docs: Add comprehensive research roadmap and Phase 1 plan

- Created RESEARCH_ROADMAP.md with 12 cutting-edge AI features
- References 20+ recent papers (2017-2024)
- Detailed implementations for RAG, attention viz, ToT reasoning
- Added PHASE1_IMPLEMENTATION.md with concrete 3-day build plan
- Positions as research lab showcasing SOTA AI/ML techniques
- Includes paper citations, uncertainty quantification, safety systems

PHASE1_IMPLEMENTATION.md

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+# 🚀 Phase 1 Implementation Plan - Research Features
+
+## Quick Wins: Build These First (2-3 days)
+
+### Priority 1: RAG Pipeline Visualization ⭐⭐⭐
+**Why:** Shows research credibility, transparency, visual appeal
+**Effort:** Medium
+**Impact:** High
+
+#### Implementation Steps:
+
+1. **Backend: Track RAG stages** (`api/rag_tracker.py`)
+```python
+class RAGTracker:
+    def __init__(self):
+        self.stages = []
+    
+    def track_query_encoding(self, query, embedding):
+        self.stages.append({
+            "stage": "encoding",
+            "query": query,
+            "embedding_preview": embedding[:10],  # First 10 dims
+            "timestamp": time.time()
+        })
+    
+    def track_retrieval(self, documents, scores):
+        self.stages.append({
+            "stage": "retrieval",
+            "num_docs": len(documents),
+            "top_scores": scores[:5],
+            "documents": [{"text": d[:100], "score": s} 
+                         for d, s in zip(documents[:5], scores[:5])]
+        })
+    
+    def track_generation(self, context, response):
+        self.stages.append({
+            "stage": "generation",
+            "context_length": len(context),
+            "response_length": len(response),
+            "attribution": self.extract_citations(response)
+        })
+```
+
+2. **Frontend: RAG Pipeline Viewer** (add to `index.html`)
+```html
+<div class="rag-pipeline" id="rag-pipeline">
+  <div class="stage" data-stage="encoding">
+    <div class="stage-icon">🔍</div>
+    <div class="stage-title">Query Encoding</div>
+    <div class="stage-details">
+      <div class="embedding-preview"></div>
+    </div>
+  </div>
+  
+  <div class="stage" data-stage="retrieval">
+    <div class="stage-icon">📚</div>
+    <div class="stage-title">Document Retrieval</div>
+    <div class="retrieved-docs"></div>
+  </div>
+  
+  <div class="stage" data-stage="generation">
+    <div class="stage-icon">✍️</div>
+    <div class="stage-title">Generation</div>
+    <div class="citations"></div>
+  </div>
+</div>
+```
+
+3. **Styling: Research Lab Theme**
+```css
+.rag-pipeline {
+  background: #1e1e1e;
+  color: #d4d4d4;
+  font-family: 'Fira Code', monospace;
+  padding: 20px;
+  border-radius: 8px;
+  margin: 20px 0;
+}
+
+.stage {
+  border-left: 3px solid #007acc;
+  padding: 15px;
+  margin: 10px 0;
+  transition: all 0.3s;
+}
+
+.stage.active {
+  border-left-color: #4ec9b0;
+  background: #2d2d2d;
+}
+
+.embedding-preview {
+  font-family: 'Courier New', monospace;
+  background: #0e0e0e;
+  padding: 10px;
+  border-radius: 4px;
+  overflow-x: auto;
+}
+```
+
+---
+
+### Priority 2: Attention Visualization ⭐⭐
+**Why:** Shows interpretability, looks impressive, educational
+**Effort:** Medium-High
+**Impact:** Very High (visually stunning)
+
+#### Implementation:
+
+1. **Mock attention data in demo mode**
+```python
+def generate_attention_heatmap(query: str, response: str):
+    """Generate synthetic attention weights for demo."""
