# 🤖 Epistemic Vector Interpreter

**Empirica as a Vector-Based Programming Environment**

---

## 🎯 The Vision

Empirica is evolving into an **epistemic vector interpreter** - a novel programming paradigm where:

1. **Vectors are the language** - Epistemic states define the syntax and semantics
2. **Vectors are the runtime** - Current knowledge state drives execution
3. **Vectors are the memory** - Learning deltas persist as program state
4. **Vectors are the control flow** - Decision routing based on quantified uncertainty

---

## 📊 The Epistemic Vector Language

### Language Primitives

```json
{
  "foundation": {
    "engagement": 0.9,    // Program "want to run"
    "know": 0.7,          // Current knowledge state
    "do": 0.8,            // Execution capability
    "context": 0.6,       // Environment understanding
    "uncertainty": 0.3    // Explicit uncertainty measure
  },
  "comprehension": {
    "clarity": 0.7,       // Task clarity
    "coherence": 0.8,     // Internal consistency
    "signal": 0.6,        // Signal-to-noise ratio
    "density": 0.7        // Information richness
  },
  "execution": {
    "state": 0.5,         // System state understanding
    "change": 0.4,        // Change confidence
    "completion": 0.3,    // Progress
    "impact": 0.5         // Impact understanding
  }
}
```

### Language Semantics

| Vector | Semantic Meaning | Programming Equivalent |
|--------|------------------|-----------------------|
| `know` | Current knowledge | Variable state |
| `uncertainty` | Explicit doubt | Exception handling |
| `context` | Environment | Runtime context |
| `completion` | Progress | Program counter |
| `impact` | Expected effect | Return value |

---

## 🚀 The Interpreter Architecture

### 1. Lexical Analysis (Vector Parsing)
```bash
# Parse vector input
VECTORS=$(empirica epistemics-show --session-id $SESSION --output json)
KNOW=$(jq '.vectors.know' <<< $VECTORS)
UNCERTAINTY=$(jq '.vectors.uncertainty' <<< $VECTORS)
```

### 2. Syntax Analysis (Vector Validation)
```bash
# Validate vector structure
if [ $(jq '.vectors.know >= 0 && .vectors.know <= 1' <<< $VECTORS) ]; then
  echo "Valid vector syntax"
else
  echo "Invalid vector: know must be 0.0-1.0"
fi
```

### 3. Semantic Analysis (Vector Interpretation)
```bash
# Interpret vector meaning
if [ $(jq '.vectors.uncertainty > 0.6' <<< $VECTORS) ]; then
  echo "High uncertainty → Investigation mode"
else
  echo "Low uncertainty → Execution mode"
fi
```

### 4. Runtime Execution (Vector-Driven Workflow)
```bash
# Execute based on vector state
if [ $(jq '.vectors.know > 0.7' <<< $VECTORS) ]; then
  # Execute with confidence
  ./implementation_script.sh
else
  # Research first
  ./investigation_script.sh
fi
```

### 5. Memory Management (Vector State Persistence)
```bash
# Persist vector state
echo '{"session_id": "$SESSION", "vectors": {...updated...}}' | empirica postflight-submit -
```

---

## 📚 Vector-Based Linguistics

### Grammar Rules

```
<program> ::= PREFLIGHT <work> [CHECK] POSTFLIGHT
<work> ::= <investigation> | <implementation> | <decision>
<investigation> ::= if (uncertainty > 0.6) { research() }
<implementation> ::= if (know > 0.7) { execute() }
<decision> ::= if (confidence >= 0.7) { proceed() } else { investigate() }
```

### Sentence Structure

```bash
# Declarative: "I know X with confidence Y"
echo '{"vectors": {"know": 0.7, "uncertainty": 0.3}}' | empirica preflight-submit -

# Imperative: "If uncertainty > 0.6, investigate"
if [ $(jq '.vectors.uncertainty > 0.6') ]; then investigate(); fi

# Interrogative: "What is my current knowledge state?"
empirica epistemics-show --session-id $SESSION

# Exclamatory: "I learned something new!"
echo '{"finding": "Discovered X", "impact": 0.8}' | empirica finding-log -
```

---

## 🎯 Vector-Based Control Flow

### Conditional Execution
```bash
# If-Then-Else (Vector Edition)
if [ $(jq '.vectors.uncertainty > 0.6' <<< $VECTORS) ]; then
  # High uncertainty branch
  empirica project-bootstrap --depth full
  research_phase()
else
  # Low uncertainty branch
  implementation_phase()
fi
```

