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README.md

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-## 🏆 Qwen3-4B Prompt Response Benchmark
-This benchmarking dataset contains 10 diverse data points where the **Qwen3-4B** model makes reasoning and cultural errors in English and Bengali.
-## 🤖 Model: Qwen3-4B
-Base model with reasoning capabilities. One of the well known open source Large Language Models.
+## 🏆 Critical Blindspot
+Dhaka (Bangladesh), Delhi (India) these are some of the most populated cities in the world. Unlike developed cities like California (USA)  their foot pathway is jammed with people. Although it’s chaotic, somehow people manage to have a workable system by human gestures,  expressions and common sense (verbal communication is inefficient in chaotic scenarios). As we are developing bipedal robots for general purpose activities, they also should understand these gestures in real time. So I propose a benchmark consisting of ‘human gestures navigation in chaotic narrow foot pathway’ for bipedal robots.
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+I will create a synthetic small dataset for demonstration and test it on current state of art physical models such as cosmos 3 2B and so forth.
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+## 🤖 Dataset Creation
+I took a sample image from https://arxiv.org/pdf/2604.00416 and used gemini & seeddance 2.0 to create a small synthetic benchmark for a narrow space. Later I annotated it with possible human actions. Although it doesn’t entirely represent real scenarios in Dhaka or Delhi, it’s a start. I selected cosmos3 to test this dataset.
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+Prompt “ Which way should the robot go to not collide with the incoming subject?”
+
 
 ## 🎯 Analysis of Qwen3-4B's errors
 The model was tested in ten diverse data points including Math, Physics, Code, Cultural understanding, toxicity and many more in English and Bengali to understand “Model’s Blind spots” particularly in reasoning. Technical and scientific prompts are collected from trusted sources. The cultural and Bengali prompts are created by me (as a trusted native speaker). I found that the model performed poorly in reasoning. It even stuck in thinking mode while answering olympiad questions. It couldn’t understand cultural norms and outputted toxic and harmful texts in Bengali prompts which suggests its pretrained dataset lacks Bengali contexts.