DSPO: Diversity-aware Subjective Policy Optimization for Robust Emotional Reasoning

arXiv:2609.36775 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• Hallucination — 22× in this paper · explained in 3 episodes
• GRPO — 18× in this paper · explained in 6 episodes
• Accuracy — 15× in this paper · explained in 1 episode
• Reinforcement Learning — 10× in this paper · explained in 4 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• Counterfactual Visual Intervention Gating — 7× in this paper
• Diversity-Aware Subjective Policy Optimization — 6× in this paper
• Distribution-Aligned Emotional Diversity Reward — 5× in this paper
• Instruction Tuning — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Generalization — 3× in this paper · explained in 13 episodes
• Grounding — 3× in this paper · explained in 3 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Actor — 2× in this paper · explained in 3 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Group Relative Policy Optimization — 2× in this paper
• Hallucination Mitigation — 2× in this paper · explained in 2 episodes
• Mode Collapse — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Affective Computing — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Chain-of-Thought (CoT) — 1× in this paper · explained in 4 episodes
• Distribution Matching — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Group Relative Policy Optimization (GRPO) — 1× in this paper · explained in 5 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Multimodal Inputs — 1× in this paper · explained in 2 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Semantic Gap — 1× in this paper · explained in 2 episodes
• Structured Reasoning — 1× in this paper · explained in 3 episodes
• Thinking mode — 1× in this paper · explained in 2 episodes
• Two-stage training — 1× in this paper · explained in 2 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Visual Grounding — 1× in this paper · explained in 2 episodes

Sources

• Qwen2.5-VL Technical Report
• CreatiParser: Generative Image Parsing of Raster Graphic Designs into Editable Layers
• FACE-net: Factual Calibration and Emotion Augmentation for Retrieval-enhanced Emotional Video Captioning
• OmniOPSD: Rationale-Privileged On-Policy Self-Distillation for Affective Computing
• EmoVerse: A MLLMs-Driven Emotion Representation Dataset for Interpretable Visual Emotion Analysis
• EmoStyle: Affective Conditioning of Style-Specialist Experts for Emotional Image Generation
• Auto-Encoding Variational Bayes
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• Evaluation and Analysis of Hallucination in Large Vision-Language Models
• A Multi-Agent Framework with Structured Reasoning and Reflective Refinement for Multimodal Empathetic Response Generation
• MiMo-V2-Flash Technical Report
• Why Attention Patterns Exist: A Unifying Temporal Perspective Analysis
• MME-Emotion: A Holistic Evaluation Benchmark for Emotional Intelligence in Multimodal Large Language Models
• R1-Omni: Explainable Omni-Multimodal Emotion Recognition with Reinforcement Learning

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