+    query_tokens = query.split()
+    response_tokens = response.split()[:20]  # First 20 tokens
+    
+    # Simulate attention: query tokens attend to relevant response tokens
+    attention = np.random.rand(len(query_tokens), len(response_tokens))
+    
+    # Add some structure (diagonal-ish for realistic look)
+    for i in range(len(query_tokens)):
+        attention[i, i:i+3] *= 2  # Boost nearby tokens
+    
+    attention = softmax(attention, axis=1)
+    
+    return {
+        "query_tokens": query_tokens,
+        "response_tokens": response_tokens,
+        "attention_weights": attention.tolist()
+    }
+```
+
+2. **Interactive heatmap with Plotly or D3.js**
+```javascript
+function renderAttentionHeatmap(data) {
+  const trace = {
+    x: data.response_tokens,
+    y: data.query_tokens,
+    z: data.attention_weights,
+    type: 'heatmap',
+    colorscale: 'Viridis',
+    hoverongaps: false
+  };
+  
+  const layout = {
+    title: 'Attention Pattern: Query → Response',
+    xaxis: { title: 'Response Tokens' },
+    yaxis: { title: 'Query Tokens' },
+    paper_bgcolor: '#1e1e1e',
+    plot_bgcolor: '#1e1e1e',
+    font: { color: '#d4d4d4' }
+  };
+  
+  Plotly.newPlot('attention-heatmap', [trace], layout);
+}
+```
+
+---
+
+### Priority 3: Paper Citation System ⭐⭐⭐
+**Why:** Academic credibility, research positioning
+**Effort:** Low
+**Impact:** High (perception)
+
+#### Implementation:
+
+1. **Paper database** (`api/papers.py`)
+```python
+RESEARCH_PAPERS = {
+    "attention": {
+        "title": "Attention is All You Need",
+        "authors": "Vaswani et al.",
+        "year": 2017,
+        "venue": "NeurIPS",
+        "url": "https://arxiv.org/abs/1706.03762",
+        "citations": 87000,
+        "summary": "Introduced the Transformer architecture using self-attention."
+    },
+    "rag": {
+        "title": "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks",
+        "authors": "Lewis et al.",
+        "year": 2020,
+        "venue": "NeurIPS",
+        "url": "https://arxiv.org/abs/2005.11401",
+        "citations": 3200,
+        "summary": "Combines retrieval with generation for factual QA."
+    },
+    "tot": {
+        "title": "Tree of Thoughts: Deliberate Problem Solving with LLMs",
+        "authors": "Yao et al.",
+        "year": 2023,
+        "venue": "NeurIPS",
+        "url": "https://arxiv.org/abs/2305.10601",
+        "citations": 450,
+        "summary": "Explores multiple reasoning paths like human problem-solving."
+    },
+    # Add 15+ more papers...
+}
+
+def get_relevant_papers(feature: str) -> List[Dict]:
+    """Return papers relevant to the current feature."""
+    feature_paper_map = {
+        "rag": ["rag", "dense_retrieval"],
+        "attention": ["attention", "transformers"],
+        "reasoning": ["tot", "cot", "self_consistency"],
+        # ...
+    }
+    return [RESEARCH_PAPERS[p] for p in feature_paper_map.get(feature, [])]
+```
+
+2. **Citation widget**
+```html
+<div class="paper-citations">
+  <div class="citation-header">
+    📚 Research Foundations
+  </div>
+  <div class="citation-list">
+    <div class="citation-item">
+      <div class="citation-title">
+        "Attention is All You Need"
+      </div>
+      <div class="citation-meta">
+        Vaswani et al., NeurIPS 2017 | 87k citations
+      </div>
+      <div class="citation-actions">
+        <a href="#" class="btn-citation">PDF</a>
+        <a href="#" class="btn-citation">Code</a>
+        <a href="#" class="btn-citation">Cite</a>
+      </div>
+    </div>
+  </div>
+</div>
+```
+
+---
+
+### Priority 4: Uncertainty Quantification ⭐⭐
+**Why:** Shows sophistication, useful for users
+**Effort:** Low-Medium
+**Impact:** Medium-High
+
+#### Implementation:
+
+1. **Confidence estimation** (demo mode)
+```python
+def estimate_confidence(query: str, response: str, mode: str) -> Dict:
+    """
+    Estimate confidence based on heuristics.
+    In production, use actual model logits.
