Final Project · 606363 — Computing Systems for Data Science and AI · Dr. Abdulkarim Albanna
AdvancedTeams of 2-3Deadline: Week 14
University of Petra
Faculty of Information Technology
Data Science and Artificial Intelligence
كلية تكنولوجيا المعلومات علم البيانات والذكاء الاصطناعي
Final Project Description
Course: 606363 — Computing Systems for Data Science and AI |
Semester: 2nd, 2025-2026 |
Instructor: Dr. Abdulkarim Albanna
1. Project Overview
Build a multi-agent system where specialized AI agents collaborate to produce high-quality research reports on any given topic. The system takes a research question as input, and through a coordinated workflow of autonomous agents, delivers a comprehensive, reviewed, and cited research report enriched with named entity extraction, source classification, web-based data collection via browser automation, and AI-generated illustrations.
This project integrates all major topics covered throughout the course: transformer architecture understanding, text classification, named entity recognition, LangChain fundamentals, LangGraph orchestration, large language model configuration, Mixture of Experts analysis, AI agent design with ReAct, multi-agent collaboration, Model Context Protocol for tool integration, Large Action Model capabilities for browser automation, and diffusion models for report illustration generation.
Working application, source code, documentation, transformer analysis report, presentation, demo video
2. Problem Statement
Conducting research is a time-consuming process that involves multiple stages: searching for relevant sources, reading and extracting key information, classifying sources by relevance and topic, extracting named entities, organizing findings into a coherent structure, writing a clear report with illustrations, and reviewing it for accuracy and completeness. Each stage requires different skills and focus areas.
This project applies the multi-agent paradigm to automate this pipeline, where each agent specializes in one stage, and a central orchestrator manages the workflow — mirroring how a real research team operates. The system leverages transformer-based language models as the backbone, employs text classification and NER techniques for intelligent information processing, uses the ReAct pattern for autonomous research, exposes tools via the Model Context Protocol, performs browser-based actions through Large Action Model capabilities, and generates visual content using diffusion models.
3. System Architecture
User Input (Research Topic)
|
v
+-------------------------+
| Orchestrator | LangGraph StateGraph - manages state & routing
+-------------------------+
|
+-------+-------+--------+--------+
| | | | |
v v v v v
+--------+ +--------+ +----------+ +--------+ +----------+
|Research| |Classif.| | NER | |Browser | | Analyzer|
| Agent | | Agent | | Agent | | Agent | | Agent |
|ReAct + | |Text | |Entity | |LAM for | |Theme |
|Search | |Classif.| |Extract | |Web | |Extract |
+--------+ +--------+ +----------+ +--------+ +----------+
| | | | |
+-----------+----------+------------+-----------+
|
v
+---------------+
| Writer Agent | Drafts report with citations
+---------------+
|
v
+------------------+
|Illustration Agent| Diffusion models for figures
+------------------+
|
v
+---------------+
| Critic Agent | Reviews accuracy, gaps, suggestions
+---------------+
|
+-----+------+
| |
APPROVE REVISE -----> back to Writer Agent
|
v
+---------------+
| Final Report | Markdown / PDF with illustrations
+---------------+
4. Technology Stack
Component
Technology
Transformer Backbone
Transformer-based LLMs (GPT-4, Claude, Mixtral); students must document the architecture
Create the LangGraph StateGraph skeleton with all agent nodes
Connected graph with conditional edges
3.2
Implement state transitions and routing logic
Working orchestration flow
3.3
Configure and optimize LLM parameters (temperature, max tokens, system prompts)
Documented LLM configuration per agent
3.4
Compare LLM performance across different models and parameter settings
LLM benchmark comparison table
3.5
Store findings in ChromaDB with embeddings
Searchable vector store
Course Alignment: Week 5 (LangGraph) -- core orchestration. Week 6 (LLMs) -- model selection and parameter optimization.
Phase 4: MoE Analysis & Research Agent (Week 7, then Week 9)
Task
Description
Deliverable
4.1
Analyze MoE architectures: study Mixtral, Switch Transformer, DeepSeek-MoE
Written MoE analysis comparing dense vs. sparse models
4.2
Optionally integrate an MoE model (Mixtral) for cost-efficient inference
MoE model integration or comparative benchmark
4.3
Implement the Research Agent with ReAct pattern
Agent that reasons about search strategy and acts on it
4.4
Integrate Tavily/Brave Search APIs as tools
Working search with result parsing
4.5
Build web scraper for detailed content extraction
Full text extraction from URLs
4.6
Build ReAct loop: agent decides to search more or stop
Autonomous iterative search behavior
Course Alignment: Week 7 (Mixture of Experts) -- analyze and optionally use MoE models. Week 9 (AI Agents and ReAct) -- implement the core research agent.
