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Natural-language critiques can transform how we evaluate and optimize song generation models, leading to more human-aligned outputs.
LLMs struggle with personal information retrieval, with the best model only achieving 57.3% accuracy on a new benchmark designed to evaluate mobile assistant capabilities.
IACM-RL reduces infinite loops and stale context errors by proactively managing dynamic user intents, setting a new standard for robust tool invocation.
Finally, a fully open-source, reproducible system for long-form song generation is here, complete with licensed data, code, and a Qwen-based model that rivals closed-source systems.
