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Training voice agents in an audio-native environment can more than double their task success rates and enhance efficiency without external dependencies.
The Qwen3-Omni model can reconstruct complex narratives from garbled audio inputs, revealing a hidden layer of reasoning that challenges our understanding of audio language processing.
No single perspective is sufficient for art interpretation; MMArt reveals that each perspective uniquely enhances understanding and retrieval of visual art.
Forget relying on implicit reasoning: A-MAR's explicit reasoning plans unlock better artwork understanding by strategically retrieving relevant evidence.
Speaker drift, a subtle but pervasive flaw in modern TTS, can now be automatically detected by prompting LLMs with geometric representations of speaker embeddings.
