Creates an open-ended interactive world simulator with an unbounded interaction horizon via causal pretraining, a distilled real-time runtime that drives 720p@60fps, a wider action/event repertoire, and a pilot–director agent split for behavior planning and environment synthesis.
AI Agent Papers2026
Provides a terminal-style benchmark of 46 long-horizon tasks decomposed into fine-grained graded subtasks to produce dense intermediate rewards and partial credit, enabling evaluation of long-horizon planning, long-context management, and iterative debugging. Tasks typically require hundreds of episodes and minutes-to-hours of execution; baseline evaluations report high token and episode consumption with low pass rates, highlighting evaluation headroom.
AI Agent Papers2026
Evaluates proactive, multimodal agents on 400 bilingual real‑world tasks across five capability axes (Skill Usage, Exploration, Long‑Context Reasoning, Multimodal Understanding, Cross‑Platform Coordination) using live Docker‑based, stepwise closed‑loop evaluation to separate base model skills from framework design.
AI Model2026
Provides GGUF-quantized builds of the Qwythos-9B-v2 LLM for local runtimes, with multiple quant levels, optional MTP-enabled variants, a 1,048,576-token context window, and an optional BF16 vision projector for multimodal use.
AI Model2026
A 9B-parameter Qwen3.5-based multimodal model tuned to preserve chain-of-thought reasoning while eliminating repetition loops; restores native multi-token prediction, supports 1,048,576-token context, and targets research/red-team use.
AI Video Papers2026
Uses large-scale text-to-video generative pretraining to create GenCeption, a feed-forward perception model that performs diverse vision tasks from text instructions—depth, surface normals, camera pose, referring segmentation, and 3D keypoints—often matching or surpassing specialized models while requiring far less task-specific data.
Explores unsupervised visual pretraining on visually rich documents to improve language-model intelligence; shows visual-pretrained models outperform text-only counterparts on the same corpora. Key aspects: direct use of images/layouts (no OCR-only pipeline), scalable across backbones and benchmarks.
AI Video2026
Generates minute-scale, temporally coherent dance videos from full music tracks using a hierarchical two-stage approach: global keyframe planning plus local temporal refinement; suitable when long-range musical structure and rhythmic continuity matter.
AI Video2026
Generates a new camera viewpoint from a reference video: an IC‑LoRA adapter for LTX‑Video 2.3 that re‑renders the same scene from a requested discrete camera angle while preserving subject and content. Trained on synthetic multi‑view data, proof‑of‑concept with limited viewpoint range and best for small, chained angle shifts.
AI Agent Papers2026
Provides a deliberative Agent OS layer for robots that handles scene-conditioned planning, context-isolated skill execution, multi-stage verification, persistent multi-modal graph memory, and edge–cloud collaboration. Introduces EmbodiedWorldBench (16 scenes, 200+ tasks) and a failure-driven self-evolution loop; shows improved task success and strong memory benchmark scores.
AI Agent Papers2026
Unifies high-level visual-language reasoning and low-level control for visual navigation by decoupling cognition and control: a slow vision-language reasoner produces pixel goals with explicit chain-of-thought, and a fast action expert converts those anchors into continuous waypoints for robust urban and indoor navigation.
AI Model2026
A GGUF-format Qwen3.6 35B base model image-text-to-text release repaired via tensor-level SVD/scale correction and packaged with Hermes agent tweaks; multimodal (vision + text), MoE architecture, ready for GGUF runtimes like llama.cpp.