QMAVIS: Multimodal Audio-Video Sensemaking

• QMAVIS is a comprehensive methodology that integrates large multimodal models, automatic speech recognition, and language models for high-level, cross-modal sensemaking.
• It employs a modular, late-fusion pipeline that segments long videos into chunks and interleaves analysis to enable robust global aggregation of multimodal data.
• Evaluations on standard benchmarks show QMAVIS’s significant performance improvements and scalability in real-time analytics and diverse downstream applications.

QMAVIS (Q Team-Multimodal Audio Video Intelligent Sensemaking) is a system architecture, toolkit style, and methodology for automatic sensemaking in long-form, multimodal (video + audio + text) data streams. Designed as a solution to open-domain video/audio understanding that requires both high-level abstraction and fine-grained temporal reasoning, QMAVIS integrates large multimodal models (LMMs), automatic speech recognition, and LLMs in a modular and scalable late-fusion framework. It serves as both a practical pipeline for long video-audio analytics and a research protocol for benchmarking cross-modal sensemaking capabilities.

1. Core Pipeline Architecture and Mathematical Foundation

QMAVIS adopts a late-fusion paradigm for video-audio sensemaking over long-duration content. The pipeline proceeds in sequential modules:

1. Segmentation: The input long video (with audio) is split into $N$ contiguous chunks $\{C_i\}_{i=1}^N$ of ca. 30 s each.
2. Unimodal Analysis:
   • Video chunk $V_i$ is processed by a pretrained video LMM (e.g., Qwen2-VL-72B) to produce a caption $X_{V,i}$.
   • Corresponding audio chunk $A_i$ is transcribed by an ASR model (e.g., Whisper-L3) into $X_{A,i}$.
3. Interleaving & Fusion: The set $$\mathcal{S} = [X_{V,1}, X_{A,1}, \ldots, X_{V,N}, X_{A,N}]$$ is constructed by sequence-wise concatenation of chunk-level representations.
4. Global Aggregation: A LLM $f_\mathrm{LLM}$ (e.g. Qwen2-Chat) produces an overall answer, dialogue, caption, or summary:

$X_\mathrm{out} = f_\mathrm{LLM}(X_C, \mathcal{S})$

where $X_C$ is the aggregation prompt (possibly including a query or instructions).

No early cross-modal fusion is performed; all interaction between modalities occurs at the global LLM aggregation stage via self-attention. All mathematical mappings reflect token-sequence concatenation and vanilla LLM completion.

2. Design Principles and Integration with Pretrained Models

QMAVIS is intentionally modular, leveraging strengths of independent pre-trained models:

• Video LMMs: (e.g., Qwen2-VL-72B, VideoLLaMA2, InternVL2) for per-chunk visual description.
• ASR modules: (e.g., Whisper-L3) for robust long-form speech transcription, with separate alignment for non-speech audio cues as required.
• Aggregation LLM: (e.g., Qwen2-Chat) for narrative assembly, QA, and high-order sensemaking.
• Plug-and-play architecture: Additional downstream tasks (scene classification, event detection, dialogue simulation, clustering) can be implemented by connecting containerized services with a uniform JSON API (Pham et al., 2024).

Importantly, this architecture supports real-time processing and batch analytics for videos ranging from under a minute to over an hour. Each module is independently deployable and replaceable, providing scalability and system extensibility.

3. Advanced Multimodal Fusion Mechanisms

While QMAVIS’s default pipeline uses late fusion, several advanced multimodal interaction modules developed in the literature are readily incorporated for enhanced sensemaking in challenging settings:

• Wavelet-Driven Fusion: Frequency-domain synchronization and fusion of audio-video features using discrete wavelet transforms, followed by cross-modal attention with text. This module enhances sensitivity to rapid tonal shifts, micro-expressions, and subtle emotional cues, yielding significant performance gains on intent recognition tasks ($+1.13\%$ on MIntRec) (Gong et al., 27 May 2025).
• Temporal-Spatial Perception: Declarative prompt construction, temporally-guided segment selection, spatial token-merging, and cross-modal audio–vision attention as in the TSPM model. Frame-level relevance is scored by scaled dot-product between prompt and visual embeddings, with SOTA performance on MUSIC-AVQA and AVQA ($+2.33$ pp, avg. acc.) (Li et al., 2024).
• Real-time Strategy: Asynchronous streaming, fast DWT computation, ring buffer maintenance, and dynamic pipeline adjustments (fallbacks on modality dropout and SNR tracking) facilitate high-throughput, near real-time operation (Gong et al., 27 May 2025).

