title: AV-Reasoner: Improving and Benchmarking Clue-Grounded Audio-Visual
    Counting for MLLMs
summary: Despite progress in video understanding, current MLLMs struggle with counting
  tasks. Existing benchmarks are limited by short videos, close-set queries, lack
  of clue annotations, and weak multimodal coverage. In this paper, we introduce
  CG-AV-Counting, a manually-annotated clue-grounded counting benchmark with
  1,027 multimodal questions and 5,845 annotated clues over 497 long videos. It
  supports both black-box and white-box evaluation, serving as a comprehensive
  testbed for both end-to-end and reasoning-based counting. To explore ways to
  improve model's counting capability, we propose AV-Reasoner, a model trained
  with GRPO and curriculum learning to generalize counting ability from related
  tasks. AV-Reasoner achieves state-of-the-art results across multiple
  benchmarks, demonstrating the effectiveness of reinforcement learning. However,
  experiments show that on out-of-domain benchmarks, reasoning in the language
  space fails to bring performance gains. The code and benchmark have been
  realeased on https://av-reasoner.github.io.
paper_authors._id: 68424822f0c91a7dcb64193b
paper_authors.name: Lidong Lu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-06T07:41:34.755Z
paper_authors._id: 64a3de701698ad2985277148
paper_authors.avatarUrl: /avatars/09eebadbbea53ed2800591564ff5c931.svg
paper_authors.fullname: lulidong
paper_authors.type: user
paper_authors.user: lulidong
paper_authors._id: 68424822f0c91a7dcb64193c
paper_authors.name: Guo Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-06T07:41:32.158Z
paper_authors._id: 6392c73390b8e99a6779a7b0
paper_authors.avatarUrl: /avatars/9ff824ab02848120aec5e8de6780bcf1.svg
paper_authors.fullname: Guo Chen
paper_authors.type: user
paper_authors.user: cg1177
paper_authors._id: 68424822f0c91a7dcb64193d
paper_authors.name: Zhiqi Li
paper_authors._id: 68424822f0c91a7dcb64193e
paper_authors.name: Yicheng Liu
paper_authors._id: 68424822f0c91a7dcb64193f
paper_authors.name: Tong Lu
paper_title: AV-Reasoner: Improving and Benchmarking Clue-Grounded Audio-Visual
    Counting for MLLMs
paper_summary: Despite progress in video understanding, current MLLMs struggle with counting
  tasks. Existing benchmarks are limited by short videos, close-set queries, lack
  of clue annotations, and weak multimodal coverage. In this paper, we introduce
  CG-AV-Counting, a manually-annotated clue-grounded counting benchmark with
  1,027 multimodal questions and 5,845 annotated clues over 497 long videos. It
  supports both black-box and white-box evaluation, serving as a comprehensive
  testbed for both end-to-end and reasoning-based counting. To explore ways to
  improve model's counting capability, we propose AV-Reasoner, a model trained
  with GRPO and curriculum learning to generalize counting ability from related
  tasks. AV-Reasoner achieves state-of-the-art results across multiple
  benchmarks, demonstrating the effectiveness of reinforcement learning. However,
  experiments show that on out-of-domain benchmarks, reasoning in the language
  space fails to bring performance gains. The code and benchmark have been
  realeased on https://av-reasoner.github.io.
paper_ai_summary: CG-AV-Counting is a new benchmark for video counting tasks that includes multimodal data and supports end-to-end and reasoning-based models. AV-Reasoner, trained with GRPO and curriculum learning, achieves top results but shows limitations on out-of-domain tasks.
paper_ai_keywords: MLLMs CG-AV-Counting multimodal questions clue-grounded black-box evaluation white-box evaluation AV-Reasoner GRPO curriculum learning reinforcement learning out-of-domain benchmarks