+    """
+    # Heuristics for demo
+    confidence_base = 0.7
+    
+    # Boost confidence for technical mode (seems more certain)
+    if mode == "technical":
+        confidence_base += 0.1
+    
+    # Lower confidence for vague queries
+    if len(query.split()) < 5:
+        confidence_base -= 0.15
+    
+    # Add some noise for realism
+    confidence = confidence_base + np.random.uniform(-0.1, 0.1)
+    confidence = np.clip(confidence, 0.3, 0.95)
+    
+    # Estimate epistemic vs aleatoric
+    epistemic = confidence * 0.6  # Model uncertainty
+    aleatoric = confidence * 0.4  # Data ambiguity
+    
+    return {
+        "overall": round(confidence, 2),
+        "epistemic": round(epistemic, 2),
+        "aleatoric": round(aleatoric, 2),
+        "calibration_error": round(abs(confidence - 0.8), 3),
+        "interpretation": interpret_confidence(confidence)
+    }
+
+def interpret_confidence(conf: float) -> str:
+    if conf > 0.85:
+        return "High confidence - well-established knowledge"
+    elif conf > 0.65:
+        return "Moderate confidence - generally accurate"
+    else:
+        return "Low confidence - consider verifying independently"
+```
+
+2. **Confidence gauge widget**
+```html
+<div class="confidence-gauge">
+  <div class="gauge-header">Confidence Analysis</div>
+  
+  <div class="gauge-visual">
+    <svg viewBox="0 0 200 100">
+      <!-- Arc background -->
+      <path d="M 20,80 A 60,60 0 0,1 180,80" 
+            stroke="#333" stroke-width="20" fill="none"/>
+      
+      <!-- Confidence arc (dynamic) -->
+      <path id="confidence-arc" 
+            d="M 20,80 A 60,60 0 0,1 180,80" 
+            stroke="url(#confidence-gradient)" 
+            stroke-width="20" 
+            fill="none"
+            stroke-dasharray="251.2"
+            stroke-dashoffset="125.6"/>
+      
+      <defs>
+        <linearGradient id="confidence-gradient">
+          <stop offset="0%" stop-color="#f56565"/>
+          <stop offset="50%" stop-color="#f6ad55"/>
+          <stop offset="100%" stop-color="#48bb78"/>
+        </linearGradient>
+      </defs>
+    </svg>
+    
+    <div class="gauge-value">76%</div>
+  </div>
+  
+  <div class="uncertainty-breakdown">
+    <div class="uncertainty-item">
+      <span class="label">Epistemic (Model)</span>
+      <div class="bar" style="width: 60%"></div>
+    </div>
+    <div class="uncertainty-item">
+      <span class="label">Aleatoric (Data)</span>
+      <div class="bar" style="width: 85%"></div>
+    </div>
+  </div>
+</div>
+```
+
+---
+
+## Integration Plan
+
+### Step 1: Update `api/ask.py`
+Add these fields to response:
+```python
+{
+  "result": "...",
+  "research_data": {
+    "rag_pipeline": {...},  # RAG stages
+    "attention": {...},      # Attention weights
+    "confidence": {...},     # Uncertainty metrics
+    "papers": [...]          # Relevant citations
+  }
+}
+```
+
+### Step 2: Update `public/index.html`
+Add new sections:
+```html
+<div class="research-panel" style="display:none" id="research-panel">
+  <div class="panel-tabs">
+    <button class="tab active" data-tab="rag">RAG Pipeline</button>
+    <button class="tab" data-tab="attention">Attention</button>
+    <button class="tab" data-tab="confidence">Confidence</button>
+    <button class="tab" data-tab="papers">Papers</button>
+  </div>
+  
+  <div class="panel-content">
+    <div id="rag-tab" class="tab-pane active"></div>
+    <div id="attention-tab" class="tab-pane"></div>
+    <div id="confidence-tab" class="tab-pane"></div>
+    <div id="papers-tab" class="tab-pane"></div>
+  </div>
+</div>
+
+<button id="toggle-research" class="btn-toggle">
+  🔬 Show Research Details
+</button>
+```
+
+### Step 3: Add Dependencies
+```bash
+# For visualization
+npm install plotly.js d3
+
+# Or use CDN in HTML
+<script src="https://cdn.plot.ly/plotly-2.27.0.min.js"></script>
+```
+
+---
+
+## Timeline
+
+**Day 1:**
+- ✅ Set up paper database
+- ✅ Add citation widget
+- ✅ Basic confidence estimation
+- ✅ Update response structure
+
+**Day 2:**
+- ✅ Implement RAG tracker (mock data)
+- ✅ Build RAG pipeline UI
+- ✅ Style research panel
+- ✅ Add confidence gauge
+
+**Day 3:**
+- ✅ Generate attention heatmaps
+- ✅ Integrate Plotly visualization
+- ✅ Polish animations
+- ✅ Test & deploy
+
+---
+
+## Success Criteria
+
+✓ Users can toggle "Research Mode"
+✓ 4 interactive visualizations working
+✓ 10+ papers cited with links
+✓ Confidence scores shown per response
+✓ Dark theme, monospace aesthetic
+✓ Export visualizations as images
+✓ Mobile responsive
+
+---
+
+## Next Phase Preview
+
+Once Phase 1 is solid, Phase 2 adds:
+- 🌳 Tree-of-Thoughts interactive explorer
+- 🕸️ Knowledge graph visualization
+- 🧠 Cognitive load real-time monitor
+- 📊 A/B testing dashboard
+
+**Ready to start implementing?** Let's begin with the paper citation system (easiest) or RAG pipeline (most visual impact)?

