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R^3 achieves a groundbreaking balance between compliance and semantic intent preservation, outperforming existing methods in video ad rectification.
Explainable detection of hateful videos is now possible, revealing the nuanced reasoning behind classifications that traditional methods overlook.
Agentic RAG gets a 7.7 point accuracy boost thanks to Search-P1's path-centric reward shaping, which extracts learning signals even from failed reasoning attempts.
Dramatically reduce hallucination in industrial RAG systems by jointly optimizing retrieval and generation with graph-aware retrieval and reinforcement learning, leading to a 92.7% reduction in URL hallucination in a real-world advertising QA system.
Spatial relationship hallucinations in image inpainting can be significantly reduced by directly optimizing for preferences on background plausibility, even when foregrounds are identical.
