Add long-context document analysis use-case example for Nemotron 3 Super

use-case-examples/Long-Context-Document-Analysis-with-Nemotron-Super/README.md

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+# Long-Context Document Analysis with Nemotron 3 Super
+
+Analyze large document collections in a single context window using Nemotron 3 Super's native 1M-token context capability - no chunking, no RAG pipeline required.
+
+## Overview
+
+This example demonstrates how to leverage Nemotron 3 Super's 1M native context window for practical document analysis tasks, progressing from single-document summarization to full cross-document synthesis:
+
+1. **Document Corpus Construction** - Build a realistic multi-document corpus for analysis
+2. **Single-Document Analysis** - Summarize and extract key findings from individual documents
+3. **Multi-Document Q&A** - Answer questions that require synthesizing information across multiple documents
+4. **Cross-Document Synthesis** - Identify themes, contradictions, and connections across the full corpus
+5. **Context Length Scaling** - Compare quality and latency at different context sizes (32K, 128K, 256K)
+
+## Models Used
+
+| Component | Model | Parameters | Context Window | Deployment |
+|-----------|-------|------------|----------------|------------|
+| **Document Analysis** | `nvidia/nemotron-3-super-120b-a12b` | 120B total / 12B active | **1M tokens (native)** | NVIDIA API or self-hosted (vLLM) |
+
+## Why Nemotron 3 Super for Long-Context Tasks?
+
+- **1M native context window** - Trained with long-context extension via CPT methodology
+- **Outperforms on RULER at 1M** - Higher accuracy than GPT-OSS and Qwen3.5 at maximum context
+- **8x the context of Qwen 3.5** (128K) - Process entire codebases, legal corpora, or research collections
+- **Hybrid Mamba-Transformer MoE** architecture maintains quality across long sequences
+- **5x throughput improvement** - Process large documents efficiently
+
+## Requirements
+
+- Python 3.10+
+- NVIDIA API Key ([get one here](https://build.nvidia.com/))
+
+## Quick Start
+
+```bash
+# Install dependencies
+pip install openai tiktoken
+
+# Set your API key
+export NVIDIA_API_KEY="your-key-here"
+
+# Run the notebook
+jupyter notebook long_context_analysis_tutorial.ipynb
+```
+
+## What You'll Learn
+
+- How to structure prompts for effective long-context document analysis
+- Building multi-document corpora that fit within context windows
+- Techniques for cross-document synthesis without RAG
+- How context length affects response quality and latency
+- Best practices for instruction placement in long contexts