Edit-Compass and EditReward-Compass are new unified benchmarks for fine-grained image editing evaluation and realistic reward modeling in reinforcement learning optimization.
Qwen3.6-27B: Flagship-level coding in a 27B dense model, April 2026
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3representative citing papers
An open-source causal video world model sustains hour-long, 720p/60fps interactive generation without visual drift, paired with a VLM-based director-pilot agentic harness for rich, open-ended interaction.
LingBot-VLA 2.0 combines 60k hours of multi-embodiment pretraining data, an expanded whole-body action space, and dual-query distillation from depth and video teachers to improve VLA performance on GM-100 and long-horizon mobile manipulation tasks.
citing papers explorer
-
Edit-Compass & EditReward-Compass: A Unified Benchmark for Image Editing and Reward Modeling
Edit-Compass and EditReward-Compass are new unified benchmarks for fine-grained image editing evaluation and realistic reward modeling in reinforcement learning optimization.
-
Infinite Worlds with Versatile Interactions
An open-source causal video world model sustains hour-long, 720p/60fps interactive generation without visual drift, paired with a VLM-based director-pilot agentic harness for rich, open-ended interaction.
-
From Foundation to Application: Improving VLA Models in Practice
LingBot-VLA 2.0 combines 60k hours of multi-embodiment pretraining data, an expanded whole-body action space, and dual-query distillation from depth and video teachers to improve VLA performance on GM-100 and long-horizon mobile manipulation tasks.