AI Model2026
A multimodal, agentic LLM optimized for long‑horizon, visually grounded workflows — capable of operating browsers and terminals and autonomously executing and testing code. Open‑source weights are available and the family ships in mini, Pro and Max variants for different compute/quality tradeoffs.
AI Model2026
Multimodal agentic model for long-horizon computer and browser tasks, with visual self-correction and function-calling. The Pro variant is a 397B Mixture-of-Experts (≈17B active) model with a 262,144-token context window, Docker deployment recipes, and weights currently marked “coming soon.”
AI Video Papers2026
Translates natural-language instructions into executable programs that maintain an explicit, persistent global world state and compiles state-augmented 3D oriented bounding boxes into pixel-aligned conditioning signals for pretrained video generators. The approach decouples state evolution from rendering, enabling programmable entity control, off-screen state, and long-horizon interactive scenarios.
AI Audio2026
Ruibin Yuan, Hanfeng Lin +56Multimodal Art Projection (MAP), Hong Kong University of Science and Technology (HKUST)
Generates full songs from lyrics and a style prompt, producing vocals and accompaniment and exporting editable symbolic scores for melody/chord control. Uses symbolic planning with agentic multi-turn editing and runs local 48 kHz inference on a 24GB GPU.
AI Model2026
A multimodal Mixture-of-Experts foundation model with a million-token context window; uses a causal encoder–decoder layout and aggressive KV-cache compression (~890 bytes/token) to limit per-token activation to 8B/16B—designed for long-context, agentic, and multimodal workloads.
AI Model2026
Open-weights preview checkpoint for a multimodal reasoning model that generates text from text and image/video inputs, exposes adjustable reasoning effort and tool-calling, and supports an extended 262,144-token context for long-horizon tasks and agent-style workflows.
AI Model2026
Preview agentic language model for research and engineering workflows that turns research questions into executable, verifiable workflows via tool use and long-context reasoning; built on a 744B-parameter MoE (GLM-5.2) with MIT-licensed BF16 and FP8 checkpoints.
Presents an open 7B dense foundation model for mathematical reasoning and tool-assisted web search, supporting 256K-token context. Key features: hybrid gated sliding-window + global attention for efficient long-context training, FP8 Muon optimizer, progressive 16K→64K→256K curriculum, and open-source weights, data, and recipes.
AI Model2026
A pretrained autoregressive foundation LLM from Yandex that uses a hybrid MoE design (80B params, 3B active) and supports up to 262,144-token context. Notably strong on Russian factual knowledge, math and coding benchmarks; intended for research and fine-tuning rather than out-of-the-box production.
AI Agent Papers2026
Turns past discovery traces into replayable simulators so alternative exploration policies can be evaluated offline ('dreaming'), enabling fast, low-cost meta-level policy improvement for agent-driven discovery across coding, optimization, and GPU-kernel tasks.
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.
Describes DeepSeek-V4.1-Flash, a 552B-parameter multimodal Mixture-of-Experts LLM that supports up to 1M-token contexts and reduces KV cache footprint using Compressed Sparse Attention 2, FP4 KV caching, and SWA Bounded Replay to lower HBM/SSD and prefill costs.