Qwen-Image-Agent is a unified agent framework that progressively builds sufficient generation context for T2I models via Context-Aware Planning and Context Grounding, achieving SOTA on IA-Bench, Mindbench, and WISE-Verified.
Draco: Draft as cot for text-to-image preview and rare concept generation
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
GenClaw introduces a three-stage code-driven workflow for agentic image generation that inserts programmatic sketches between linguistic reasoning and pixel synthesis.
CLVR framework adds closed-loop visual verification, proxy prompt reinforcement learning, and delta-space weight merge to improve complex text-to-image generation over single-step or unverified multi-step baselines.
OmniVerifier-M1 is a generalist visual verifier using symbolic outputs for meta-verification and decoupled RL to outperform joint optimization for robust verification and agentic self-correction.
citing papers explorer
-
Qwen-Image-Agent: Bridging the Context Gap in Real-World Image Generation
Qwen-Image-Agent is a unified agent framework that progressively builds sufficient generation context for T2I models via Context-Aware Planning and Context Grounding, achieving SOTA on IA-Bench, Mindbench, and WISE-Verified.
-
GenClaw: Code-Driven Agentic Image Generation
GenClaw introduces a three-stage code-driven workflow for agentic image generation that inserts programmatic sketches between linguistic reasoning and pixel synthesis.
-
Unlocking Complex Visual Generation via Closed-Loop Verified Reasoning
CLVR framework adds closed-loop visual verification, proxy prompt reinforcement learning, and delta-space weight merge to improve complex text-to-image generation over single-step or unverified multi-step baselines.
-
OmniVerifier-M1: Multimodal Meta-Verifier with Explicit Structured Recalibration
OmniVerifier-M1 is a generalist visual verifier using symbolic outputs for meta-verification and decoupled RL to outperform joint optimization for robust verification and agentic self-correction.