RESEARCH_ROADMAP.md

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+# 🔬 Eidolon Cognitive Tutor - Research Lab Roadmap
+
+## Vision: Showcase Cutting-Edge AI/ML Research in Education
+
+Transform the tutor into a **living research demonstration** that visualizes state-of-the-art AI concepts, inspired by recent breakthrough papers (2020-2024).
+
+---
+
+## 🎯 Core Research Themes
+
+### 1. **Explainable AI & Interpretability**
+*Show users HOW the AI thinks, not just WHAT it outputs*
+
+#### 🧠 Cognitive Architecture Visualization
+**Papers:**
+- "Attention is All You Need" (Vaswani et al., 2017)
+- "A Mathematical Framework for Transformer Circuits" (Elhage et al., 2021)
+- "Interpretability in the Wild" (Anthropic, 2023)
+
+**Implementation:**
+```
+┌─────────────────────────────────────────┐
+│  🧠 COGNITIVE PROCESS VIEWER            │
+├─────────────────────────────────────────┤
+│  Query: "Explain quantum entanglement"  │
+│                                         │
+│  [1] Token Attention Heatmap            │
+│      ████████░░░░ "quantum" → physics   │
+│      ██████████░░ "entangle" → connect  │
+│                                         │
+│  [2] Knowledge Retrieval                │
+│      ↳ Quantum Mechanics (0.94)         │
+│      ↳ Bell's Theorem (0.87)            │
+│      ↳ EPR Paradox (0.81)               │
+│                                         │
+│  [3] Reasoning Chain                    │
+│      Think: Need simple analogy         │
+│      → Retrieve: coin flip metaphor     │
+│      → Synthesize: connected particles  │
+│      → Verify: scientifically accurate  │
+│                                         │
+│  [4] Confidence: 89% ±3%                │
+└─────────────────────────────────────────┘
+```
+
+**Features:**
+- Real-time attention weight visualization
+- Interactive layer-by-layer activation inspection
+- Concept activation mapping
+- Neuron-level feature visualization
+
+---
+
+### 2. **Meta-Learning & Few-Shot Adaptation**
+*Demonstrate how AI learns to learn*
+
+#### 🎓 Adaptive Learning System
+**Papers:**
+- "Model-Agnostic Meta-Learning (MAML)" (Finn et al., 2017)
+- "Learning to Learn by Gradient Descent" (Andrychowicz et al., 2016)
+- "Meta-Learning with Implicit Gradients" (Rajeswaran et al., 2019)
+
+**Implementation:**
+```python
+class MetaLearningTutor:
+    """
+    Adapts teaching strategy based on learner's responses.
+    Uses inner loop (student adaptation) and outer loop (strategy refinement).
+    """
+    
+    def adapt(self, student_responses: List[Response]) -> TeachingPolicy:
+        # Extract learning patterns
+        mastery_curve = self.estimate_mastery(student_responses)
+        confusion_points = self.identify_gaps(student_responses)
+        
+        # Few-shot adaptation: learn from 3-5 interactions
+        adapted_policy = self.maml_adapt(
+            base_policy=self.teaching_policy,
+            support_set=student_responses[-5:],  # Last 5 interactions
+            adaptation_steps=3
+        )
+        
+        return adapted_policy
+```
+
+**Visualization:**
+- Learning curve evolution
+- Gradient flow diagrams
+- Task similarity clustering
+- Adaptation trajectory in embedding space
+
+---
+
+### 3. **Knowledge Graphs & Multi-Hop Reasoning**
+*Show structured knowledge retrieval and reasoning*
+
+#### 🕸️ Interactive Knowledge Graph
+**Papers:**
+- "Graph Neural Networks: A Review" (Zhou et al., 2020)
+- "Knowledge Graphs" (Hogan et al., 2021)
+- "REALM: Retrieval-Augmented Language Model Pre-Training" (Guu et al., 2020)
+
+**Implementation:**
+```
+Query: "How does photosynthesis relate to climate change?"
+
+Knowledge Graph Traversal:
+  [Photosynthesis] ──produces──→ [Oxygen]
+         ↓                            ↓
+    absorbs CO2              breathed by animals
+         ↓                            ↓
+  [Carbon Cycle] ←──affects── [Climate Change]
+         ↓
+    regulated by
+         ↓
+   [Deforestation] ──causes──→ [Global Warming]
+
+Multi-Hop Reasoning Path (3 hops):
+  1. Photosynthesis absorbs CO2 (confidence: 0.99)
+  2. CO2 is a greenhouse gas (confidence: 0.98)
+  3. Therefore photosynthesis mitigates climate change (confidence: 0.92)
+```
+
+**Features:**
+- Interactive graph exploration (zoom, filter, highlight)
+- GNN reasoning path visualization
+- Confidence propagation through graph
+- Counterfactual reasoning ("What if we remove this node?")
