Update README.md

README.md

@@ -23,71 +23,79 @@ pretty_name: PAVO-Bench
 size_categories:
   - 10K<n<100K
 ---
-
 # PAVO-Bench: 50K-Turn Benchmark for ASR-LLM-TTS Pipeline Routing
-
 **Authors:** NarasingaMoorthy VeiluKanthaPerumal (University of Pennsylvania); Mohammed Imthathullah (Google)
-
 > **Note on the dataset viewer:** PAVO-Bench is distributed as a set of JSON
 > result files with heterogeneous schemas (tier-level statistics, coupling
 > matrices, ablation tables, etc.), so the HF auto-viewer is disabled. Download
 > the individual files directly via `huggingface_hub` (see Usage below).
-
 ## Description
-
 PAVO-Bench is a comprehensive benchmark suite for evaluating **ASR-LLM-TTS voice pipeline routing** decisions. It provides 50,000 turns of benchmark data designed to measure how well different pipeline configurations balance **latency**, **quality**, **cost**, and **energy consumption** when routing spoken-language queries through cascaded Automatic Speech Recognition (ASR), Large Language Model (LLM), and Text-to-Speech (TTS) components.
-
 The benchmark is organized into three tiers of increasing scale and complexity, plus component-level ablation studies. All results were produced on GPU hardware.
-
 ## Dataset Files
-
 ### Tier 1 -- Unit-Level Validation
-
 | File | Description |
 |------|-------------|
 | `tier1_statistical_results.json` | Statistical reproducibility results across 5 trials of 1,000 turns each (seeds 42, 123, 456, 789, 1024). Reports mean, std, and 95% confidence intervals for PAVO latency, quality, cost, and energy metrics. |
 | `tier1_coupling_results.json` | Coupling constraint validation measuring LLM quality degradation as a function of ASR word-error rate (WER 0--20%) using llama3.1:8b. |
 | `tier1_llm_latency_results.json` | LLM latency profiling for llama3.1:8b across short (50 token), medium (200 token), and long (500 token) generation contexts. Reports total latency, time-to-first-token, and tokens/second. |
-
 ### Tier 2 -- Integration-Level Evaluation
-
 | File | Description |
 |------|-------------|
 | `tier2_e2e_results.json` | End-to-end pipeline measurements for cloud_premium (whisper-large-v3 + llama3.1:8b) and edge_fast (whisper-tiny + gemma2:2b) configurations on 200 LibriSpeech samples. Includes per-stage latency breakdowns, sample ASR outputs, and sample LLM responses. |
 | `tier2_cross_dataset_results.json` | Cross-dataset ASR evaluation on LibriSpeech and FLEURS for whisper-large-v3 and whisper-tiny models (200 samples each). Reports WER and latency statistics. |
 | `tier2_noise_robustness_results.json` | ASR robustness under white noise at SNR levels 5--30 dB, plus clean baseline. Reports WER degradation across noise conditions. |
-
 ### Tier 3 -- Scale Evaluation
-
 | File | Description |
 |------|-------------|
 | `tier3_50k_summary.json` | Summary statistics for the full 50,000-turn PAVO-Bench dataset: 40K train / 10K test split, complexity distribution (levels 1--5), generation time, and error rate. |
 | `tier3_scaling_results.json` | LLM scaling benchmarks across multiple models (gemma2:2b, llama3.1:8b, etc.) with simple, medium, and complex query types. Reports latency, throughput, and real-time suitability. |
-
 ### Component Analysis
-
 | File | Description |
 |------|-------------|
 | `component_ablation_results.json` | Ablation study comparing PAVO-Full, PAVO-NoCoupling, and other ablated configurations. Reports latency, quality, cost, energy, coupling violations, and infeasible percentages. |
 | `supervised_baseline_results.json` | Supervised vs. RL routing comparison on 100K synthetic state vectors with oracle routing labels. Reports accuracy, top-3 accuracy, cost gap vs. oracle, decision latency, and training time for Logistic Regression, Random Forest, XGBoost, MLP (cross-entropy), and MLP (PPO). |
-
 ## Usage
-
 ### Load individual JSON files directly
-
 ```python
 import json
 from huggingface_hub import hf_hub_download
-
 # Download a specific results file
 path = hf_hub_download(
     repo_id="vnmoorthy/pavo-bench",
     filename="tier3_50k_summary.json",
     repo_type="dataset",
 )
-
 with open(path) as f:
     data = json.load(f)
-
 print(f"Total samples: {data['total_samples']}")
-print(f"Train/Test split: {data['train_samples']}/{data['test_samples']}")
+print(f"Train/Test split: {data['train_samples']}/{data['test_samples']}")
+```
+### Download all files
+```python
+from huggingface_hub import snapshot_download
+snapshot_download(
+    repo_id="vnmoorthy/pavo-bench",
+    repo_type="dataset",
+    local_dir="./pavo-bench-data",
+)
+```
+## Benchmark Metrics
+- **Latency** (ms): End-to-end and per-component response time
+- **Quality** (0--1): Composite score incorporating ASR accuracy and LLM response quality
+- **Cost** (USD): Per-turn inference cost
+- **Energy** (mJ): Per-turn energy consumption
+- **Coupling violations**: Cases where ASR errors propagate and degrade LLM quality
+## Citation
+If you use PAVO-Bench in your research, please cite:
+```bibtex
+@misc{pavo-bench-2026,
+  author = {VeiluKanthaPerumal, NarasingaMoorthy and Imthathullah, Mohammed},
+  title = {PAVO-Bench: A 50K-Turn Benchmark for ASR-LLM-TTS Pipeline Routing},
+  year = {2026},
+  institution = {University of Pennsylvania},
+  url = {https://huggingface.co/datasets/vnmoorthy/pavo-bench}
+}
+```
+## License
+This dataset is released under the [Creative Commons Attribution 4.0 International (CC-BY 4.0)](https://creativecommons.org/licenses/by/4.0/) license. The accompanying code on GitHub is released separately under the MIT License.