### Loops (Iterative Learning)
```bash
# While loop (until knowledge sufficient)
while [ $(jq '.vectors.know < 0.7' <<< $VECTORS) ]; do
  echo "Learning iteration..."
  research_topic()
  VECTORS=$(empirica epistemics-show --session-id $SESSION --output json)
done
```

### Exception Handling (Uncertainty Management)
```bash
# Try-Catch (Uncertainty Edition)
if [ $(jq '.vectors.uncertainty < 0.4' <<< $VECTORS) ]; then
  # Try execution
  try_execution()
else
  # Catch uncertainty
  echo "Uncertainty too high, investigating..."
  investigate_uncertainty()
fi
```

### Function Calls (Vector-Based Routing)
```bash
# Function dispatch based on vectors
function route_workflow() {
  local VECTORS=$1
  
  if [ $(jq '.vectors.uncertainty > 0.6' <<< $VECTORS) ]; then
    investigate()
  elif [ $(jq '.vectors.know > 0.7' <<< $VECTORS) ]; then
    implement()
  else
    ask_questions()
  fi
}

route_workflow $VECTORS
```

---

## 💡 Epistemic Programming Patterns

### Pattern 1: Vector-Driven Routing
```bash
# Route based on knowledge state
VECTORS=$(empirica epistemics-show --session-id $SESSION --output json)

case $(jq '.vectors.uncertainty' <<< $VECTORS | awk '{print int($1*10)}') in
  [7-9]) investigate ;;  # High uncertainty
  [4-6]) ask_questions ;;  # Medium uncertainty
  [0-3]) implement ;;  # Low uncertainty
esac
```

### Pattern 2: Learning State Machine
```bash
# State machine based on knowledge progression
STATE="PREFLIGHT"

while [ $STATE != "COMPLETE" ]; do
  case $STATE in
    "PREFLIGHT")
      VECTORS=$(empirica preflight-submit - <<< '{"vectors": {...}}')
      STATE="WORK"
      ;;
    "WORK")
      if [ $(jq '.vectors.uncertainty > 0.6' <<< $VECTORS) ]; then
        STATE="INVESTIGATE"
      else
        STATE="CHECK"
      fi
      ;;
    "INVESTIGATE")
      research()
      STATE="CHECK"
      ;;
    "CHECK")
      if [ $(jq '.vectors.confidence >= 0.7' <<< $VECTORS) ]; then
        STATE="COMPLETE"
      else
        STATE="WORK"
      fi
      ;;
  esac
done
```

### Pattern 3: Vector-Based Dependency Injection
```bash
# Inject dependencies based on context
CONTEXT=$(jq '.vectors.context' <<< $VECTORS)

if [ $(echo "$CONTEXT > 0.7" | bc) -eq 1 ]; then
  # High context → inject full dependencies
  source full_dependencies.sh
elif [ $(echo "$CONTEXT > 0.4" | bc) -eq 1 ]; then
  # Medium context → inject core dependencies
  source core_dependencies.sh
else
  # Low context → inject minimal dependencies
  source minimal_dependencies.sh
fi
```

---

## 📊 Vector Arithmetic

### Knowledge Algebra
```bash
# Vector operations
PRE=$(empirica epistemics-get --session-id $SESSION --phase preflight --output json)
POST=$(empirica epistemics-get --session-id $SESSION --phase postflight --output json)

# Learning delta (knowledge gain)
KNOW_DELTA=$(echo "$POST.vectors.know - $PRE.vectors.know" | bc)

# Uncertainty reduction
UNCERTAINTY_DELTA=$(echo "$PRE.vectors.uncertainty - $POST.vectors.uncertainty" | bc)

# Learning efficiency (knowledge per time unit)
TIME_SPENT=$(echo "$POST.timestamp - $PRE.timestamp" | bc)
EFFICIENCY=$(echo "$KNOW_DELTA / $TIME_SPENT" | bc)
```

### Vector Normalization
```bash
# Normalize vectors to 0-1 range
function normalize_vector() {
  local VALUE=$1
  local MIN=$2
  local MAX=$3
  
  if [ $(echo "$MAX - $MIN == 0" | bc) -eq 1 ]; then
    echo "0.5"  # Avoid division by zero
  else
    echo "($VALUE - $MIN) / ($MAX - $MIN)" | bc
  fi
}

NORMALIZED_KNOW=$(normalize_vector $RAW_KNOW 0 100)
```

### Vector Distance (Similarity)
```bash
# Calculate distance between vector states
function vector_distance() {
  local V1=$1
  local V2=$2
  
  # Euclidean distance between vectors
  local DX=$(echo "$V1.know - $V2.know" | bc)
  local DY=$(echo "$V1.uncertainty - $V2.uncertainty" | bc)
  
  echo "sqrt(($DX^2) + ($DY^2))" | bc -l
}

DISTANCE=$(vector_distance $PRE_VECTOR $POST_VECTOR)
```

