{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/muirbench-a-comprehensive-benchmark-for","title":"MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding","arxiv_id":"2406.09411","date":"2024-06-13","proceeding":null,"authors":["Fei Wang","Xingyu Fu","James Y. Huang","Zekun Li","Qin Liu","Xiaogeng Liu","Mingyu Derek Ma","Nan Xu","Wenxuan Zhou","Kai Zhang","Tianyi Lorena Yan","Wenjie Jacky Mo","Hsiang-Hui Liu","Pan Lu","Chunyuan Li","Chaowei Xiao","Kai-Wei Chang","Dan Roth","Sheng Zhang","Hoifung Poon","Muhao Chen"],"abstract":"We introduce MuirBench, a comprehensive benchmark that focuses on robust multi-image understanding capabilities of multimodal LLMs. MuirBench consists of 12 diverse multi-image tasks (e.g., scene understanding, ordering) that involve 10 categories of multi-image relations (e.g., multiview, temporal relations). Comprising 11,264 images and 2,600 multiple-choice questions, MuirBench is created in a pairwise manner, where each standard instance is paired with an unanswerable variant that has minimal semantic differences, in order for a reliable assessment. Evaluated upon 20 recent multi-modal LLMs, our results reveal that even the best-performing models like GPT-4o and Gemini Pro find it challenging to solve MuirBench, achieving 68.0% and 49.3% in accuracy. Open-source multimodal LLMs trained on single images can hardly generalize to multi-image questions, hovering below 33.3% in accuracy. These results highlight the importance of MuirBench in encouraging the community to develop multimodal LLMs that can look beyond a single image, suggesting potential pathways for future improvements.","url_abs":"https://arxiv.org/abs/2406.09411v2","url_pdf":"https://arxiv.org/pdf/2406.09411v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"muirbench-a-comprehensive-benchmark-for","repo_url":"https://github.com/muirbench/MuirBench","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"multiple-choice","task_name":"Multiple-choice"},{"task_slug":"scene-understanding","task_name":"Scene Understanding"}],"methods":[],"datasets_introduced":[{"slug":"muirbench","name":"MuirBench","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2406.09411","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.09411"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/muirbench/MuirBench","reach":null}],"summary":{"ran_draft_wrong":2},"by_repo_kind":{"official":{"samples":2,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":2,"samples":[{"code_sha256_prefix":"1cc5bed6d9e89200","entry":"create_prompt","repo":"muirbench/MuirBench","repo_kind":"official","path":"eval/utils/preprocess.py","file_url":"https://github.com/muirbench/MuirBench/blob/HEAD/eval/utils/preprocess.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"1cc5bed6d9e89200"}},{"code_sha256_prefix":"b745b9c89ddb6cf5","entry":"parse_multi_choice_response","repo":"muirbench/MuirBench","repo_kind":"official","path":"eval/utils/postprocess.py","file_url":"https://github.com/muirbench/MuirBench/blob/HEAD/eval/utils/postprocess.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"b745b9c89ddb6cf5"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}