Update README.md

README.md

@@ -19,14 +19,14 @@ Calibration uses natural top-k routing rather than forcing all experts to activa
 
 ### Calibration dataset
 
-Three calibration passes were run:
-
-1. **Coding pass** — Agentic coding samples (tool calling, multi-turn code generation, function calling) with English and Chinese system prompts.
-2. **Broad pass** — Large-scale diverse samples drawn from WildChat-NonToxic and LMSYS-Chat covering real user conversations across a wide range of topics and languages.
-3. **Deep pass** — Long-context samples (>8K tokens) from coding and diverse sources to exercise deep-sequence expert activation patterns.
-4. **Image pass** — Image question-answering prompts, with the input images drawn from a large collection of public, high quality image datasets.
-5. **Audio pass** — Medium-size dataset of mostly speech.
-6. **Video pass** — Diverse set of video question-answering prompts, with a wide variety of input videos of different durations and resolutions.
+Six calibration passes were run:
+
+1. **Coding** — Agentic coding samples (tool calling, multi-turn code generation, function calling) with English and Chinese system prompts.
+2. **Broad** — Large-scale diverse samples drawn from WildChat-NonToxic and LMSYS-Chat covering real user conversations across a wide range of topics and languages.
+3. **Deep** — Long-context samples (>8K tokens) from coding and diverse sources to exercise deep-sequence expert activation patterns.
+4. **Image** — Image question-answering prompts, with the input images drawn from a large collection of public, high quality image datasets.
+5. **Audio** — Medium-size dataset of mostly speech.
+6. **Video** — Diverse set of video question-answering prompts, with a wide variety of input videos of different durations and resolutions.
 
 ### Requirements