+
+---
+
+### 4. **Retrieval-Augmented Generation (RAG)**
+*Transparent source attribution and knowledge grounding*
+
+#### 📚 RAG Pipeline Visualization
+**Papers:**
+- "Retrieval-Augmented Generation for Knowledge-Intensive NLP" (Lewis et al., 2020)
+- "Dense Passage Retrieval" (Karpukhin et al., 2020)
+- "REPLUG: Retrieval-Augmented Black-Box Language Models" (Shi et al., 2023)
+
+**Implementation:**
+```
+┌─────────────────────────────────────────┐
+│  RAG PIPELINE INSPECTOR                 │
+├─────────────────────────────────────────┤
+│  [1] Query Encoding                     │
+│      "Explain transformer architecture" │
+│      → Embedding: [0.23, -0.45, ...]    │
+│                                         │
+│  [2] Semantic Search                    │
+│      🔍 Searching 10M+ passages...      │
+│      ✓ Top 5 retrieved in 12ms          │
+│                                         │
+│  [3] Retrieved Context                  │
+│      📄 "Attention is All You Need"     │
+│         Relevance: 0.94 | Cited: 87k    │
+│      📄 "BERT: Pre-training..."         │
+│         Relevance: 0.89 | Cited: 52k    │
+│      [show more...]                     │
+│                                         │
+│  [4] Re-ranking (Cross-Encoder)         │
+│      Passage 1: 0.94 → 0.97 ⬆           │
+│      Passage 2: 0.89 → 0.85 ⬇           │
+│                                         │
+│  [5] Generation with Attribution        │
+│      "Transformers use self-attention   │
+│       [1] to process sequences..."      │
+│                                         │
+│      [1] Vaswani et al. 2017, p.3       │
+└─────────────────────────────────────────┘
+```
+
+**Features:**
+- Embedding space visualization (t-SNE/UMAP)
+- Semantic similarity scores
+- Source credibility indicators
+- Hallucination detection
+
+---
+
+### 5. **Uncertainty Quantification & Calibration**
+*Show when the AI is confident vs. uncertain*
+
+#### 📊 Confidence Calibration System
+**Papers:**
+- "On Calibration of Modern Neural Networks" (Guo et al., 2017)
+- "Uncertainty in Deep Learning" (Gal, 2016)
+- "Conformal Prediction Under Covariate Shift" (Tibshirani et al., 2019)
+
+**Implementation:**
+```python
+class UncertaintyQuantifier:
+    """
+    Estimates epistemic (model) and aleatoric (data) uncertainty.
+    """
+    
+    def compute_uncertainty(self, response: str) -> Dict:
+        return {
+            "epistemic": self.model_uncertainty(),  # What model doesn't know
+            "aleatoric": self.data_uncertainty(),   # Inherent ambiguity
+            "calibration_score": self.calibration(), # How well-calibrated
+            "conformal_set": self.conformal_predict() # Prediction interval
+        }
+```
+
+**Visualization:**
+```
+┌─────────────────────────────────────────┐
+│  UNCERTAINTY DASHBOARD                  │
+├─────────────────────────────────────────┤
+│  Overall Confidence: 76% ±8%            │
+│                                         │
+│  Epistemic (Model) ██████░░░░ 60%      │
+│  → Model hasn't seen enough examples    │
+│                                         │
+│  Aleatoric (Data)  █████████░ 85%      │
+│  → Question has inherent ambiguity      │
+│                                         │
+│  Calibration Plot:                      │
+│   1.0 ┤        ╱                        │
+│       │      ╱                          │
+│       │    ╱ (perfectly calibrated)     │
+│   0.0 └──────────────                   │
+│                                         │
+│  ⚠️  Low confidence detected!           │
+│  💡 Suggestion: "Could you clarify...?" │
+└─────────────────────────────────────────┘
+```
+
+---
+
+### 6. **Constitutional AI & Safety**
+*Demonstrate alignment and safety mechanisms*
+
+#### 🛡️ Safety-First Design
+**Papers:**
+- "Constitutional AI: Harmlessness from AI Feedback" (Bai et al., 2022)
+- "Training language models to follow instructions with human feedback" (Ouyang et al., 2022)
+- "Red Teaming Language Models" (Perez et al., 2022)
+
+**Implementation:**
+```
+User Query: "How do I hack into..."
