Discover the Best AI Resources
Curated essentials, no noise — just what matters
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.
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.
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.
Helps produce and revise natural, readable Japanese for work documents by applying a 12-item writing constitution and sudachipy-powered lint to detect and remove “AI-smelling” patterns; includes diagnostic scoring and iterative rewrite loops.
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.
A GGUF-distributed Qwen3.6 35B MoE model variant repaired with a
GGUF conversions of Laguna S 2.1 for llama.cpp, including quantized builds (Q4_K_M, Q8_0, F16) and a small DFlash drafter for speculative decoding; configured for a 256K default context window and intended for local inference and serving with Poolside's llama.cpp fork.
A GGUF build of Qwen3.6 (35B) post-processed with the Genesis numerical repair to reduce training noise and restore weight distributions; provides a more stable, uncensored multimodal (image+text) MoE model with long-context support for local use.
27B multimodal reasoning model built on Qwen3.5-27B that preserves the base model's native multi-token-prediction head, full vision tower, and a 1,048,576-token YaRN context window. Designed for agentic tool use, long-context reasoning, and research deployments; released under Apache-2.0.
Provides 1,800+ hours of synchronized egocentric multi-view recordings with 3D hand reconstructions, wide‑FOV depth, and hierarchical task/subtask annotations for embodied AI and robot learning. Includes six fisheye views, hand meshes, and per-episode temporal labels across 44k+ episodes.
End-to-end 0.8B multimodal OCR and page-level document parser that converts page images into structured Markdown (text, LaTeX formulas, HTML tables, and image crops). Post-trained from Qwen3.5-0.8B using mixed real/synthetic data and SFT+RL+OPD; achieves 96.58 on OmniDocBench v1.6.
Uses pretrained multimodal LLMs as zero-shot, training-free reward models for text-to-image RL by scoring how well the original text prompt can be recovered from a generated image via image-conditioned prompt log-likelihood; includes a Self-SpectraReward closed-loop variant.