New text-generation model. Tags: qwen3_5, image-text-to-text, 27b, early-access, draft, abliterated, abliterix, aeon
New text-generation model. Tags: gguf, llama.cpp, quantized, empero-ai, qwen3.5, qwen3.8, distillation, reasoning
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Ver preçosNew text-generation model. Tags: qwen3_5, image-text-to-text, empero-ai, qwen3.5, qwen3.8, distillation, reasoning, function-calling
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New image-text-to-text model. Tags: gguf, llama.cpp, quantized, qwen3.8, qwen3.5, ridge, gated-deltanet, imatrix
New image-text-to-text model. Tags: mlx, mlx-vlm, qwen, qwen3.8, multimodal, 2-bit, 4-bit, 6-bit
New image-text-to-text model. Tags: qwen3_5, image-text-to-text, abliterated, qwen3.8, uncensored, ai-red-team, red-teaming, fp8
New image-text-to-text model. Tags: qwen3_5, image-text-to-text, unsloth, GAIN Training, COLD-FUSION, finetune, conversational, base_model:Qwen/Qwen3.8-27B
New image-text-to-text model. Tags: gguf, qwen3.8, qwen, 27b, dense, abliterated, quantized, multimodal
New text-generation model. Tags: gguf, qwen3.8, qwen3.5, heretic, abliterated, uncensored, roleplay, imatrix
Determining the biological sex of the individuals who created Upper Paleolithic hand stencils remains a challenging problem due to the absence of ground truth, population differences between contemporary and prehistoric groups, and the uncertainty introduced by image degradation. Traditional morphometric methods suffer from high structural overlap across sexes, poor cross-population generalizability, and subjective feature engineering. This study presents an uncertainty-aware deep learning framework for sex attribution in prehistoric hand stencils that explicitly models, propagates, and aggregates uncertainty throughout the analytical pipeline. The methodology combines dual image processing, dual contour extraction, structured silhouette augmentation, model architectural diversity, and ensemble-based decision aggregation. The pipeline generates twelve plausible silhouette realizations per stencil to capture boundary uncertainties, which are processed by two ensembles of ten deep neural networks each (EfficientNet-B3 and MobileViT-S) trained on 14,036 contemporary hand samples. Furthermore, a triangulated validation scheme integrates ensemble predictions with unsupervised 2D latent-space manifold mapping (UMAP + k-NN) and explainable AI spatial attributions (LayerCAM) to ensure anatomical consistency. On contemporary data, ensemble models achieve strong classification performance, with accuracies exceeding 88% in older age groups. When applied to prehistoric stencils, the framework produces both sex predictions and confidence measures of internal agreement, enabling the distinction between morphologically stable and ambiguous cases. Convergence across ensemble predictions, latent-space structure, and interpretability analyses shows that uncertainty can become a measurable component of archaeological inference, enabling robust and reproducible decoding of ancient rock art.
Interactive game world models typically autoregress visual observations directly in pixel or latent space, forcing structured properties such as pose, geometry, and occlusion to be implicitly maintained by the same generative sequence. Over long horizons, errors in these latent world properties accumulate, making consistency and controllability fragile. We explicitly model the evolving world state, delegate exact geometric computation to a fixed, zero-parameter renderer, and leave the neural model to synthesize appearance. We instantiate this idea as Marionette, a world model for interactive games with articulated characters. First, a two-stage autoregressive dynamics model predicts an explicit and interpretable 276-dimensional 3D world state comprising multi-entity articulated skeletons, metric root trajectories, and rotations. Second, a zero-parameter graphics bridge converts the predicted state into pose-control videos, computing world-space geometry and occlusion in closed form. Third, a control-conditioned video-diffusion observation model synthesizes photorealistic RGB observations from the resulting structured controls. Our experiments establish two properties of Marionette. First, the predicted world state is directly controllable. Forcing a mismatched action stream changes root-aligned joint error by 31% across 48 held-out segments. Second, long-horizon behaviour is determined in the state, and can be repaired there. Left free, the two generated characters drift to 21.2 m apart (recorded sessions stay near 5 m) and a third of frames show ground penetration. Two rules imposed on the explicit state, a terrain collider and a separation cap, cut penetration by 66% and keep the pair engaged, with no change to the observation model. Routing appearance through the predicted state costs no fidelity we can detect, at an FVD of 831 against 799 for recorded pose.
