Update README.md

README.md

@@ -166,15 +166,14 @@ According to the paper:
 
 PaveBench is designed to provide a unified foundation for both precise visual perception and interactive multimodal reasoning in the pavement domain.  -->
 
-## Benchmark Tasks
 
-PaveBench supports four core tasks:
-1. Classification
-2. Object Detection
-3. Semantic Segmentation
-4. Vision-Language Question Answering
+## Benchmark and Experiments
 
-It also includes an agent-augmented evaluation setting where vision-language models are combined with domain-specific tools for more reliable quantitative analysis. 
+1. **Visual Perception Evaluation.** PaveBench provides a unified benchmark for classification, detection, and segmentation, where modern models achieve strong yet non-saturated performance, showing that the dataset offers reliable supervision while remaining challenging in realistic scenes with hard distractors; for detection and segmentation, Longitudinal Crack and Transverse Crack are merged into **Linear Crack**, because their distinction mainly lies in global direction, whereas these two tasks focus on accurately localizing crack instances and extracting crack regions.
+
+2. **Multimodal VQA Evaluation.** Multimodal VQA results show that general-purpose VLMs perform poorly in the zero-shot setting, while LoRA fine-tuning substantially improves their ability to produce more accurate, coherent, and task-aligned responses for pavement distress analysis.
+
+3. **Agent-Augmented VQA Framework.** The agent-augmented framework further improves the reliability of interactive pavement analysis by routing user queries to specialized visual tools, reducing numerical hallucinations, and producing more visually grounded and interpretable responses without additional parameter updates.
 
 ## Citation
 If you use this dataset in your work, please cite it as: