We have 7 of 46 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
The results demonstrate that full-duplex interaction, speech recognition and generation, general language capabilities, and external tool use can be integrated in a single open speech-to-speech model without sacrificing real-time conversational behavior.
Voice Memory, a inference-only scheme for agentic speech recognition: at stream time, a frozen corrector reads a single per-domain memory and decides per utterance whether to act on the hypothesis or abstain and keep the 1-best, and transfers across corrector families and adds zero parameters to the inference path.
This work proposes an efficient method to add streaming ASR capabilities to an existing duplex S2S model by introducing a lightweight ASR head in parallel to the agent text head, enabling real-time user transcription while preserving full-duplex conversational capabilities including turn-taking and barge-in handling.
VoiceChat-TTS is proposed, a low-latency, continuous, and streamable text-to-speech model for interactive agents that enables always-on, responsive speech generation while preserving modularity and high speech quality.
This work proposes an always-on attention-coordination layer that mediates this interface and allocates human attention across one or more working agents, and introduces JarvisBench to evaluate both directions of this coordination.
JarvisBench, a benchmark for measuring the dual value of mediation in long-horizon agent workflows, is introduced and preliminary results suggest that Jarvis-style mediation can provide trace-grounded responses to user questions and improve task performance when sparse user guidance is injected at appropriate moments.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
