AI Model2026
Generates unified 768‑dimensional embeddings for text (including code), images, video and audio to enable cross‑modal semantic search and retrieval. Supports task instruction prefixes, Matryoshka truncation to 128/256/512/768 dims, and modular encoders for on‑device use under an Apache‑2.0 license.
AI Model2026
Turns Qwen3.5 into a 3-way NLI cross-encoder (entailment/contradiction/neutral) for zero-shot reranking, grading, content guarding, or action selection. Provides a pretrained Qwen3.5-4B checkpoint, helper utilities (OpenJevCrossEncoder, LatentMLPHead), and optional 35B MoE latent heads for per-task scoring.
AI Model2026
A 29B-parameter mixture-of-experts LLM for long-context reasoning and agent-style workflows, with native 256K context and Ascend NPU co-optimizations. Compatible with Transformers/vLLM ecosystems; suitable for long-horizon agents and domain fine-tuning.
AI Model2026
Fast, non-autoregressive decision engine that answers typed questions (choice/score/noul) over text or JSON states with calibrated probabilities and confidences in a single forward pass. Suited for routing, triage and moderation workflows; includes a Router to pick checkpoints per request.
AI Model2026
A non-autoregressive, multilingual System‑1 decision model that answers typed questions (choice/score/noul) over text or JSON in a single forward pass with probability distributions. Key differences: calibrated-probability design, sub-35ms GPU latency, and built-in multilingual routing; requires per-workflow recalibration and validation on long or low-resource languages.
AI Model2026
Converts multimodal inputs (text, image, audio, video) plus a question and options into calibrated probability distributions over choices. Built on Gemma 4 12B with a 30,000-question fine-tune, optimized for per-question decision classification and low-latency inference (Apache-2.0).
AI Model2026
Generates a single, ready-to-send short message (email, text, or note) tuned to sound human; a 27B fine-tuned LLM with open Apache-2.0 weights, optimized to return one concise draft rather than multiple options or explanatory preamble.
AI Model2026
A sparse-MoE multimodal LLM fine-tuned with large-scale mixed reinforcement learning, supporting text/image/video/audio and a 1M-token context window; designed for long-horizon agentic tasks, coding and visual reasoning with groupwise grading and on-policy distillation.
AI Audio2026
Performs low-latency streaming speech-to-text, emitting one token per selectable 80/120/160 ms clock with configurable transcription delay and a 30s rolling KV cache for unlimited 24/7 transcription. Bilingual (zh/en) and includes semantic VAD.
AI Model2026
Scans long documents rendered as compressed page-images, locates relevant pages, and selectively expands only those pages to full text for question answering; built on Qwen3.5-9B, supports 5x/10x/15x compression and is released under Apple’s research-only model license.
AI Model2026
Open-weights multimodal LLM checkpoint designed for 1M-token long-context agent runs that combines large-scale on-policy RL with groupwise grading for iterative self-improvement. Key traits: sparse MoE backbone (≈1.02T total / 42B active), text-only output with image/video/audio inputs, MIT-licensed weights on HuggingFace—suited for long-horizon agents and research at significant infra cost.
Xiaoqiang Wang, Mengyang Xiong +2Affiliation: Département d’informatique et de recherche opérationnelle, Université de Montréal, Montréal, QC, Canada, Affiliation: Mila, Quebec AI Institute, Montréal, QC, Canada +1
Adds token-conditioned quantum residual branches to a frozen masked-diffusion language model: a lightweight hypernetwork emits continuous quantum-circuit coordinates per token, executes a shared sparse IQP-style circuit, and injects classically-expressible expectation readouts back into transformer blocks. Trains only the added branches, scaling to 16–64 qubits with analytic, linear-cost readouts.