The HEAR Framework proposes a Vision-Sound-Language-Action paradigm for real-time, sound-centric manipulation in embodied agents. This approach addresses the limitations of existing models by incorporating continuous auditory awareness and causal persistence. The framework includes four components: a streaming Historizer, an Envisioner, an Advancer, and a Realizer policy. The authors also introduce OpenX-Sound for pretraining and HEAR-Bench, a sound-centric manipulation benchmark.