+
+┌─────────────────────────────────────────┐
+│  🛡️ SAFETY SYSTEM ACTIVATED             │
+├─────────────────────────────────────────┤
+│  [1] Harmfulness Detection              │
+│      ⚠️  Potential harm score: 0.87     │
+│      Category: Unauthorized access      │
+│                                         │
+│  [2] Constitutional Principles          │
+│      ✓ Principle 1: Do no harm          │
+│      ✓ Principle 2: Respect privacy     │
+│      ✓ Principle 3: Follow laws         │
+│                                         │
+│  [3] Response Correction                │
+│      Original: [redacted harmful path]  │
+│      Revised: "I can't help with that,  │
+│                but I can explain..."    │
+│                                         │
+│  [4] Educational Redirect               │
+│      Suggested: "Cybersecurity ethics"  │
+│                 "Penetration testing"   │
+└─────────────────────────────────────────┘
+```
+
+**Features:**
+- Real-time safety scoring
+- Principle-based reasoning chains
+- Adversarial robustness testing
+- Red team attack visualization
+
+---
+
+### 7. **Tree-of-Thoughts Reasoning**
+*Show deliberate problem-solving strategies*
+
+#### 🌳 Reasoning Tree Visualization
+**Papers:**
+- "Tree of Thoughts: Deliberate Problem Solving" (Yao et al., 2023)
+- "Chain-of-Thought Prompting" (Wei et al., 2022)
+- "Self-Consistency Improves Chain of Thought" (Wang et al., 2022)
+
+**Implementation:**
+```
+Problem: "How would you explain relativity to a 10-year-old?"
+
+Tree of Thoughts:
+                    [Root: Strategy Selection]
+                            /    |    \
+                           /     |     \
+                  [Analogy] [Story] [Demo]
+                     /          |         \
+             [Train]  [Ball]  [Twin]  [Experiment]
+            /    |      |       |         |
+       [Fast] [Slow] [Time] [Space]   [Show]
+          ↓      ↓      ↓       ↓         ↓
+     Eval:0.8  0.9    0.7     0.6       0.5
+
+Selected Path (highest score):
+  Strategy: Analogy → Concept: Train → Example: Slow train
+
+Self-Consistency Check:
+  ✓ Sampled 5 reasoning paths
+  ✓ 4/5 agree on train analogy
+  ✓ Confidence: 94%
+```
+
+**Features:**
+- Interactive tree navigation
+- Branch pruning visualization
+- Self-evaluation scores at each node
+- Comparative reasoning paths
+
+---
+
+### 8. **Cognitive Load Theory**
+*Optimize learning based on cognitive science*
+
+#### 🧠 Cognitive Load Estimation
+**Papers:**
+- "Cognitive Load Theory" (Sweller, 1988)
+- "Zone of Proximal Development" (Vygotsky)
+- "Measuring Cognitive Load Using Dual-Task Methodology" (Brünken et al., 2003)
+
+**Implementation:**
+```python
+class CognitiveLoadEstimator:
+    """
+    Estimates intrinsic, extraneous, and germane cognitive load.
+    """
+    
+    def estimate_load(self, response_metrics: Dict) -> CognitiveLoad:
+        return CognitiveLoad(
+            intrinsic=self.concept_complexity(),  # Topic difficulty
+            extraneous=self.presentation_load(),  # UI/format overhead
+            germane=self.schema_construction(),   # Productive learning
+            
+            # Zone of Proximal Development
+            zpd_score=self.zpd_alignment(),  # Too easy/hard/just right
+            optimal_challenge=self.compute_optimal_difficulty()
+        )
+```
+
+**Visualization:**
+```
+┌─────────────────────────────────────────┐
+│  COGNITIVE LOAD MONITOR                 │
+├─────────────────────────────────────────┤
+│  Current Load: 67% (Optimal: 60-80%)    │
+│                                         │
+│  Intrinsic ████████░░░░ 65%            │
+│  (concept complexity)                   │
+│                                         │
+│  Extraneous ███░░░░░░░░ 25%            │
+│  (presentation overhead)                │
+│                                         │
+│  Germane ████████████ 95%              │
+│  (productive learning)                  │
+│                                         │
+│  📍 Zone of Proximal Development        │
+│   Too Easy ←─[You]─────→ Too Hard      │
+│                                         │
+│  💡 Recommendation: Increase difficulty │
+│     from Level 3 → Level 4              │
+└─────────────────────────────────────────┘
+```
+
+---
+
+### 9. **Multimodal Learning**
+*Integrate vision, language, code, and more*
+
+#### 🎨 Cross-Modal Reasoning
+**Papers:**
+- "CLIP: Learning Transferable Visual Models" (Radford et al., 2021)
+- "Flamingo: Visual Language Models" (Alayrac et al., 2022)
+- "GPT-4 Technical Report" (OpenAI, 2023) - multimodal capabilities
+
+**Implementation:**
+```
+Query: "Explain binary search with a diagram"
+
+Response:
+  [Text] "Binary search repeatedly divides..."
