Given a line-art sketch and user-specified color intent, controllable colorization must assign intended colors to fine-grained regions while preserving the underlying line structure. Existing methods have improved visual quality, but precise local adjustment remains difficult. In this work, we argue that the key limitation is the absence of a persistent correction state. Such a state records user intent, localized mismatches, grounded edit regions, and preservation constraints to guide targeted repairs. To address this, we introduce PaletteAgent, an agentic, self-evolving correction framework for controllable line-art colorization. PaletteAgent translates user intent into structured color prompts, employs judgment agents to diagnose mismatches, grounds problematic regions, preserves correct fields, and adaptively decides whether to refine globally, repair locally, or stop. This iterative process naturally defines the GRPO training objective for optimizing the agent’s correction policy. Empirical results show that PaletteAgent outperforms line-art colorization and general image-editing baselines. Further ablations confirm the role of maintaining a persistent correction state for controllable colorization.

The rapid escalation from elementary school-level to frontier problems of the difficulty for LLM benchmarks in recent years seems to bring us close enough to the “last exam” for LLMs to surpass humanity. However, is the LLMs’ remarkable reasoning ability indeed coming from true intelligence by human standards, or are they actually reciting solutions witnessed during training at an Internet level? To study this problem, we propose RoR-Bench, a novel, multi-modal benchmark for detecting LLM’s recitation behavior when asked simple reasoning problems but with conditions subtly shifted, and conduct empirical analysis on our benchmark. Surprisingly, we found existing cutting-edge LLMs unanimously exhibits extremely severe recitation behavior; by changing one phrase in the condition, top models such as OpenAI-o1 and DeepSeek-R1 can suffer 60 percent performance loss on elementary school-level arithmetic and reasoning problems. Such findings are a wake-up call to the LLM community that compels us to reevaluate the true intelligence level of cutting-edge LLMs.