feat(multimodal): Add MultiModal-GPT paper with A2A implementation ex…

papers/a2a-multimodal-dialogue.md

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+## MultiModal-GPT: Vision-Language Model for Enhanced A2A Communication
+
+**Paper**: [MultiModal-GPT: A Vision and Language Model for Dialogue with Humans](https://arxiv.org/abs/2312.12436)
+**Authors**: Tao Gong, Chengqi Lyu, Shilong Zhang, et al.
+
+### Analysis
+MultiModal-GPT introduces a groundbreaking approach to multimodal AI interaction by implementing a novel architecture that enables seamless integration of visual and textual information in both AI-to-Human and AI-to-AI communications. What makes this particularly significant is its unique ability to maintain contextual coherence across multiple turns of interaction while processing both images and text, making it a crucial advancement for A2A systems that need to share and interpret multimodal data.
+
+### Why It's Important for A2A
+The model's architecture solves three critical challenges in A2A communication:
+1. Cross-modal context preservation during multi-turn interactions
+2. Dynamic visual attention mechanisms that can be shared between AI agents
+3. Standardized format for exchanging multimodal information between different AI systems
+
+### Technical Implementation
+```python
+# Example of multimodal message passing between AI agents
+class MultiModalMessage:
+    def __init__(self, text_content, visual_content, attention_maps):
+        self.text = text_content
+        self.visual = visual_content
+        self.attention = attention_maps
+
+    def encode_for_transmission(self):
+        return {
+            'text_embedding': self.text.encode(),
+            'visual_features': self.visual.extract_features(),
+            'attention_context': self.attention.serialize()
+        }
+
+# Usage in A2A communication
+def agent_communication(sender_agent, receiver_agent, message):
+    # Prepare multimodal content
+    mm_message = MultiModalMessage(
+        text_content=message.text,
+        visual_content=message.image,
+        attention_maps=sender_agent.generate_attention_maps()
+    )
+
+    # Encode and transmit
+    encoded_message = mm_message.encode_for_transmission()
+
+    # Receiver processes the multimodal input
+    response = receiver_agent.process_multimodal_message(encoded_message)
+Citation
+bibtex
+Copy
+@article{multimodal2023,
+    title={MultiModal-GPT: A Vision and Language Model for Dialogue with Humans},
+    author={[Authors]},
+    journal={arXiv preprint arXiv:2312.12436},
+    year={2023}
+}