XGC-AVis: Multi-Agent AV Framework

• XGC-AVis is a multi-agent system that integrates video, audio, subtitles, and non-speech descriptors into a temporally aligned representation for question answering.
• It employs a four-stage pipeline—perception, planning, execution, and reflection—to precisely retrieve and reconcile temporal segments for improved inference.
• Validated on the XGC-AVQuiz benchmark, the framework demonstrates enhanced synchronization and quality perception, addressing key limitations in current multimodal large models.

Searching arXiv for papers on “XGC-AVis” and related acronym collisions to ground the article. XGC-AVis is a multi-agent framework for audio-visual content understanding that was introduced to improve multimodal large models (MLLMs) in two specific capabilities: audio-video temporal alignment and key video segment retrieval. It organizes inference into four stages—perception, planning, execution, and reflection—and is paired with XGC-AVQuiz, a benchmark containing 2,232 videos and 2,685 question-answer pairs across 20 tasks. The system is presented as improving these capabilities without additional training, particularly on temporal localization and quality perception tasks, which the accompanying study identifies as persistent weaknesses of current MLLMs (Cao et al., 27 Sep 2025).

1. Definition and nomenclature

In the relevant 2025 literature, XGC-AVis denotes a multi-agent audio-visual collaboration system designed to interweave video, audio, subtitles, and audio descriptions into a temporally aligned representation for question answering and related multimodal reasoning. Its target setting is not generic multimodal interaction, but audio-visual understanding in which non-speech audio, synchronization, temporal localization, and selective retrieval of relevant segments are central failure modes for baseline MLLMs (Cao et al., 27 Sep 2025).

The name sits within a crowded acronym landscape. In separate literatures, AVIS refers to “Autonomous Visual Information Seeking,” an LLM-based framework for knowledge-intensive visual question answering (Hu et al., 2023); “Audio-Visual Instance Segmentation,” an instance-level segmentation-and-tracking task for sounding objects in video (Guo et al., 2023); and “Autoregressive Video Inverse problem Solver,” a diffusion-based framework for zero-shot video restoration (Kwon et al., 20 May 2026). Separately, XGC is also the acronym of the X-point Gyrokinetic Code used in plasma-physics research, including XGC1, XGC-S, and edge neoclassical studies (Hager et al., 2017, Cole et al., 2019, Mollén et al., 2020, Ku et al., 6 Oct 2025). In this usage, however, XGC-AVis specifically names the 2025 multi-agent audio-visual system rather than a plasma-simulation toolchain.

A common misconception is to treat XGC-AVis as a simple prompting wrapper around a single MLLM. The framework is instead defined by explicit role separation across multiple agents, structured temporal retrieval, and a conflict-resolution stage. Another misconception is that transcripts alone are sufficient for robust audio-visual reasoning; the benchmark analysis explicitly argues that current models remain weak on non-speech audio, temporal alignment, and quality perception even when subtitles are available.

2. Architectural design

XGC-AVis is organized as a four-stage pipeline: perception, planning, execution, and reflection. The design objective is to transform raw multimodal streams into aligned evidence, retrieve the most relevant temporal intervals, answer using only those intervals, and reconcile divergent interpretations when independent reasoning paths disagree (Cao et al., 27 Sep 2025).

Stage	Primary role	Main components
Perception	Convert raw input into temporally grounded evidence	Deepgram translator, r1-aqa audio descriptor
Planning	Identify relevant time segment(s)	Aria, Qwen2.5-Omni
Execution	Answer from retrieved segments	Gemini 2.0 Flash, Gemini 2.0 Flash
Reflection	Resolve disagreement and return final answer	Gemini 2.0 Flash as Decider

The architectural principle is multimodal interweaving rather than modality concatenation in isolation. Audio is segmented; speech is converted to subtitles; non-speech events are described semantically; and these outputs are aligned with video frames to form coherent multimodal units. This suggests a retrieval-first regime in which temporal grounding is treated as a prerequisite for answering, rather than as a by-product of end-to-end multimodal fusion.

The design also uses redundancy deliberately. Two planners inspect the same aligned evidence but with different model capabilities, and two executors answer independently from the respective retrieved segments. The reflection stage is then invoked only when necessary. A plausible implication is that the framework uses disagreement not as noise to be suppressed but as a diagnostic signal for ambiguous or weakly localized questions.

