Moshi is the first real-time full-duplex spoken large language model that casts dialogue as speech-to-speech generation using parallel audio streams and an inner monologue of time-aligned text tokens.
wav2vec 2.0: A framework for self-supervised learning of speech representations
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MMTalker combines multi-resolution mesh sampling with residual graph convolutions and dual cross-attention to synthesize accurate 3D talking head motions from audio.
Voxtral Realtime is an end-to-end trained streaming ASR model that achieves Whisper-level transcription quality at 480ms delay after scaling pretraining across 13 languages.
Step-Audio 2 integrates a latent audio encoder, reasoning-centric reinforcement learning, and discrete audio token generation into language modeling to deliver state-of-the-art performance on audio understanding and conversational benchmarks.
Atlas reaches over 42% accuracy on Natural Questions with only 64 examples, outperforming a 540B-parameter model by 3% with 50x fewer parameters.
Contrastive learning trains unsupervised dense retrievers that beat BM25 on most BEIR datasets and support cross-lingual retrieval across scripts.
Codex achieves 28.8% pass@1 on HumanEval, rising to 70.2% with 100 samples per problem via repeated sampling.
A hybrid confidence-aware ASR training framework with learnable weights reduces Telugu medical WER from 24.3% to 15.8% and Kannada from 31.7% to 25.4%, outperforming standard fine-tuning.
Domain-adapted ECG foundation models with self-supervised pretraining and selective fine-tuning reach macro-AUROC 0.8509 for multi-label structural heart disease detection on the EchoNext benchmark.
citing papers explorer
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Moshi: a speech-text foundation model for real-time dialogue
Moshi is the first real-time full-duplex spoken large language model that casts dialogue as speech-to-speech generation using parallel audio streams and an inner monologue of time-aligned text tokens.
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MMTalker: Multiresolution 3D Talking Head Synthesis with Multimodal Feature Fusion
MMTalker combines multi-resolution mesh sampling with residual graph convolutions and dual cross-attention to synthesize accurate 3D talking head motions from audio.
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Voxtral Realtime
Voxtral Realtime is an end-to-end trained streaming ASR model that achieves Whisper-level transcription quality at 480ms delay after scaling pretraining across 13 languages.
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Step-Audio 2 Technical Report
Step-Audio 2 integrates a latent audio encoder, reasoning-centric reinforcement learning, and discrete audio token generation into language modeling to deliver state-of-the-art performance on audio understanding and conversational benchmarks.
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Atlas: Few-shot Learning with Retrieval Augmented Language Models
Atlas reaches over 42% accuracy on Natural Questions with only 64 examples, outperforming a 540B-parameter model by 3% with 50x fewer parameters.
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Unsupervised Dense Information Retrieval with Contrastive Learning
Contrastive learning trains unsupervised dense retrievers that beat BM25 on most BEIR datasets and support cross-lingual retrieval across scripts.
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Evaluating Large Language Models Trained on Code
Codex achieves 28.8% pass@1 on HumanEval, rising to 70.2% with 100 samples per problem via repeated sampling.
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Enhancing ASR Performance in the Medical Domain for Dravidian Languages
A hybrid confidence-aware ASR training framework with learnable weights reduces Telugu medical WER from 24.3% to 15.8% and Kannada from 31.7% to 25.4%, outperforming standard fine-tuning.
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Domain-Adapted Fine-Tuning of ECG Foundation Models for Multi-Label Structural Heart Disease Screening
Domain-adapted ECG foundation models with self-supervised pretraining and selective fine-tuning reach macro-AUROC 0.8509 for multi-label structural heart disease detection on the EchoNext benchmark.