AI Video Papers2026
Serves interactive, long-lived streaming video-generation sessions by jointly scheduling session placement and GPU autoscaling to meet tight per-chunk latency. Combines migration-aware placement, load-driven autoscaling, coalesced chunk processing, GPU–CPU offloading and NCCL GPU–GPU migration; reports ~37% reductions in worst-case per-chunk latency and GPU operating cost.
AI Dataset2026
Provides agentic instruction‑tuning trajectories for software‑engineering tasks, formatted for supervised fine‑tuning and agent training. Contains multi‑file edits, tests, docs and structured agent traces (≈5,115 records, 1.9 GiB). Intended for commercial use; licensed CC‑BY 4.0 with additional permissive licenses.
AI Audio2026
Generates streaming, low‑latency neural speech for real‑time dialogue by autoregressively producing audio frames as text arrives; joint text–speech training preserves natural prosody. Optimized for vLLM streaming (~50 ms first chunk), supports short‑clip voice cloning and four languages.
AI Model2026
Provides a pre-quantized NVFP4 checkpoint of GLM-5.2 for long-context reasoning and coding; reduces model footprint so GLM-5.2 can run on multi‑GPU Blackwell nodes and is ready for inference with SGLang and vLLM.
AI Model2026
NVFP4-quantized variant of Qwen3.6-27B that reduces parameter bits from 16 to 4, cutting disk and GPU memory requirements by ~2.5× while keeping comparable benchmark accuracy; ready for vLLM-based inference on NVIDIA hardware and supports long, multimodal contexts.
AI Dataset2026
Provides SimReady 3D assets for robotics and industrial simulation. The OpenUSD content is prepared for simulation workflows, helping teams assemble environments for physical AI.
AI Model2026
Deployment-optimized hybrid MoE LLM (75B total / 9.3B active) produced via Iterative Puzzle compression and Multi-Token Prediction to double server throughput and raise single-GPU concurrency; designed for multilingual reasoning, long-context generation, and high-volume agentic/chat deployments.
Embodied AI2026
Autonomously generates, iteratively debugs, and composes robot control programs into a reusable skill library for discovering and refining manipulation skills across simulation and real robots. Uses a code-as-policy continual learning loop—accumulating validated sensorimotor skills instead of model weights—to tackle long-horizon household and benchmark tasks.
AI Model2026
Generates 2048-d multilingual text embeddings for retrieval and semantic search, suited for RAG and dense retrieval. Pruned and distilled from the Ministral-3 family into a ~1.14B BF16 model, supports long contexts (up to 32,768 tokens) and optimized for NVIDIA GPU inference.
AI Dataset2026
Provides multi-turn tool-calling examples grounded in SEC filings for fine-tuning financial reasoning agents, covering tasks such as numerical reasoning, financial modeling, and market analysis.
AI Model2026
Adds vision to GLM-5.2 by attaching a MoonViT encoder and a trained 49.5M-parameter PatchMerger projector to enable image→text multimodal reasoning; text and vision backbones are frozen, uses NVFP4 quantized weights and targets Blackwell B200 GPUs.
AI Model2026
Generates and reasons about multimodal physical-world content—text, images, video and action trajectories—conditioned on text, images, video and robot/vehicle action inputs. An edge-sized (4B) Mixture‑of‑Transformers omni-model optimized for single‑GPU inference and Physical AI tasks (image→video, action prediction, robot policy).