+     ↓
+  [Code] def binary_search(arr, target): ...
+     ↓
+  [Diagram] 
+     [1,3,5,7,9,11,13,15]
+          ↓
+        [9,11,13,15]
+          ↓
+        [9,11]
+     ↓
+  [Animation] Step-by-step execution
+     ↓
+  [Interactive] Try your own example!
+
+Cross-Modal Attention:
+  Text ←──0.87──→ Code
+  Code ←──0.92──→ Diagram
+  Diagram ←─0.78─→ Animation
+```
+
+**Features:**
+- LaTeX equation rendering
+- Mermaid diagram generation
+- Code execution sandbox
+- Interactive visualizations
+
+---
+
+### 10. **Direct Preference Optimization (DPO)**
+*Show alignment without reward models*
+
+#### 🎯 Preference Learning Visualization
+**Papers:**
+- "Direct Preference Optimization" (Rafailov et al., 2023)
+- "RLHF: Training language models to follow instructions" (Ouyang et al., 2022)
+
+**Implementation:**
+```
+User Feedback: 👍 or 👎 on responses
+
+┌─────────────────────────────────────────┐
+│  PREFERENCE LEARNING DASHBOARD          │
+├─────────────────────────────────────────┤
+│  Response A: "Quantum mechanics is..."  │
+│  Response B: "Let me explain quantum.." │
+│                                         │
+│  User Preferred: B (more engaging)      │
+│                                         │
+│  Policy Update:                         │
+│    Engagement ↑ +15%                    │
+│    Technical detail ↓ -5%              │
+│    Simplicity ↑ +20%                    │
+│                                         │
+│  Implicit Reward Model:                 │
+│    r(B) - r(A) = +2.3                   │
+│                                         │
+│  Learning Progress:                     │
+│    Epoch 0 ████████████████░░ 85%      │
+│    Converged after 142 preferences      │
+└─────────────────────────────────────────┘
+```
+
+---
+
+## 🏗️ Architecture Overview
+
+```
+┌────────────────────────────────────────────────────────┐
+│                    USER INTERFACE                      │
+│  ┌──────────┐ ┌──────────┐ ┌──────────┐              │
+│  │ Chat UI  │ │ Viz Panel│ │ Controls │              │
+│  └────┬─────┘ └────┬─────┘ └────┬─────┘              │
+└───────┼────────────┼────────────┼────────────────────┘
+        │            │            │
+┌───────▼────────────▼────────────▼────────────────────┐
+│              COGNITIVE ORCHESTRATOR                   │
+│  ┌────────────────────────────────────────────────┐  │
+│  │  • Query Understanding                          │  │
+│  │  • Reasoning Strategy Selection                 │  │
+│  │  • Multi-System Coordination                    │  │
+│  └────────────────────────────────────────────────┘  │
+└──────────┬──────────────┬──────────────┬────────────┘
+           │              │              │
+    ┌──────▼───┐   ┌──────▼───┐   ┌────▼──────┐
+    │   RAG    │   │Knowledge │   │Uncertainty│
+    │ Pipeline │   │  Graph   │   │Quantifier │
+    └──────────┘   └──────────┘   └───────────┘
+           │              │              │
+    ┌──────▼──────────────▼──────────────▼───────┐
+    │        LLM with Instrumentation             │
+    │  • Attention tracking                        │
+    │  • Activation logging                        │
+    │  • Token probability capture                 │
+    └─────────────────────────────────────────────┘
+```
+
+---
+
+## 🎨 UI/UX Design Principles
+
+### Research Lab Aesthetic
+- **Dark theme** with syntax highlighting (like Jupyter/VSCode)
+- **Monospace fonts** for code and data
+- **Live metrics** updating in real-time
+- **Interactive plots** (Plotly/D3.js)
+- **Collapsible panels** for technical details
+- **Export options** (save visualizations, data, configs)
+
+### Information Hierarchy
+```
+┌─────────────────────────────────────────┐
+│  [Main Response]  ← Primary focus       │
+│   Clear, readable, large                │
+│                                         │
+│  [Reasoning Visualization]              │
+│   ↳ Expandable details                  │
+│   ↳ Interactive elements                │
+│                                         │
+│  [Technical Metrics]                    │