3. Operational workflow

The perception stage converts raw audio-visual input into richer evidence. Audio is first split into equal-length clips. Deepgram transcribes speech into subtitles with timestamps, while r1-aqa describes non-speech audio events and handles segments differently depending on whether speech is detected. Video frames, audio segments, subtitles, and audio descriptions are then interwoven and temporally aligned into coherent multimodal units (Cao et al., 27 Sep 2025).

The planning stage performs temporal retrieval. Planner 1 is Aria, which can process video plus text, and Planner 2 is Qwen2.5-Omni, which can process video, audio, and text. Both planners receive video tokens, audio tokens, subtitles, audio descriptions, and the question, and are prompted to identify relevant time segment(s) and provide reasoning. The paper specifies a JSON-like output format:

{ "time_segments": [ { "start_time": ..., "end_time": ..., "reasoning": ... } ] }

The execution stage answers the question using only the retrieved segment(s). Both executors are Gemini 2.0 Flash, but each executor is conditioned on the plan produced by its corresponding planner. Each receives the extracted time segments, subtitles, audio descriptions, video frames, and planner reasoning. If a planner cannot find a relevant segment, the entire video is treated as relevant by default. Each executor returns an option letter and a reasoning path.

The reflection stage resolves conflicts. If the two executors agree, the answer is returned directly. If they disagree, XGC-AVis merges both planners’ segments, both executors’ answers, and both reasoning paths, and sends them to a Decider, also based on Gemini 2.0 Flash, which selects the final answer. Factually, the workflow therefore couples retrieval, answer generation, and adjudication; interpretively, this resembles a constrained multi-agent deliberation procedure centered on temporal evidence selection.

4. XGC-AVQuiz benchmark

XGC-AVQuiz was introduced together with XGC-AVis as the first benchmark intended to comprehensively assess MLLMs in both real-world and AI-generated scenarios. It contains 2,232 videos, 2,685 question-answer pairs, 20 tasks, 4 categories, 10 major domains, and 53 fine-grained categories. The benchmark’s two stated innovations are AIGC Scenario Expansion and a Quality Perception Dimension (Cao et al., 27 Sep 2025).

The video corpus comprises 1,102 professionally generated content (PGC) videos, 753 user-generated content (UGC) videos, and 377 AI-generated content (AIGC) videos. The average video length is 43 seconds, and 36% of videos are between 30 seconds and 1 minute. The AIGC material includes unrealistic audio-visual scenes, virtual avatar content from YouTube, Kling platform content, and examples with A/V misalignment, semantic inconsistency, and unnatural audio. The quality-perception setting explicitly tests A/V synchronization, content consistency, distortion type, distortion localization, and overall A/V quality.

The benchmark defines four categories with a gradual difficulty progression from recognition to localization to reasoning to quality perception. Representative tasks include audio source recognition, music recognition, counting, audio source localization, speaker localization, music localization, music understanding, event causal reasoning, human interaction reasoning, identity reasoning, audio causal reasoning, A/V prediction, emotion reasoning, A/V content matching, music temporal matching, audio temporal matching, speech temporal matching, distortion type classification, distortion localization, and A/V overall quality.

Data collection is correspondingly heterogeneous. PGC material includes 108 English-language films and TV shows spanning 14 themes, trimmed into 10-minute clips, plus 100 music videos in Korean, English, and Japanese. UGC collection includes 1,000 short videos from YouTube and TikTok, while livestream data includes 100 Twitch recordings covering game, chat, and singing streams. Low-quality content is drawn from SJTU-UAV, AGAVQA-3k, and simulated livestream glitches such as audio stuttering, A/V desynchronization, and video freezing. Annotation was performed by 47 professional annotators, with QA pairs distributed evenly across all 20 tasks.

Evaluation is multiple choice with 2 to 6 answer options and exactly one correct answer. Models are evaluated zero-shot using 15 uniformly sampled frames per video and the complete audio segment. Deepgram API is used to generate subtitles for most models, ChatGPT-Audio is used for ChatGPT-4o, and non-compliant outputs are scored with Qwen-plus under a five-round voting strategy with manual verification when needed.

