Question-Specific Knowledge Graphs for Efficient Visual Reasoning
cs.CL
Submitted: 2026-09-28
Updated: 2026-09-28
Terminology
Sources
- Qwen2.5-VL Technical Report
- Prune Redundancy, Preserve Essence: Vision Token Compression in VLMs via Synergistic Importance-Diversity
- MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
- Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models
- VIDEOP2R: Video Understanding from Perception to Reasoning
- GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization
- Efficient Whole Slide Pathology VQA via Token Compression
- VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model
- Interactive Post-Training for Vision-Language-Action Models
- Scaling Behaviors of LLM Reinforcement Learning Post-Training: An Empirical Study in Mathematical Reasoning
- Learning from Mistakes: Negative Reasoning Samples Enhance Out-of-Domain Generalization
- The information bottleneck method
- Visionary-R1: Mitigating Shortcuts in Visual Reasoning with Reinforcement Learning
- Qwen3 Technical Report
- Perception-R1: Pioneering Perception Policy with Reinforcement Learning
- Reassessing the Role of Supervised Fine-Tuning: An Empirical Study in VLM Reasoning
- WikiSeeker: Rethinking the Role of Vision-Language Models in Knowledge-Based Visual Question Answering
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