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README.md

@@ -71,7 +71,7 @@ We introduce **HPD-Parsing**, a lightweight (1B) and high-throughput document pa
 **🔄 Staged Adaptation with Automated Difficulty-Aware Data Curation**: We develop a staged adaptation strategy that transfers conventional autoregressive document parsing capabilities to the proposed hierarchical parallel decoding paradigm while preserving parsing accuracy. The strategy is supported by an automated difficulty-aware data curation pipeline that integrates large-scale data collection, model-assisted annotation, difficulty estimation, and balanced sampling. By progressively adapting the model and emphasizing challenging samples, the training framework mitigates the accuracy degradation caused by the transition to parallel decoding with minimal manual annotation effort.
 
 
-**⚡ State-of-the-Art Throughput with Competitive Parsing Accuracy**: HPD-Parsing achieves state-of-the-art inference efficiency on OmniDocBench v1.6, reaching a peak throughput of 4,752 Tokens Per Second (TPS). It delivers 1.62x the throughput of the fastest existing document parsing model and more than 3.06x that of its autoregressive baseline, while maintaining competitive parsing accuracy. These results demonstrate that document parsing can be effectively executed through global layout coordination and localized parallel decoding rather than a single sequential generation trajectory.
+**⚡ State-of-the-Art Throughput with Competitive Parsing Accuracy**: HPD-Parsing achieves state-of-the-art inference efficiency on OmniDocBench v1.6, reaching a peak throughput of 4,752 Tokens Per Second (TPS). It delivers $1.62\times$ the throughput of the fastest existing document parsing model and more than $3.06\times$ that of its autoregressive baseline, while maintaining competitive parsing accuracy. These results demonstrate that document parsing can be effectively executed through global layout coordination and localized parallel decoding rather than a single sequential generation trajectory.
 
 
 ### **HPD-Parsing Architecture**
@@ -83,9 +83,7 @@ HPD-Parsing adopts **InternVL3.5-1B** as its backbone, applies dynamic tile-base
 </div>
 
 
-
-
-## Usage
+## Inference with vLLM
 
 HPD-Parsing runs on a **customized build of vLLM** (based on vLLM v0.17.1) that implements the dynamic request forking required by hierarchical parallel decoding and adapts P-MTP speculative decoding.
 
@@ -206,6 +204,26 @@ Key arguments:
 
 > Reference implementation only. Use the vLLM path for production throughput — its paged KV cache enables true concurrent branches with zero-copy prefix sharing.
 
+## Performance
+
+#### 1. OmniDocBench v1.6 — Accuracy
+
+With only 1B parameters, HPD-Parsing establishes a new state of the art among end-to-end **unified** parsers. Best results in each column are in **bold**.
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/weishu20/HPD-Parsing-images/master/images/Accuracy.png" width="800"/>
+</div>
+
+
+
+#### 2. OmniDocBench v1.6 — Efficiency
+Throughput under batch size 512 on NVIDIA A800 80GB with vLLM, HPD-Parsing increases throughput from 1.02 to 2.68 PPS and from 1,554.8 to 4,752.1 TPS, corresponding to improvements $2.62\times$ and $3.06\times$, respectively. Despite processing approximately 4,800 input tokens per page, over four times that of DeepSeek-OCR-2, HPD-Parsing still achieves $1.31\times$ higher PPS and $1.62\times$ higher TPS, demonstrating strong inference efficiency under a considerably larger input-token budget.Its acceleration advantage grows with document length, reaching up to $18.04\times$ fewer decoding steps, $3.67\times$ higher request throughput, and $5.80\times$ lower single-request latency in the longest output-length bucket.
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/weishu20/HPD-Parsing-images/master/images/Efficiency.png" width="800"/>
+</div>
+
+
 ## Evaluation & Benchmark
 
 Scripts under [`eval/`](./eval) reproduce both the throughput (TPS) and the OmniDocBench v1.6 accuracy numbers via a decoupled **infer -> convert -> evaluate** pipeline: