AI Model2026
Provides an abliterated (refusal-removed) build of Qwen3.8-27B for offline research and red‑teaming, keeping multimodal vision, an MTP speculative head, and a 262,144-token context. It has no built-in safety guardrails and is released under Apache‑2.0 for research use only.
Uses cooperative multi-agent RL where multiple decoupled models provide peer-derived pseudo-rewards to each other, enabling unsupervised improvements in reasoning; increases cohort diversity to reduce correlated errors and avoid training collapse, showing consistent gains across text and multimodal benchmarks.
Embodied AI2026
Turns embodied navigation into 2D visual prompting where a vision-language model selects image pixels that are projected to 3D actions; adds selective chain-of-thought, compressed anchor-trajectory memory, and a two-level alignment objective to improve sample and runtime efficiency.
AI Video Papers2026
Introduces SemComp-Bench: a benchmark and VLM-based evaluation protocol for measuring outcome achievement and task-relevant semantic grounding in instruction-driven video generation. Ships with SemComp-Data, curated image–instruction–outcome triplets and OA/GR scoring.
AI Dataset2026
Converts 200+ hours of expert Figma screen recordings into 3,469 Playwright-MCP action trajectories for training and evaluating vision-language and GUI agents; includes 126 long‑horizon tasks, phase labels, a 10‑skill taxonomy, and is CC‑BY‑4.0 licensed.
AI Video Papers2026
Yunheng Li, Guohong Mu +5VCIP, School of Computer Science, Nankai University, Brain and Artificial Intelligence Lab, Northwestern Polytechnical University +2
Treats human annotations as oracle rollouts and separates them from on-policy baselines to improve reinforcement learning for video multimodal LLMs. Key features include a decoupled advantage estimator, sign-balanced pruning, and scalable gains across model sizes and data budgets.
AI Video Papers2026
Evaluates visual reasoning in video generation models using 27 photorealistic tasks (810 instances), a two-level taxonomy of domains and skill tags, and task designs that enforce valid intermediate trajectories and calibrated difficulty.
Generates group images that bind up to ten reference identities to distinct people and locations by predicting an explicit identity–layout plan and supervising faces with Layout-Grounded ID Loss. Improves identity fidelity while cutting copy-paste duplication; suited for multi-person image synthesis but requires identity-annotated face regions and paired training data.
AI Dataset2026
Provides 1,021.64 hours across 597 CAD/BIM workflows with synchronized screen recordings and interaction logs; each workflow includes video, timestamped input events, task specs, source files, final outputs, and evaluation rubrics for training or evaluating desktop CAD agents.
AI Video Papers2026
Continues a live or ongoing video stream while applying user-specified edits on the fly using a lightweight edit-ignition adapter. The adapter injects edits only in chunks where requests arrive and uses history cross-attention and temporal causal self-attention to preserve continuity and stability for unbounded streaming edits.
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.