AI Model2026
Provides 2-bit quantized weights of Qwen3.8-27B (~10.15 GB) for local deployment, enabling the full 27B parameter model to run on a single 24 GB GPU with long-context support. Delivered as safetensors plus a companion SGLang runtime; measured to match FP8 reference on common benchmarks with small or no quality loss.
AI Video Papers2026
Xianyun Sun, Chaoyou Fu +7Affiliation: Project Leader & Corresponding AuthorProject Page: https://xianyunsun.github.io/OmniAssistBench/, Affiliation: Nankai University +1
Benchmarks assistant-style, multi-turn interaction for omni-modal LLMs on real-time video by reverse-engineering Internet clips into guided multi-turn interactions. It provides predefined priors and segment-level constraints so models must follow exact routes while being evaluated on answer correctness, timing, visual-prompt handling, and context retention.
AI Model2026
Provides FP8-quantized Hugging Face weights and config for Qwen3.8-Flash-Next (block size 128), preserving near-original performance. Compatible with Transformers, vLLM, SGLang and TokenSpeed; intended for efficient deployment of a 125B multimodal causal LM with very long context support.
AI Video Papers2026
Generates enterable omnimodal world-model rollouts that follow continuous 6-DoF camera control while jointly producing 720p video, environmental sound, music and speech. Uses dataset-level motion calibration, a specialized data engine, progressive training and autoregressive post-training to support long-horizon first- and third-person interaction.
AI Agent Papers2026
Assesses mobile planning agents' ability to call tools, plan long-horizon workflows, and coordinate sub-agents in realistic, interactive phone scenarios via a stateful executable sandbox. Covers 13 domains, 212 tools, evidence-based verification, and tests memory, skill usage, permission and runtime constraints.
AI Dataset2026
Evaluates whether tool-using LLM agents reliably complete stateful business workflows via 507 executable agent–tool–user tasks across retail, travel, auto insurance, neobank, and IT/HR consulting. Provides browsable Parquet tables for tasks, scenarios, and agent instructions; v1.0 is intended for evaluation-only.
AI Agent Papers2026
Evaluates AI agents' ability to complete end-to-end scientific workflows by releasing and assessing 97 tasks from a 300-task FrontierChallenge suite across chemistry, materials, life science, and electrochemistry. Finds that top agent configurations achieved only a 20.6% pass rate despite high partial scores, revealing a gap between partial progress/confident completion claims and actual complete scientific deliverables.
AI Model2026
Generates L2-normalized multimodal embeddings (default 4,096‑D) for text, images, videos and visual documents, supporting interleaved inputs and flexible dimension truncation (Matryoshka). Designed for cross-modal retrieval, ranking and downstream retrieval systems; audio is not supported.
AI Dataset2026
A 10‑billion‑document retrieval benchmark with per‑document 768‑dim unit‑norm dense embeddings and mGTE sparse embeddings, FineWeb text/metadata, and exact top‑1000 MS MARCO ground truth for ~120k queries. Built for large‑scale evaluation of dense/sparse/hybrid retrieval, filtered search, indexing, ANNS algorithms, and embedding compression.
AI Dataset2026
Evaluates retrieval-augmented generation by decomposing user queries into sub-queries and answers into atomic claims, scoring retrieval by query coverage and generation by claim verifiability. Reference-free benchmark with 800 queries, inlined retrieved chunks, and answers from multiple RAG systems; runs locally without API keys.
AI Agent Papers2026
Proposes treating game development as a recursive data engine and introduces RLHEV (Reinforcement Learning with Human-Engine Verification) to combine dense engine checks (collision, physics, navigability) with human acceptance feedback, producing trajectory data and rewards for post-training world models.
AI Model2026
GGUF-quantized build of Qwen3.8-Flash-Next for image-text-to-text inference and local deployment. Ships with Unsloth Dynamic 3.0 quantization, thinking-mode controls (preserve_thinking, reasoning_effort), and native long-context support (262k, extensible to 1M with YaRN).