Who Says What: Symbolic Trimodal Binding Mechanisms in Audio-Visual LLMs

arXiv:2609.31193 · cs.CV, cs.SD, eess.AS · Submitted 2026-09-25 · Read on arXiv

cs.CV, cs.SD, eess.AS

Submitted: 2026-09-25

Updated: 2026-09-25

Terminology

• Fine-Tuning — 13× in this paper · explained in 5 episodes
• Grounding — 13× in this paper · explained in 3 episodes
• Accuracy — 12× in this paper · explained in 1 episode
• Active Speaker Detection — 12× in this paper
• Benchmark — 11× in this paper · explained in 2 episodes
• Generalization — 7× in this paper · explained in 13 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Alignment — 5× in this paper · explained in 1 episode
• Visual Grounding — 5× in this paper · explained in 2 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Hallucination — 3× in this paper · explained in 3 episodes
• Interpretability — 3× in this paper · explained in 15 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Generalizability — 2× in this paper · explained in 6 episodes
• Question Answering — 2× in this paper
• Robustness — 2× in this paper · explained in 5 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Causal Inference — 1× in this paper · explained in 4 episodes
• Core — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• LoRA Fine-Tuning — 1× in this paper · explained in 3 episodes
• Low-Rank Adaptation (LoRA) — 1× in this paper · explained in 9 episodes
• Representational Similarity Analysis — 1× in this paper
• Scaffolding — 1× in this paper · explained in 3 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Sources

• Qwen2.5-VL Technical Report
• Seeing to Generalize: How Visual Data Corrects Binding Shortcuts
• DiaDem: Advancing Dialogue Descriptions in Audiovisual Video Captioning for Multimodal Large Language Models
• VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs
• MiniCPM-o 4.5: Towards Real-Time Full-Duplex Omni-Modal Interaction
• Discovering Variable Binding Circuitry with Desiderata
• Uncovering Grounding IDs: How External Cues Shape Multimodal Binding
• Fork-Merge Decoding: Enhancing Multimodal Understanding in Audio-Visual Large Language Models
• ViKey: Enhancing Temporal Understanding in Videos via Visual Prompting
• Rethinking Visual Prompting for Multimodal Large Language Models with External Knowledge
• Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration
• See, Hear, and Understand: Benchmarking Audiovisual Human Speech Understanding in Multimodal Large Language Models
• MUSAN: A Music, Speech, and Noise Corpus
• video-SALMONN 2: Caption-Enhanced Audio-Visual Large Language Models
• D-ORCA: Dialogue-Centric Optimization for Robust Audio-Visual Captioning
• Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small
• Visual Prompting in Multimodal Large Language Models: A Survey
• SocialOmni: Benchmarking Audio-Visual Social Interactivity in Omni Models
• Qwen2.5-Omni Technical Report
• Qwen3-Omni Technical Report

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