junjie Wang, Yiming Ren, Yuxin Meng, Yihang Shi, Zhiqiang Lin, Yulin Song, Ruihang Chu, Yiran Xu, Ziming Li, Yunfei Zhao, Zihan Wang, Yu Qiao, Ruiming Tang, Minghao Liu, and Yujiu Yang. 2026. SIN-Bench: Tracing Native Evidence Chains in Long-Context Multimodal Scientific Interleaved Literature. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 22278–22302, Budapest, Hungary. Association for Computational Linguistics.