arXiv Computer Vision
SleuthVLM: Do We Really Need Audio-in-Video for Omni Video Understanding?
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arXiv Computer Vision
Sep 7
Training-Free Speech-Centric Omni Understanding with Frozen VLMs
The paper introduces Training-Free Omni (TFO), a plug‑and‑play framework that transforms a frozen vision‑language model (VLM) into a speech‑centric omni model without modifying its architecture or requiring multimodal re‑alignment. TFO leverages Whisper to generate confidence‑filtered, timestamped transcripts and routes them through the VLM’s existing language interface, leaving the visual pathway untouched. Evaluations on 56 benchmarks across 21 languages show that TFO matches or surpasses native omni models on audio‑visual tasks, improves audio‑only performance, and preserves strong visual and reasoning capabilities.
arXiv AI
Jun 9
AVI-Bench: Toward Human-like Audio-Visual Intelligence of Omni-MLLMs
arXiv:2606. 07643v1 Announce Type: cross Abstract: Recent advances in Omni-Multimodal Large Language Models (Omni-MLLMs) have enabled strong integration of vision, audio, and language.
arXiv Computer Vision
Sep 1
TraceAV-Bench: Benchmarking Multi-Hop Trajectory Reasoning over Long Audio-Visual Videos
arXiv:2605.07593v2 Announce Type: replace Abstract: Real-world audio-visual understanding requires chaining evidence that is sparse, temporally dispersed, and split across the visual and auditory str...
arXiv AI
Oct 2
SONIC-O1: A Real-World Benchmark for Evaluating Multimodal Large Language Models on Audio-Video Understanding
SONIC‑O1 is a new benchmark designed to evaluate multimodal large language models on audio‑video understanding. It contains 60 hours of 231 clips across 13 real‑world conversational domains, with 4,958 human‑verified annotations and demographic metadata. The benchmark tests open‑ended summarization, multiple‑choice question answering, and temporally grounded reasoning, revealing performance gaps between model families and across demographic groups.
arXiv AI
Sep 18
AVTrace: Diagnosing Audio-Visual Temporal Reasoning in Omni Models
AVTrace is a diagnostic suite designed to evaluate audio‑visual temporal reasoning in omni models. It covers tasks such as onset and span grounding, synchronization, next‑step prediction, cross‑modal localization, chain parsing, and event‑conditioned comprehension, providing 34,114 training examples and balanced development and test splits. Five open omni models were tested, all scoring below the majority‑label baseline on synchronization verification and showing low performance on chain parsing and event‑conditioned tasks, while parameter‑efficient temporal post‑training improved some metrics.
Hugging Face Trending Papers
Jul 21
OmniReasoner: Thinking with Long Audio-Video via Native Tool Use
Long audio-video reasoning is difficult for omnimodal LLMs because the decisive evidence is often sparse, cross-modal, and too expensive to preserve with uniformly high-fidelity inputs. We introduce OmniReasoner, a tool-use post-training framework for Thinking with Long Audio-Video: omni-modal LLMs learn, via supervised fine-tuning and reinforcement learning, to decide whether and where to call a zoom-in tool before answering.