5. Empirical performance and ablation findings

On XGC-AVQuiz, XGC-AVis reports the strongest overall result among the compared systems, with average accuracy 58.2, compared with 51.3 for Gemini 2.0 Flash, a reported improvement of +6.8%. Category-level results are 59.5 for A/V Recognition, 51.7 for A/V Localization, 51.0 for A/V Perception, and 69.3 for A/V Reasoning. On the Daily-Omni benchmark, XGC-AVis reports 71.5 versus 61.8 for Daily-Omni, corresponding to +3.7% (Cao et al., 27 Sep 2025).

Performance remains strongest across duration buckets: 57.1 for videos shorter than 10 seconds, 62.1 for 10–30 second videos, 53.1 for 30 seconds to 1 minute, 67.4 for 1–2 minute videos, and 63.6 for videos longer than 2 minutes. The largest gain over Gemini 2.0 Flash is +4.0% on 10–30 second videos. Across audio types, XGC-AVis reports 62.7 on speech, 57.5 on sound, 51.6 on music, and 54.8 on mix, with the largest gain over Gemini 2.0 Flash being +8.6% on speech and +4.9% on mix. Across content types, it reports 63.8 on PGC, 54.7 on UGC, and 52.6 on AIGC, with the largest gain over the second-best system being +8.1% on UGC.

The ablation study isolates the two-planner design. The Gemini 2.0 Flash baseline reports Recognition 54.4, Localization 49.3, Perception 40.2, and Reasoning 65.9. XGC-AVis without Planner 2 reports 57.1, 48.3, 43.5, and 65.7. XGC-AVis without Planner 1 reports 48.0, 39.9, 41.4, and 52.5. Full XGC-AVis reports 59.5, 51.7, 51.0, and 69.3. The paper’s stated takeaway is that removing either planner hurts performance and that the improvement is most pronounced in A/V quality perception.

These results are used to support a broader analytical claim: simple modality concatenation is often insufficient. The paper reports that many open-source VLMs do not benefit much from merely adding audio transcripts, that many open-source OLMs struggle to integrate audio, video, and subtitles, and that even strong closed-source MLLMs remain weak on quality perception and temporal alignment. XGC-AVis is therefore positioned not merely as a stronger backbone, but as an inference-time system for structured temporal retrieval and multi-agent reconciliation.

6. Limitations, interpretive significance, and relation to adjacent research

The paper’s discussion emphasizes that current MLLMs still struggle with quality perception and temporal alignment, and XGC-AVis does not eliminate these difficulties so much as improve performance relative to direct prompting and baseline systems. The reported unresolved weaknesses include audio-video synchronization, distortion localization, music understanding, general sound understanding, and temporal localization. This suggests that the framework is best understood as a stronger inference protocol for present MLLMs rather than a complete solution to audio-visual grounding (Cao et al., 27 Sep 2025).

Another limitation is implicit in the benchmark design itself: XGC-AVQuiz stresses quality-degraded and AI-generated scenarios, so the reported gains are tied to a problem formulation in which low-level sensory cues and high-level semantic reasoning must be integrated. A plausible implication is that the framework’s main advantage may be largest precisely where question answering depends on selective temporal evidence rather than on coarse clip-level semantics.

Within the broader AVIS literature, XGC-AVis occupies a distinct position. “Autonomous Visual Information Seeking” treats visual question answering as dynamic search over tools and external knowledge rather than as audio-visual temporal alignment (Hu et al., 2023). “Audio-Visual Instance Segmentation” addresses identification, segmentation, and tracking of sounding object instances at the pixel level rather than segment-level multi-agent reasoning over MLLMs (Guo et al., 2023). The diffusion-based AVIS framework for video inverse problems is a restoration method rather than a question-answering or understanding system (Kwon et al., 20 May 2026). XGC-AVis thus belongs most directly to the emerging line of work in which multimodal reasoning is decomposed into specialized agents, explicit retrieval over temporal spans, and post hoc reconciliation of competing inferences.

Its central contribution is therefore not a new foundation model, but a systems-level recipe: richer perception through subtitle and non-speech description generation, dual-planner temporal retrieval, segment-focused execution, and reflective conflict resolution, all evaluated on a benchmark deliberately constructed to expose weaknesses in synchronization, quality perception, and AI-generated content understanding.