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New benchmarks SpecVQA and M3-VQA challenge multimodal LLMs in scientific and multi-hop reasoning

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 6 sources

Researchers have introduced M$^3$-VQA, a new benchmark designed to evaluate multimodal large language models (MLLMs) on complex reasoning tasks involving multiple entities and multi-hop inference. The benchmark challenges models to understand fine-grained details across visual and textual sources, requiring sequential and parallel reasoning. Initial evaluations of 16 leading MLLMs revealed significant limitations in their knowledge acquisition and reasoning capabilities, though performance improved substantially when provided with precise evidence. AI

Summary written by gemini-2.5-flash-lite from 6 sources. How we write summaries →

IMPACT This benchmark will drive advancements in multimodal reasoning for LLMs by highlighting current limitations.

RANK_REASON Introduces a new benchmark for evaluating multimodal large language models.

Read on arXiv cs.CL →

COVERAGE [6]

  1. arXiv cs.AI TIER_1 · Jialu Shen, Han Lyu, Suyang Zhong, Hanzheng Li, Haoyi Tao, Nan Wang, Changhong Chen, Xi Fang ·

    SpecVQA: A Benchmark for Spectral Understanding and Visual Question Answering in Scientific Images

    arXiv:2604.28039v1 Announce Type: new Abstract: Spectra are a prevalent yet highly information-dense form of scientific imagery, presenting substantial challenges to multimodal large language models (MLLMs) due to their unstructured and domain-specific characteristics. Here we in…

  2. arXiv cs.AI TIER_1 · Xi Fang ·

    SpecVQA: A Benchmark for Spectral Understanding and Visual Question Answering in Scientific Images

    Spectra are a prevalent yet highly information-dense form of scientific imagery, presenting substantial challenges to multimodal large language models (MLLMs) due to their unstructured and domain-specific characteristics. Here we introduce SpecVQA, a professional scientific-image…

  3. arXiv cs.CL TIER_1 · Boqi Chen, Xudong Liu, Yunke Ao, Jianing Qiu ·

    Revisiting Greedy Decoding for Visual Question Answering: A Calibration Perspective

    arXiv:2604.23443v1 Announce Type: new Abstract: Stochastic sampling strategies are widely adopted in large language models (LLMs) to balance output coherence and diversity. These heuristics are often inherited in Multimodal LLMs (MLLMs) without task-specific justification. Howeve…

  4. Hugging Face Daily Papers TIER_1 ·

    M$^3$-VQA: A Benchmark for Multimodal, Multi-Entity, Multi-Hop Visual Question Answering

    We present M$^3$-VQA, a novel knowledge-based Visual Question Answering (VQA) benchmark, to enhance the evaluation of multimodal large language models (MLLMs) in fine-grained multimodal entity understanding and complex multi-hop reasoning. Unlike existing VQA datasets that focus …

  5. arXiv cs.CV TIER_1 · Jiatong Ma, Longteng Guo, Yuchen Liu, Zijia Zhao, Dongze Hao, Xuanxu Lin, Jing Liu ·

    M$^3$-VQA: A Benchmark for Multimodal, Multi-Entity, Multi-Hop Visual Question Answering

    arXiv:2604.25122v1 Announce Type: new Abstract: We present M$^3$-VQA, a novel knowledge-based Visual Question Answering (VQA) benchmark, to enhance the evaluation of multimodal large language models (MLLMs) in fine-grained multimodal entity understanding and complex multi-hop rea…

  6. arXiv cs.CV TIER_1 · Jing Liu ·

    M$^3$-VQA: A Benchmark for Multimodal, Multi-Entity, Multi-Hop Visual Question Answering

    We present M$^3$-VQA, a novel knowledge-based Visual Question Answering (VQA) benchmark, to enhance the evaluation of multimodal large language models (MLLMs) in fine-grained multimodal entity understanding and complex multi-hop reasoning. Unlike existing VQA datasets that focus …

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