HumanOmniV2: From Understanding to Omni-Modal Reasoning with Context
Authors: name: Qize Yang; name: Shimin Yao; name: Weixuan Chen; name: Shenghao Fu; name: Detao Bai; name: Jiaxing Zhao; name: Boyuan Sun; name: Bowen Yin; name: Xihan Wei; name: Jingren Zhou
Published: 2025-06-26 14:01:03

Abstract: With the rapid evolution of multimodal large language models, the capacity to
deeply understand and interpret human intentions has emerged as a critical
capability, which demands detailed and thoughtful reasoning. In recent studies,
Reinforcement Learning (RL) has demonstrated potential in enhancing the
reasoning capabilities of Large Language Models (LLMs). Nonetheless, the
challenges associated with adapting RL to multimodal data and formats remain
largely unaddressed. In this paper, we identify two issues in existing
multimodal reasoning models: insufficient global context understanding and
shortcut problems. Insufficient context understanding can happen when a model
misinterprets multimodal context, resulting in incorrect answers. The shortcut
problem occurs when the model overlooks crucial clues in multimodal inputs,
directly addressing the query without considering the multimodal information.
To tackle these issues, we emphasize the necessity for the model to reason with
a clear understanding of the global context within multimodal inputs. This
global context understanding can effectively prevent the model from overlooking
key multimodal cues and ensure a thorough reasoning process. To ensure the
accurate interpretation of multimodal context information, we implement a
context reward judged by a large language model, alongside format and accuracy
rewards. Additionally, to improve complex reasoning capability, we employ the
LLM to assess the logical reward, determining whether the reasoning process
successfully integrates multimodal information with logical methods. We also
introduce a reasoning omni-modal benchmark, IntentBench, aimed at evaluating
models in understanding complex human intentions and emotions. Our proposed
method demonstrates advanced performance across multiple omni-modal benchmarks
compared to other open-source omni-modal models.