6The Batch (DeepLearning.AI)·
Brain2Qwerty v2 translates MEG brain waves to text with 39% word error rate
Researchers from Meta and several French and Spanish institutions released Brain2Qwerty v2, a non-invasive brain-computer interface system that decodes magnetoencephalography (MEG) signals into text using a CNN/conformer encoder, a word-aligner, and a fine-tuned Qwen3-4B language model with per-subject LoRA adapters. The system achieves a 39% word error rate on 9 subjects, down from 43% in v1, trained on 90 hours of MEG recordings. A notable finding is that cross-subject training substantially outperforms single-subject training, suggesting a data-scaling dynamic analogous to LLM pretraining. Training code and v1 data have been open-sourced.
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Meta releases Brain2Qwerty v2: non-invasive brain-to-text decoding at 61% word accuracy
Meta AI released Brain2Qwerty v2, an end-to-end deep learning pipeline that decodes text from non-invasive magnetoencephalography (MEG) brain recordings in real time, achieving 61% word accuracy — up from 8% for prior non-invasive methods and approaching surgical-implant performance. The system was trained on ~22,000 sentences from nine participants and uses fine-tuned large language models on neural data to bridge noisy brain signals and coherent language. Meta is releasing full training code for both v1 and v2, and partner institution BCBL is releasing the v1 dataset. The work is part of Meta's broader Digital Brain Project and open neuroscience initiative.
BrainJanus: Unified autoregressive model for brain encoding/decoding across vision and language
BrainJanus is a unified model that integrates brain neural activity, vision, and language within a single autoregressive framework using next-token prediction. The system introduces a Unified Brain Tokenizer to quantize neural dynamics into discrete tokens aligned with visual and linguistic representations in a shared embedding space. It supports any-to-any generation including image-to-brain, text-to-brain, brain-to-image, and brain-to-text tasks, with reported zero-shot generalization and interpretable biological topography. The work positions itself as a general-purpose brain modeling paradigm at the intersection of neuroscience and multimodal AI.
Qwen releases Qwen3.5-35B-A3B-Base multimodal MoE model on Hugging Face
Qwen has released Qwen3.5-35B-A3B-Base, a 35B-parameter mixture-of-experts image-text-to-text base model on Hugging Face, activating approximately 3B parameters per forward pass. The model supports conversational use and is compatible with Azure deployment endpoints. With over 109K downloads, it represents a notable open-weights multimodal MoE release from the Qwen team.
Mistral Releases Voxtral Transcribe 2: State-of-the-Art Speech-to-Text with Sub-200ms Realtime Model
Mistral AI has released Voxtral Transcribe 2, a family of two speech-to-text models: Voxtral Mini Transcribe V2 for batch transcription and Voxtral Realtime for live applications. Voxtral Realtime features a novel streaming architecture with configurable latency down to sub-200ms, a 4B parameter footprint suitable for edge deployment, and is released as open weights under Apache 2.0. Voxtral Mini Transcribe V2 claims state-of-the-art word error rate on FLEURS at $0.003/min, outperforming GPT-4o mini Transcribe, Gemini 2.5 Flash, AssemblyAI, and Deepgram Nova on accuracy benchmarks. Both models support 13 languages with speaker diarization, word-level timestamps, and context biasing.
Meta Introduces TRIBE v2: Predictive Foundation Model for Human Brain Activity
Meta AI has released TRIBE v2, a foundation model that predicts high-resolution fMRI brain activity in response to visual, auditory, and language stimuli. Trained on data from over 700 healthy volunteers, it achieves a 70x resolution increase over comparable models and supports zero-shot generalization to new subjects, languages, and tasks. The release includes model weights, codebase, a research paper, and an interactive demo under a CC BY-NC license. Meta positions the work as a bridge between neuroscience and AI development, enabling hypothesis testing without requiring human subjects in every experiment.
Nemotron-Labs Audex-30B: Unified audio-text MoE LLM with no text regression
Nvidia's Nemotron Labs introduces Audex-30B-A3B, a 30B-parameter mixture-of-experts audio-text LLM built on the Nemotron-Cascade-2 text backbone. The model handles audio understanding, ASR, translation, TTS, and speech-to-speech generation within a single Transformer decoder by projecting audio into the text embedding space. Training used 157.4B audio tokens and 320.5B text tokens with multi-stage supervised learning, RL, and on-policy distillation. Model checkpoints are publicly released, and the authors report state-of-the-art audio performance with minimal regression on text reasoning and agentic capabilities.
Qwen releases Qwen3.5-122B-A10B multimodal MoE model on Hugging Face
Qwen has released Qwen3.5-122B-A10B, a 122B-parameter mixture-of-experts image-text-to-text model with 10B active parameters, published on Hugging Face. The model supports conversational use and is compatible with Azure deployment endpoints. High download counts (840K) and likes (564) suggest rapid community uptake shortly after release.
Qwen releases Qwen3.5-35B-A3B multimodal MoE model on Hugging Face
Qwen has released Qwen3.5-35B-A3B, a 35B-parameter mixture-of-experts image-text-to-text model with approximately 3B active parameters, published on Hugging Face. The model supports conversational use and is compatible with Azure deployment endpoints. With over 2.8 million downloads and 1,400+ likes, it has seen substantial community uptake.