Does Gradient Conflict Predict the Understanding--Generation Trade-off? A Controlled Audit of Conflict-Metric Validity in Unified Multimodal Models

arXiv:2609.38465 · cs.LG, cs.AI, cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.LG, cs.AI, cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• Accuracy — 15× in this paper · explained in 1 episode
• Benchmark — 14× in this paper · explained in 2 episodes
• Consistency — 12× in this paper · explained in 3 episodes
• Alignment — 9× in this paper · explained in 1 episode
• Transformer — 9× in this paper · explained in 2 episodes
• Multimodal Learning — 5× in this paper · explained in 2 episodes
• Self-consistency — 5× in this paper · explained in 2 episodes
• Foundation Model — 4× in this paper · explained in 3 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Core — 3× in this paper · explained in 2 episodes
• Decoupling — 3× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 3× in this paper
• Instruction Tuning — 3× in this paper · explained in 2 episodes
• Multi-objective Optimization — 3× in this paper · explained in 4 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Foundation Models — 2× in this paper · explained in 7 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Adaptive Weighting — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Catastrophic Forgetting — 1× in this paper · explained in 5 episodes
• Continual Learning — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• International Conference on Machine Learning — 1× in this paper
• Linear probe — 1× in this paper · explained in 2 episodes
• LoRA Fine-Tuning — 1× in this paper · explained in 3 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Probing Classifiers: Promises, Shortcomings, and Advances
• Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling
• Unified Autoregressive Visual Generation and Understanding with Continuous Tokens
• UniEval: Unified Holistic Evaluation for Unified Multimodal Understanding and Generation
• Conflict-Averse Gradient Descent for Multi-task Learning
• Second-Order Multi-Level Variance Correction for Modality Competition in Multimodal Models
• A Metric Learning Reality Check
• Multi-Task Learning as a Bargaining Game
• Do Understanding and Generation Fight? A Diagnostic Study of DPO for Unified Multimodal Models
• Do ImageNet Classifiers Generalize to ImageNet?
• Independent Component Alignment for Multi-Task Learning
• UGen: Unified Autoregressive Multimodal Model with Progressive Vocabulary Learning
• Chameleon: Mixed-Modal Early-Fusion Foundation Models
• HunyuanImage 3.0 Technical Report
• What Makes Training Multi-Modal Classification Networks Hard?
• Emu3: Next-Token Prediction is All You Need
• Mitigating Intra- and Inter-modal Forgetting in Continual Learning of Unified Multimodal Models
• Pareto LoRA: Mitigating Modality Imbalance in Unified Multimodal Models via Pareto-Optimal Gradient Integration
• On-the-fly Modulation for Balanced Multimodal Learning

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