Chain of Modality dynamically orchestrates multimodal input topologies and bifurcates cognitive execution to overcome static fusion biases in Omni-MLLMs.
Echoink-r1: Exploring audio-visual reasoning in multimodal llms via reinforcement learning.arXiv preprint arXiv:2505.04623
5 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 5representative citing papers
MTSS replaces monolithic video captions with factorized streams and relational grounding, yielding reported gains in understanding benchmarks and generation consistency.
Omni-LLMs show systematic failures at cross-modal coreference; a new dataset and both training-free and training-based fixes produce substantial gains on 13 models.
AVRT transfers reasoning to audio-visual models by distilling traces from single-modality teachers via LLM merger followed by SFT cold-start and RL, achieving SOTA on OmniBench, DailyOmni, and MMAR with 3B/7B models.
Relax is a new RL training engine with omni-native design and async execution that delivers up to 2x speedups over baselines like veRL while converging to equivalent reward levels on Qwen3 models.
citing papers explorer
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Chain of Modality: From Static Fusion to Dynamic Orchestration in Omni-MLLMs
Chain of Modality dynamically orchestrates multimodal input topologies and bifurcates cognitive execution to overcome static fusion biases in Omni-MLLMs.
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Script-a-Video: Deep Structured Audio-visual Captions via Factorized Streams and Relational Grounding
MTSS replaces monolithic video captions with factorized streams and relational grounding, yielding reported gains in understanding benchmarks and generation consistency.
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Cross-Modal Coreference Alignment: Enabling Reliable Information Transfer in Omni-LLMs
Omni-LLMs show systematic failures at cross-modal coreference; a new dataset and both training-free and training-based fixes produce substantial gains on 13 models.
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AVRT: Audio-Visual Reasoning Transfer through Single-Modality Teachers
AVRT transfers reasoning to audio-visual models by distilling traces from single-modality teachers via LLM merger followed by SFT cold-start and RL, achieving SOTA on OmniBench, DailyOmni, and MMAR with 3B/7B models.
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Relax: An Asynchronous Reinforcement Learning Engine for Omni-Modal Post-Training at Scale
Relax is a new RL training engine with omni-native design and async execution that delivers up to 2x speedups over baselines like veRL while converging to equivalent reward levels on Qwen3 models.