LLM News

Researchers introduce SOLACE, a post-training framework that replaces external reward models with an internal self-confidence signal derived from how accurately a text-to-image model recovers injected noise. The method enables fully unsupervised optimization and shows measurable improvements in compositional generation, text rendering, and text-image alignment.

Researchers introduce Conditioned Activation Transport (CAT), a technique that reduces unsafe content generation in text-to-image models during inference without the quality degradation seen in previous linear steering approaches. The method uses a contrastive dataset of 2,300 safe/unsafe prompt pairs and geometry-based conditioning to target only unsafe activation regions.