+│   ↳ Confidence, uncertainty             │
+│   ↳ Performance stats                   │
+│                                         │
+│  [Research Context]                     │
+│   ↳ Paper references                    │
+│   ↳ Related concepts                    │
+└─────────────────────────────────────────┘
+```
+
+---
+
+## 📊 Data & Metrics to Track
+
+### Learning Analytics
+- **Mastery progression** per concept
+- **Difficulty calibration** accuracy
+- **Engagement metrics** (time, interactions)
+- **Confusion signals** (repeated questions, clarifications)
+
+### AI Performance Metrics
+- **Inference latency** (p50, p95, p99)
+- **Token usage** per query
+- **Cache hit rates**
+- **Retrieval precision/recall**
+- **Calibration error** (Expected Calibration Error)
+- **Hallucination rate**
+
+### A/B Testing Framework
+- **Reasoning strategies** (ToT vs CoT vs ReAct)
+- **Explanation styles** (technical vs analogical)
+- **Interaction patterns** (Socratic vs direct)
+
+---
+
+## 🔬 Experimental Features
+
+### 1. **Research Playground**
+- **Compare models** side-by-side (GPT-4 vs Claude vs Llama)
+- **Ablation studies** (remove RAG, change prompts)
+- **Hyperparameter tuning** interface
+
+### 2. **Dataset Explorer**
+- Browse training data examples
+- Show nearest neighbors in embedding space
+- Visualize data distribution
+
+### 3. **Live Fine-Tuning**
+- User corrections improve model in real-time
+- Show gradient updates
+- Track loss curves
+
+---
+
+## 📚 Paper References Dashboard
+
+Every feature should link to relevant papers:
+
+```
+┌─────────────────────────────────────────┐
+│  📄 RESEARCH FOUNDATIONS                │
+├─────────────────────────────────────────┤
+│  This feature implements concepts from: │
+│                                         │
+│  [1] "Tree of Thoughts: Deliberate      │
+│       Problem Solving with Large        │
+│       Language Models"                  │
+│       Yao et al., 2023                  │
+│       [PDF] [Code] [Cite]               │
+│                                         │
+│  [2] "Self-Consistency Improves Chain   │
+│       of Thought Reasoning"             │
+│       Wang et al., 2022                 │
+│       [PDF] [Code] [Cite]               │
+│                                         │
+│  📊 Implementation Faithfulness: 87%    │
+└─────────────────────────────────────────┘
+```
+
+---
+
+## 🚀 Implementation Priority
+
+### Phase 1: Core Research Infrastructure (Week 1-2)
+1. ✅ Attention visualization
+2. ✅ RAG pipeline inspector
+3. ✅ Uncertainty quantification
+4. ✅ Paper reference system
+
+### Phase 2: Advanced Reasoning (Week 3-4)
+5. ✅ Tree-of-Thoughts
+6. ✅ Knowledge graph
+7. ✅ Meta-learning adaptation
+8. ✅ Cognitive load estimation
+
+### Phase 3: Safety & Alignment (Week 5)
+9. ✅ Constitutional AI
+10. ✅ Preference learning (DPO)
+11. ✅ Hallucination detection
+
+### Phase 4: Polish & Deploy (Week 6)
+12. ✅ Multimodal support
+13. ✅ Research playground
+14. ✅ Documentation & demos
+
+---
+
+## 🎯 Success Metrics
+
+### For Research Positioning
+- ✓ Cite 15+ recent papers (2020-2024)
+- ✓ Implement 3+ state-of-the-art techniques
+- ✓ Provide interactive visualizations for each
+- ✓ Show rigorous evaluation metrics
+
+### For User Engagement
+- ✓ 10+ interactive research features
+- ✓ Export-quality visualizations
+- ✓ Developer-friendly API
+- ✓ Reproducible experiments
+
+---
+
+## 💡 Unique Value Proposition
+
+**"The only AI tutor that shows its work at the research level"**
+
+- See actual attention patterns (not just outputs)
+- Understand retrieval and reasoning (not black box)
+- Track learning with cognitive science (not just analytics)
+- Reference cutting-edge papers (academic credibility)
+- Experiment with AI techniques (interactive research)
+
+This positions you as a **research lab** that:
+1. Understands the latest AI/ML advances
+2. Implements them rigorously
+3. Makes them accessible and educational
+4. Contributes to interpretability research
+
+---
+
+**Next Steps:** Pick 2-3 features from Phase 1 to prototype first?