… Evaluating this fine-tuned model on six safety benchmarks , we find that it is significantly safer than the base model and control model. This behavioral shift persists even when restricting the analysis to responses lacking explicit verbalization of evaluation awareness. …
… The following papers were recommended by the Semantic Scholar API Dynin-Omni: Omnimodal Unified Large Diffusion Language Model 2026 LegoDiffusion: Micro-Serving Text-to-Image Diffusion Workflows 2026 Diffusion Model as a Generalist Segmentation Learner 2026 StructDiff: A Structure-Preserving and Sp… …
… The following papers were recommended by the Semantic Scholar API ActCam: Zero-Shot Joint Camera and 3D Motion Control for Video Generation 2026 CRePE: Curved Ray Expectation Positional Encoding for Unified-Camera-Controlled Video Generation 2026 Rays as Pixels: Learning A Joint Distribution of Vid… …
… View arXiv page View PDF GitHub 4 Add to collection Community 📄 Paper: https://arxiv.org/abs/2605.06638 the thing that sticks with me is the reported power law T ∝ D^γ and the fact that γ grows as you move from simple implication to richer first-order logic. that hints expressiveness, not just hori… …
… To address these structural bottlenecks, we propose shifting the focus to streaming video generation. We identify that its chunk-level synthesis and few denoising steps are intrinsically suited for TTS, significantly lowering computational overhead while enabling fine-grained temporal control. …
… To analyze this effect, text-deletion , image-mask-rate, and format-saturation controls suggest that such gains can arise from residual textual and distributional cues rather than valid visual evidence. …
… A controlled lab study with 40 participants shows MAIC-UI reduces editing iterations 4.9 vs. 7.0 and significantly improves learnability and controllability compared to direct Text-to-HTML generation. …
… Furthermore, LenVM enables continuous control over the trade off between performance and efficiency. …
… Deployed robots encounter distribution shifts, long-tail failures, task variations, and human correction opportunities that fixed demonstration datasets cannot fully capture. …
… A core observation is that the gap between open and closed source is shifting away from raw image quality and toward data engineering, post-training, long context, multi-turn consistency, tool use, and verification closed loops. …