{"slug": "what-to-edit-next-visually-aligned-image-editing-follow-up-suggestions-in", "title": "What to Edit Next: Visually Aligned Image-Editing Follow-Up Suggestions in Conversational Systems", "summary": "Researchers from Alibaba's Qwen team developed a three-stage framework for multimodal follow-up edit suggestions in image-creation conversations, trained on 100,000 real samples from Qwen App. In a live A/B test with millions of users, the framework reduced visual inconsistency from 3.7% to 0.9%, improved recommendation CTR by 32.70%, image take-away rate by 16.32%, and average conversation turns per user by 39.90% (all p<0.05).", "body_md": "arXiv:2608.07565v1 Announce Type: new\nAbstract: Conversational assistants increasingly recommend follow-up edits to help users continue a task. Existing systems primarily target text-only interactions, leaving image-creation conversations underexplored. In image-creation tasks, useful follow-up edit suggestions must reflect user preferences, offer diverse directions, and remain executable on the current image. We collected 100,000 real multi-turn image-creation conversation samples from Qwen App and found that 80.1% are image-dependent, underscoring the need for multimodal recommendation. We address this setting with a three-stage framework. In Stage 1, we use real online data to build a human-reviewed table of appropriate follow-up editing intents, then create SFT targets and fine-tune a multimodal policy. In Stage 2, to align rule-guided SFT suggestions with actual user choices, we use user click feedback to optimize the policy through multi-objective reinforcement learning. In Stage 3, to reduce visual inconsistencies between suggested edits and the current image, we introduce a visual verifier as additional training supervision. Extensive experiments demonstrate that our framework significantly outperforms baselines on both automatic and human evaluations. In a live user-randomized A/B test with millions of users, our final framework reduces visual inconsistency from 3.7% to 0.9%. Furthermore, it significantly improves recommendation CTR by 32.70%, image take-away rate by 16.32%, and average conversation turns per user by 39.90% (all p<0.05).", "url": "https://wpnews.pro/news/what-to-edit-next-visually-aligned-image-editing-follow-up-suggestions-in", "canonical_source": "https://arxiv.org/abs/2608.07565", "published_at": "2026-08-11 04:00:00+00:00", "updated_at": "2026-08-11 04:24:20.272412+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "generative-ai"], "entities": ["Qwen App", "Alibaba"], "alternates": {"html": "https://wpnews.pro/news/what-to-edit-next-visually-aligned-image-editing-follow-up-suggestions-in", "markdown": "https://wpnews.pro/news/what-to-edit-next-visually-aligned-image-editing-follow-up-suggestions-in.md", "text": "https://wpnews.pro/news/what-to-edit-next-visually-aligned-image-editing-follow-up-suggestions-in.txt", "jsonld": "https://wpnews.pro/news/what-to-edit-next-visually-aligned-image-editing-follow-up-suggestions-in.jsonld"}}