VS-CoT achieves the highest Top@1 accuracy (0.348) and Pass@N accuracy (0.485) on SimpleQA among all methods tested

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• VS-CoT with GPT-4.1 as generator achieves 45.9% accuracy on Qwen3-1.7B-Base fine-tuned on math benchmarks, the highest single resultfinding0.846
  Best performing VS variant for math synthetic data generation with GPT-4.1
• VS-CoT negative synthetic data achieves 36.81% accuracy on GSM8K test set after offline RL, vs. 34.12% with positive-only trainingfinding0.821
  Demonstrates VS-generated diverse negative examples improve downstream model performance in offline RL
• 90.45% accuracy on ScienceQA benchmark with Multimodal-CoT Large (738M parameters)finding0.791
  State-of-the-art result on ScienceQA; represents +3.91% improvement over prior best published result of 86.54%.
• One-stage CoT (QCM→RA) shows 12.31% accuracy drop vs. no-CoT (QCM→A) on ScienceQA; two-stage framework (rationale generation + answer inference) achieves 85.31% accuracy with vision featuresfinding0.789
  Empirical evidence that naive one-stage CoT fails in language-only setting; two-stage + vision achieves state-of-the-art.
• Multimodal-CoT trained with InstructBLIP/ChatGPT-generated rationales achieves 87.76% accuracy on ScienceQA, comparable to human-annotated rationale performance of 90.45%finding0.784
  Evidence that Multimodal-CoT can operate without human-annotated reasoning chains by using large models to generate pseudo-rationales.
• VS-Multi achieves average accuracy of 37.5% on math benchmarks (avg of MATH500, OlympiadBench, Minerva) with Gemini-2.5-Flash as generator and Qwen3-4B as SFT model, vs. 40.7% baselinefinding0.777
  Best VS result in synthetic data generation for math, demonstrating downstream improvement through diversity
• VS-Multi achieves Coverage-N of 0.71 on Open-Ended QA vs. 0.10 for Direct, while maintaining precision of 0.96finding0.774
  Demonstrates VS generates a broader range of valid answers without sacrificing accuracy
• VS-CoT and VS-Multi push the Pareto front of the diversity-quality trade-off across creative writing tasksclaim0.756
  Empirical finding from creative writing experiments showing VS variants achieve higher diversity without sacrificing quality