4. Evaluation Protocols and Benchmarks

QMAVIS is evaluated on a suite of long-form, multi-modal benchmarks that probe its sensemaking capabilities:

• VideoMME: Long videos across 6 domains; measures Top-1 accuracy in MC-QA format. QMAVIS outperforms VideoLLaMA2 by 38.75% (66.46% vs. 47.90%) (Lin et al., 10 Jan 2026).
• PerceptionTest & EgoSchema: Short to medium-length videos with high demand for cross-modal reasoning. QMAVIS achieves 1–2% improvement over prior state of the art (Lin et al., 10 Jan 2026).
• MAVERIX: 2,556 questions over 700 videos, designed for modality interdependence, temporal alignment, and social/subjective reasoning; supports open-ended and MCQ protocols (Xie et al., 27 Mar 2025).
• XGC-AVQuiz: 2,685 QAs over 2,232 diverse videos (PGC/UGC/AIGC) across 20 task types, emphasizing quality perception and fine temporal localization (Cao et al., 27 Sep 2025).
• AVHaystacksQA: Large-scale, multi-video retrieval and step-wise answer grounding; emphasizes agent-based multi-modal retrieval, temporal grounding, and meta-aggregation (Chowdhury et al., 8 Jun 2025).

All core metrics are standard in the field: Top-1/Top-N accuracy, matched temporal grounding score (MTGS), stepwise error (StEM), and cross-modal gain ($\Delta_{VA}$). Real-time deployments are further assessed via throughput and latency.

5. Comparative Performance and Ablative Insights

Empirical results indicate that QMAVIS’s architectural choices are decisive for performance:

Method	VideoMME	PerceptionTest	EgoSchema
VideoLLaMA2	47.90	57.50	63.90
InternVideo2 (Base)	41.90	52.16	55.00
PandaGPT	22.58	31.63	24.00
QMAVIS (full)	66.46	57.72	65.00
QMAVIS (no LLM)	63.00	53.14	62.80
QMAVIS (no ASR)	48.18	58.37	–

• The aggregation LLM adds 3–4 pp to Top-1 accuracy, evidencing the criticality of late-fusion self-attention and global prompt conditioning.
• Omitting ASR reduces accuracy to near the single-modality baseline on speech-centric datasets; on some benchmarks dominated by non-speech cues, removing the ASR may not be detrimental, underscoring the importance of modality/task alignment (Lin et al., 10 Jan 2026).

These findings are stable across diverse question styles, durations, and task types, with additional gains achieved by advanced wavelet fusion or temporally-aware frame selection (Gong et al., 27 May 2025Li et al., 2024).

6. Extensions, Downstream Applications, and Future Directions

QMAVIS underpins a broad spectrum of applications and further developments:

• Downstream Tasks: Audio/video clustering, comprehensive summarization, event/violence/riot detection via integrated multimodal signals and rule-based or learned post-processing (Pham et al., 2024).
• Embodied AI and Real-Time Analytics: Live operation on drones, robots, or surveillance systems, facilitated by asynchronous and streaming pipeline designs (Gong et al., 27 May 2025).
• Dialogue Understanding and Generation: Architectures such as the Conductor–Creator split in MAViD enable bidirectional, contextually-coherent multimodal dialogue and replay (Pang et al., 2 Dec 2025).
• Multi-Agent and Collaborative Reasoning: Multi-agent retrieval, temporal grounding, and aggregation protocols as detailed in MAGNET and XGC-AVis offer blueprints for scalable, distributed sensemaking over multimodal archives (Chowdhury et al., 8 Jun 2025Cao et al., 27 Sep 2025).
• Cross-Modal Grounding and Robustness: Integration of frequency-domain approaches, declarative guided attention, and adversarial/perturbed input management enhances robustness and semantic grounding in realistic, noisy scenarios.

A plausible implication is that future QMAVIS-style systems will increasingly leverage hybrid early/late fusion, agentic orchestration, and domain-adaptive multimodal representation learning, supported by rigorous evaluation against diverse, fine-grained annotated benchmarks. These directions address key community challenges in scalable and actionable multimodal intelligence.

7. Context Within the Modern Multimodal Pipeline Ecosystem

QMAVIS stands at the intersection of recent trends in robust long-form multimodal processing:

• It contrasts with early-fusion models such as VideoLLaMA2, which couple audio/video tokens before LLM decoding, and with single-pass monolithic architectures (Cheng et al., 2024).
• Unlike explicit cross-attention fusion blocks, QMAVIS relies on powerful LLM-based attention over interleaved high-level representations, simplifying integration and promoting model-agnostic scalability.
• The modular pipeline readily accommodates innovations in frequency-domain fusion, event grounding, streaming, and agentic planning, facilitating extensible, research-driven advancement of audio-video sensemaking systems.

In summary, QMAVIS defines a comprehensive, extensible methodology for intelligent, multimodal understanding of long-duration video and audio content, validated by substantial empirical improvements, standardized benchmarks, and architectural flexibility (Lin et al., 10 Jan 2026Xie et al., 27 Mar 2025Pham et al., 2024Gong et al., 27 May 2025Cao et al., 27 Sep 2025Chowdhury et al., 8 Jun 2025Li et al., 2024Pang et al., 2 Dec 2025Cheng et al., 2024).