Builds an editable, persistent 3D world state to drive iterative previsualization for film, games, and design — enabling local edits and recombinations instead of one-shot video regeneration. Uses separate stages for state construction, state evolution, and state access, with render-feedback camera refinement.
AI Video Papers2026
Externalizes persistent scene state into a camera-indexed world bank and designs a long-horizon teacher whose sparse-attention supervision is distilled into a three-step student, enabling responsive, low-latency interactive long-horizon video generation with bounded denoiser context.
AI Model2026
Provides an FP8-post-trained 27B multimodal causal language model with a native vision encoder, large-context support (262,144 native, extensible to 1,000,000), controllable thinking-mode reasoning, and compatibility with common inference engines for deployment.
AI Agent Papers2026
Supports multimodal scientific understanding, long-horizon agentic workflows and scientific tool interaction using a unified pipeline of multimodal pretraining, supervised fine-tuning and scalable multi-task reinforcement learning. Distinctive features include time-series modules for signal forecasting and a separate Memory Decoder that enables rapid domain specialization without changing the frozen 397B backbone.
AI Video Papers2026
Predicts future video frames conditioned on an observed frame, a language instruction, and a sequence of end-effector poses and gripper states for robot manipulation. Uses per-arm SE(3) geometric encoding (PRoPE-style), a lightweight depth branch, SAM3 masks with a frozen V-JEPA teacher, and distribution-matching distillation for efficient, consistent action-conditioned rollouts.
AI Model2026
Provides a 27B Qwen3.8 GGUF build for local/offline deployment, optimized with Unsloth Dynamic V3.0 quantization. Offers switchable thinking-mode, native vision-language understanding, and native long-context support (262k+ tokens).
AI Model2026
Provides a Mixture-of-Experts language model tuned for million-token contexts and agentic workflows, with DSpark speculative decoding, FP4/FP8 mixed-precision support, and vLLM/SGLang deployment recipes for low-latency production inference.
AI Model2026
A 27B Qwen3.8 vision‑language causal transformer quantized to NVFP4 for lower‑memory inference. Provides 262K native context (extensible to 1M), Unsloth Dynamic V3.0 4‑bit quantization and MTP support so Qwen3.8‑class multimodal workloads can run on 24GB‑class GPUs.
AI Agent Papers2026
Yiwei Li, Wanli Yang +11Affiliation: Meituan, Affiliation: University of Chinese Academy of [email protected]
Systematically evaluates LLM-driven autonomous agents on long-horizon AI research tasks using rule-based within-run metrics (Solution Framing, Execution, Feedback Control). Focuses on experience reuse and harness effects across 36 tasks and seven frontier models, finding agents act more like engineering optimizers than autonomous researchers.
Provides a large-scale benchmark and a human-aligned metric for humanoid whole-body motion tracking — about 153 hours of optical mocap from professional performers plus HumanScore trained on 12K human-labeled preference pairs to reveal contact, timing, and stability failures.
AI Agent Papers2026
Defines "agentic transactions" and an ACID-style reliability framework for LLM agents that manage long-horizon tasks over persistent environments. Implements an ACID-compliant data agent using exploration–execution–validation cycles, confidence-divergence checks, semantic isolation, and append-only durable workspaces.
AI Model2026
Provides a full GGUF quant ladder of an "abliterated" Qwen3.8-27B for local llama.cpp inference — includes every K-quant, embedded MTP speculative head, and optional vision projectors; refusal behavior was reduced at the weight level, so validate before production.