Speculative Interaction Agents achieve 1.3-2.2x speedups for real-time tool-calling agents via async I/O decoupling and speculative calls, with clock-based training for small edge models.
Moonshine: Speech recognition for live transcription and voice commands
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3representative citing papers
A 33M-parameter raw-audio CTC model with 19-block RoPE E-Branchformer achieves 9.19% whitespace-insensitive IPA CER on a 16,660-utterance 41-language test set, outperforming a 575M-parameter PhoneticXEUS baseline at 9.78% under matched normalization.
citing papers explorer
-
Speculative Interaction Agents: Building Real-Time Agents with Asynchronous I/O and Speculative Tool Calling
Speculative Interaction Agents achieve 1.3-2.2x speedups for real-time tool-calling agents via async I/O decoupling and speculative calls, with clock-based training for small edge models.
-
BranchShine: Compact Raw-Audio-to-IPA Transcription with a RoPE E-Branchformer Encoder
A 33M-parameter raw-audio CTC model with 19-block RoPE E-Branchformer achieves 9.19% whitespace-insensitive IPA CER on a 16,660-utterance 41-language test set, outperforming a 575M-parameter PhoneticXEUS baseline at 9.78% under matched normalization.
- Diagnostic-Driven Layer-Wise Compensation for Post-Training Quantization of Encoder-Decoder ASR Models