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README.md

@@ -1,4 +1,23 @@
 ---
+language:
+  - en
+license: mit
+pretty_name: Appointment Bench
+tags:
+  - audio
+  - benchmark
+  - speech-to-speech
+  - voice-ai
+  - multi-turn
+  - tool-use
+  - evaluation
+  - state-tracking
+  - function-calling
+task_categories:
+  - automatic-speech-recognition
+  - text-generation
+size_categories:
+  - n<1K
 configs:
   - config_name: default
     data_files:
@@ -6,6 +25,128 @@ configs:
         path: metadata.jsonl
 ---
 
-# appointment_bench
+# Appointment Bench
 
-25-turn dual-appointment benchmark: two patients (Daniel/Danielle Nolan), two doctors (Perry/Barry), phone number swap+revert, 3 false memory traps, slot-taken error recovery, cross-entity state tracking
+**25-turn multi-turn speech-to-speech benchmark** for evaluating voice AI models as a dental office receptionist handling appointment scheduling.
+
+Part of [Audio Arena](https://audioarena.ai), a suite of 6 benchmarks spanning 221 turns across different domains. Built by [Arcada Labs](https://arcada.dev).
+
+[Leaderboard](https://audioarena.ai/leaderboard) | [GitHub](https://github.com/Design-Arena/audio-arena) | [All Benchmarks](#part-of-audio-arena)
+
+## Dataset Description
+
+The model acts as a dental office receptionist scheduling appointments for two patients with confusable names (Daniel and Danielle Nolan) across two doctors (Dr. Perry and Dr. Barry). The conversation includes phone number swaps and reverts, slot-taken error recovery, and cross-entity state tracking where information from one patient's booking must not leak into another's.
+
+## What This Benchmark Tests
+
+- **Tool use**: 4 functions — appointment booking, availability lookup, patient record retrieval, schedule management
+- **Confusable entity disambiguation**: Two patients with near-identical names (Daniel/Danielle Nolan), two doctors (Perry/Barry)
+- **Phone number swap and revert**: Correction followed by reverting back to the original
+- **False memory traps**: 4 turns that assert things the model never said or did
+- **Slot-taken error recovery**: Handling booking conflicts when a requested slot is already occupied
+- **Cross-entity state tracking**: Keeping two patients' details separate across the full conversation
+
+## Dataset Structure
+
+```
+appointment-bench/
+├── audio/                          # TTS-generated audio (1 WAV per turn)
+│   ├── turn_000.wav
+│   ├── turn_001.wav
+│   └── ... (25 files)
+├── real_audio/                     # Human-recorded audio
+│   ├── person1/
+│   │   └── turn_000.wav ... turn_024.wav
+│   └── person2/
+│       └── turn_000.wav ... turn_024.wav
+├── benchmark/
+│   ├── turns.json                  # Turn definitions with golden answers
+│   ├── hard_turns.json             # Same as turns.json but input_text=null (audio-only)
+│   ├── tool_schemas.json           # Tool/function schemas (4 tools)
+│   └── knowledge_base.txt          # Dental office KB
+└── metadata.jsonl                  # HF dataset viewer metadata
+```
+
+### Metadata Fields
+
+| Field | Description |
+|-------|-------------|
+| `file_name` | Path to the audio file |
+| `turn_id` | Turn index (0–24) |
+| `speaker` | `tts`, `person1`, or `person2` |
+| `input_text` | What the user says (text transcript) |
+| `golden_text` | Expected assistant response |
+| `required_function_call` | Tool call the model should make (JSON, nullable) |
+| `function_call_response` | Scripted tool response (JSON, nullable) |
+| `categories` | Evaluation categories for this turn |
+| `subcategory` | Specific sub-skill being tested |
+| `scoring_dimensions` | Which judge dimensions apply |
+
+## Audio Format
+
+- **Format**: WAV, 16-bit PCM, mono
+- **TTS audio**: Generated via text-to-speech
+- **Real audio**: Human-recorded by multiple speakers, same transcript content
+
+## Usage
+
+### With Audio Arena CLI
+
+```bash
+pip install audio-arena  # or: git clone + uv sync
+
+# Run with a text model
+uv run audio-arena run appointment_bench --model claude-sonnet-4-5 --service anthropic
+
+# Run with a speech-to-speech model
+uv run audio-arena run appointment_bench --model gpt-realtime --service openai-realtime
+
+# Judge the results
+uv run audio-arena judge runs/appointment_bench/<run_dir>
+```
+
+### With Hugging Face Datasets
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("arcada-labs/appointment-bench")
+```
+
+## Evaluation
+
+Models are judged on up to 5 dimensions per turn:
+
+| Dimension | Description |
+|-----------|-------------|
+| `tool_use_correct` | Correct function called with correct arguments |
+| `instruction_following` | User's request was actually completed |
+| `kb_grounding` | Claims are supported by the knowledge base or tool results |
+| `state_tracking` | Consistency with earlier turns (scored on tagged turns only) |
+| `ambiguity_handling` | Correct disambiguation (scored on tagged turns only) |
+
+For speech-to-speech models, a 6th `turn_taking` dimension evaluates audio timing correctness.
+
+See the [full methodology](https://github.com/Design-Arena/audio-arena#methodology) for details on two-phase evaluation, penalty absorption, and category-aware scoring.
+
+## Part of Audio Arena
+
+| Benchmark | Turns | Scenario |
+|-----------|-------|----------|
+| [Conversation Bench](https://huggingface.co/datasets/arcada-labs/conversation-bench) | 75 | Conference assistant |
+| **Appointment Bench** (this dataset) | 25 | Dental office scheduling |
+| [Assistant Bench](https://huggingface.co/datasets/arcada-labs/assistant-bench) | 31 | Personal assistant |
+| [Event Bench](https://huggingface.co/datasets/arcada-labs/event-bench) | 29 | Event planning |
+| [Grocery Bench](https://huggingface.co/datasets/arcada-labs/grocery-bench) | 30 | Grocery ordering |
+| [Product Bench](https://huggingface.co/datasets/arcada-labs/product-bench) | 31 | Laptop comparison shopping |
+
+## Citation
+
+```bibtex
+@misc{audioarena2026,
+  title={Audio Arena: Multi-Turn Speech-to-Speech Evaluation Benchmarks},
+  author={Arcada Labs},
+  year={2026},
+  url={https://audioarena.ai}
+}
+```