This project follows a Competition-Based Learning (CBL) approach. All teams will build their own version of the Multi-Agent Research Assistant, then compete head-to-head in a live evaluation event. This fosters deeper engagement, peer learning, and real-world benchmarking skills.
9.1 Competition Format
Stage
When
Description
Checkpoint 1
Week 7
Teams demo their NLP pipeline (classification + NER + research agent). Instructor feedback only, no scoring.
Checkpoint 2
Week 11
Teams demo full multi-agent pipeline with MCP integration. Peer feedback round.
Competition Day
Week 15
Live head-to-head competition. All teams receive the SAME 3 unseen research topics and run their systems in real-time.
9.2 Competition Day Protocol
The instructor reveals 3 research topics (unseen by all teams) at the start of the session.
All teams run their systems simultaneously on the same topics.
Each system produces 3 research reports. Time limit: 30 minutes per topic.
Reports are anonymized and distributed to a judging panel (instructor + 2 external judges or peer teams).
Teams give a 10-minute live presentation of their architecture and demo.
Judges score each report and presentation independently using the rubric below.
Average score across 3 topics from judging panel (rubric above)
Architecture & Topic Coverage
20%
Demonstrates all 12 course topics integrated into the system (transformers, NER, MoE, MCP, LAMs, diffusion, etc.)
Code Quality & Documentation
15%
Clean code, GitHub repo, README, technical report with architecture diagrams
Live Presentation
15%
10-minute demo + Q&A. Clarity, technical depth, team coordination
Peer Review
10%
Each team reviews 2 other teams' reports and provides structured feedback. Quality of review is graded.
9.5 Competition Prizes & Ranking
Rank
Award
Bonus
1st Place
Gold Award
+5 bonus points on final grade
2nd Place
Silver Award
+3 bonus points on final grade
3rd Place
Bronze Award
+2 bonus points on final grade
Best Innovation
Innovation Award
+2 bonus points (can stack with placement)
Best Report
Quality Award
+2 bonus points (can stack with placement)
Fastest System
Speed Award
+1 bonus point
Academic Integrity: All code must be original team work. Teams may use open-source libraries and LLM APIs but must not copy other teams' agent logic or prompts. Plagiarism results in disqualification and a failing grade.
10. Expected Output Example
Input: "The impact of Mixture of Experts architecture on Large Language Model efficiency"
# Research Report: The Impact of MoE on LLM Efficiency
## Executive Summary
This report examines how Mixture of Experts (MoE) architectures have
transformed large language model efficiency by enabling sparse activation,
reducing computational costs while maintaining or improving performance...
## Named Entities Identified
- Organizations: Google, Mistral AI, DeepSeek
- People: Noam Shazeer, William Fedus, Albert Jiang
- Technologies: Switch Transformer, Mixtral 8x7B, DeepSeek-MoE, GShard
- Dates: 2017, 2021, 2024
## Source Classification Summary
- Technical Papers: 8 sources (high relevance)
- Blog Posts / Tutorials: 4 sources (medium relevance)
- News Articles: 2 sources (low relevance, filtered)
## 1. Introduction
The rapid scaling of large language models has created significant
computational challenges...
## 2. Transformer Architecture Background
### 2.1 Self-Attention Mechanism
### 2.2 Feed-Forward Networks as Expert Candidates
## 3. MoE Architecture Fundamentals
### 3.1 Gating Mechanisms
### 3.2 Expert Networks
### 3.3 Top-K Selection
## 4. Key MoE Models
### 4.1 Switch Transformer (Google, 2021)
### 4.2 Mixtral 8x7B (Mistral, 2024)
### 4.3 DeepSeek-MoE
## 5. Performance Analysis
### 5.1 Computational Efficiency
### 5.2 Quality Benchmarks
[Figure 1: MoE vs Dense Model Efficiency Comparison -- generated illustration]
## 6. Challenges and Limitations
## 7. Conclusion
## References
[1] Fedus et al., "Switch Transformers", 2021. https://...
[2] Mistral AI, "Mixtral of Experts", 2024. https://...
[3] Vaswani et al., "Attention Is All You Need", 2017. https://...
...
11. Submission Requirements
Item
Details
Source Code
GitHub repository with clean commit history and README
Transformer Analysis
Written document explaining the transformer architecture of the chosen LLM backbone
MoE Comparison
Analysis report comparing MoE vs. dense model architectures with benchmarks
