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Textual interfaces impact LLM reasoning for audio-visual tasks

By PulseAugur Editorial · [1 sources] ·

Researchers have explored how textual interface design impacts prompt optimization for multimodal reasoning in audio-visual question answering tasks. They compared three interface designs: a direct inference system using a single omni-modal LLM (OmniLLM), a captioner-reasoner system, and an agentic architecture with multiple OmniLLMs. Experiments with Qwen3-Omni and Gemini models indicated that direct prompt optimization yields model-dependent improvements, while the other two systems offer more interpretable interfaces but are more challenging to optimize. AI

IMPACT Investigates how interface design can improve LLM reasoning capabilities in multimodal contexts.

RANK_REASON Research paper detailing a new method for multimodal LLM reasoning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Textual interfaces impact LLM reasoning for audio-visual tasks

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Research paper detailing a new method for multimodal LLM reasoning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Yi Wang, Tianzi Wang, Tong Lei, Dahan Wang, Peter Bell ·

    Designing Textual Interfaces To Enhance Llm Reasoning On Audio-Visual Question Answering

    arXiv:2610.20085v1 Announce Type: cross Abstract: Automatic prompt optimization (APO) has shown strong effectiveness for text-based LLMs and LLM systems, but its application to audio-visual understanding systems requires suitable textual interfaces through which multimodal reason…

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