Fine-grained perception of multimodal information is critical for advancing human-AI interaction. With recent progress in audio-visual technologies, Omni Language Models (OLMs), capable of processing audio and video signals in parallel, have emerged as a promising paradigm for achieving richer understanding and reasoning. However, their capacity to capture and describe fine-grained details remains limited explored. In this work, we present a systematic and comprehensive investigation of omni detailed perception from the perspectives of the data pipeline, models, and benchmark. We first identify an inherent "co-growth" between detail and hallucination in current OLMs. To address this, we propose Omni-Detective, an agentic data generation pipeline integrating tool-calling, to autonomously produce highly detailed yet minimally hallucinatory multimodal data. Based on the data generated with Omni-Detective, we train two captioning models: Audio-Captioner for audio-only detailed perception, and Omni-Captioner for audio-visual detailed perception. Under the cascade evaluation protocol, Audio-Captioner achieves the best performance on MMAU and MMAR among all open-source models, surpassing Gemini 2.5 Flash and delivering performance comparable to Gemini 2.5 Pro. On existing detailed captioning benchmarks, Omni-Captioner sets a new state-of-the-art on VDC and achieves the best trade-off between detail and hallucination on the video-SALMONN 2 testset. Given the absence of a dedicated benchmark for omni detailed perception, we design Omni-Cloze, a novel cloze-style evaluation for detailed audio, visual, and audio-visual captioning that ensures stable, efficient, and reliable assessment. Experimental results and analysis demonstrate the effectiveness of Omni-Detective in generating high-quality detailed captions, as well as the superiority of Omni-Cloze in evaluating such detailed captions.

翻译：多模态信息的细粒度感知对于推进人机交互至关重要。随着视听技术的近期进展，能够并行处理音频与视频信号的Omni语言模型（OLMs）已成为实现更丰富理解与推理的前沿范例。然而，它们在捕捉与描述细粒度细节方面的能力仍缺乏深入探索。本工作从数据流程、模型和基准三个维度对全维精细感知展开了系统而全面的研究。我们首先发现当前OLMs中细节与幻觉之间存在固有的“协同增长”现象。为解决此问题，我们提出Omni-Detective——一种集成工具调用的智能体数据生成流程，可自主产生高细节度且低幻觉的多模态数据。基于Omni-Detective生成的数据，我们训练了两个描述模型：面向纯音频精细感知的Audio-Captioner，以及面向视听精细感知的Omni-Captioner。在级联评估协议下，Audio-Captioner在所有开源模型中于MMAU和MMAR基准上取得最佳性能，超越Gemini 2.5 Flash，并与Gemini 2.5 Pro性能相当。在现有精细描述基准上，Omni-Captioner在VDC上创下新最优结果，并在video-SALMONN 2测试集上实现了细节与幻觉的最佳权衡。鉴于全维精细感知专用基准的缺失，我们设计了Omni-Cloze——一种面向精细音频、视觉及视听描述的新型完形填空式评估方法，可确保稳定、高效且可靠的评测。实验结果与分析证明了Omni-Detective在生成高质量精细描述方面的有效性，以及Omni-Cloze在评估此类精细描述上的优越性。