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However, existing open LMMs largely focus on single-image tasks, their applications to multi-image scenarios remains less explored. Additionally, prior LMM research separately tackles different scenarios, leaving it impossible to generalize cross scenarios with new emerging capabilities. To this end, we introduce LLaVA-NeXT-Interleave, which simultaneously tackles Multi-image, Multi-frame (video), Multi-view (3D), and Multi-patch (single-image) scenarios in LMMs. To enable these capabilities, we regard the interleaved data format as a general template and compile the M4-Instruct dataset with 1,177.6k samples, spanning 4 primary domains with 14 tasks and 41 datasets. We also curate the LLaVA-Interleave Bench to comprehensively evaluate the multi-image performance of LMMs. Through extensive experiments, LLaVA-NeXT-Interleave achieves leading results in multi-image, video, and 3D benchmarks, while maintaining the performance of single-image tasks. Besides, our model also exhibits several emerging capabilities, e.g., transferring tasks across different settings and modalities. Code is available at https://github.com/LLaVA-VL/LLaVA-NeXT","url_abs":"https://arxiv.org/abs/2407.07895v2","url_pdf":"https://arxiv.org/pdf/2407.07895v2.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":"llava-next-interleave-tackling-multi-image","repo_url":"https://github.com/LLaVA-VL/LLaVA-NeXT","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"llava-next-interleave-tackling-multi-image","repo_url":"https://github.com/dinhvietcuong1996/icme25-inova","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"llava-next-interleave-tackling-multi-image","repo_url":"https://github.com/pwc-1/Paper-9/tree/main/llava_next","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"video-question-answering","task_name":"Video Question Answering"},{"task_slug":"zeroshot-video-question-answer","task_name":"Zero-Shot Video Question Answer"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-question-answering-on-next-qa","task":"Video Question Answering","dataset":"NExT-QA","model":"LLaVA-NeXT-Interleave(14B)","rank_in_archive_order":16,"of":47,"metrics":{"Accuracy":"79.1"},"uses_additional_data":false},{"leaderboard":"/sota/video-question-answering-on-next-qa","task":"Video Question Answering","dataset":"NExT-QA","model":"LLaVA-NeXT-Interleave(7B)","rank_in_archive_order":19,"of":47,"metrics":{"Accuracy":"78.2"},"uses_additional_data":false},{"leaderboard":"/sota/video-question-answering-on-next-qa","task":"Video Question Answering","dataset":"NExT-QA","model":"LLaVA-NeXT-Interleave(DPO)","rank_in_archive_order":20,"of":47,"metrics":{"Accuracy":"77.9"},"uses_additional_data":false},{"leaderboard":"/sota/zero-shot-video-question-answer-on-vnbench","task":"Zero-Shot Video Question Answer","dataset":"VNBench","model":"LLaVA-NeXT-Video-7B","rank_in_archive_order":6,"of":9,"metrics":{"Accuracy":"20.1"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2407.07895","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.07895"}},"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. 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