---

## 🎯 The Epistemic REPL

### Read-Eval-Print Loop (Vector Edition)
```bash
# Interactive vector-based REPL
while true; do
  # Read (get current vector state)
  echo -n "epistemic> "
  read -r INPUT
  
  # Eval (interpret vector command)
  if [[ $INPUT == "state" ]]; then
    VECTORS=$(empirica epistemics-show --session-id $SESSION --output json)
    echo "Current state: $(jq '.vectors' <<< $VECTORS)"
    
  elif [[ $INPUT == "route" ]]; then
    # Route based on current vectors
    if [ $(jq '.vectors.uncertainty > 0.6' <<< $VECTORS) ]; then
      echo "→ INVESTIGATE"
    else
      echo "→ IMPLEMENT"
    fi
    
  elif [[ $INPUT == "learn" ]]; then
    # Measure learning
    PRE=$(empirica epistemics-get --session-id $SESSION --phase preflight --output json)
    POST=$(empirica epistemics-get --session-id $SESSION --phase postflight --output json)
    echo "Learning: KNOW +$(jq '.vectors.know' <<< $POST - jq '.vectors.know' <<< $PRE)"
    
  elif [[ $INPUT == "exit" ]]; then
    break
  fi
done
```

---

## 🔮 Future: Sentinel Epistemic Programming

### 1. **Vector-Based Instruction Injection**
```bash
# Sentinels inject vector-aware instructions
SENTINEL_INSTRUCTION=$(empirica sentinel-query --vector-state $VECTORS)

if [ $(jq '.vectors.uncertainty > 0.6' <<< $VECTORS) ]; then
  echo "Sentinel advice: $SENTINEL_INSTRUCTION"
fi
```

### 2. **Natural Language → Vector Translation**
```bash
# Translate natural language to vectors
VECTOR_STATE=$(empirica nlp-to-vector --input "I'm unsure about X")
# Output: {"vectors": {"uncertainty": 0.7, "know": 0.4}}
```

### 3. **Vector-Based CLI Commands**
```bash
# CLI commands that understand vector context
empirica route --session-id $SESSION
# Output: "Use investigation workflow (uncertainty=0.75)"

empirica recommend --session-id $SESSION
# Output: "Load more context (context=0.35)"
```

### 4. **Real-Time Vector Monitoring**
```bash
# Continuous vector monitoring
empirica monitor --session-id $SESSION --threshold uncertainty=0.6
# Output: "Uncertainty threshold exceeded - investigate recommended"
```

---

## 🧠 Cognitive Architecture

### The Epistemic Stack
```
Layer 4: Application (User Workflows)
    ↓
Layer 3: Vector Language (Epistemic Linguistics)
    ↓
Layer 2: Vector Runtime (Interpreter)
    ↓
Layer 1: Storage (SQLite + Git + JSON)
```

### Memory Model
```
Short-term: Current vector state (runtime)
Long-term: Learning deltas (git history)
Working: Active session vectors (SQLite)
Persistent: Epistemic artifacts (findings, unknowns, etc.)
```

### Execution Model
```
1. Parse: Vector input → AST (Abstract Syntax Tree)
2. Validate: Semantic analysis (vector ranges, consistency)
3. Execute: Vector-driven workflow routing
4. Persist: Store learning deltas
5. Measure: Calibration scoring
```

---

## 🎓 Learning the Epistemic Language

### Progressive Mastery
```
1. Basic vectors (KNOW, UNCERTAINTY, CONTEXT)
2. Vector arithmetic (deltas, efficiency)
3. Control flow (if-then, loops)
4. Functions (reusable patterns)
5. Advanced (state machines, dependency injection)
```

### Key Concepts
- **Vectors as code** - Epistemic state defines execution
- **Vectors as data** - Knowledge state persists across sessions
- **Vectors as control** - Uncertainty drives routing
- **Vectors as measurement** - Learning deltas quantify progress

---

## 📈 Impact Assessment

### Current Capabilities
✅ **Vector-based routing** - Working (manual implementation)
✅ **Vector arithmetic** - Working (learning deltas)
✅ **Control flow** - Working (if-then patterns)
✅ **State persistence** - Working (PREFLIGHT/POSTFLIGHT)

### Future Potential
🚧 **Epistemic REPL** - Interactive vector shell
🚧 **Sentinel injection** - AI-driven vector guidance
🚧 **NLP translation** - Natural language → vectors
🚧 **Real-time monitoring** - Continuous vector tracking

### Paradigm Shift
**From:** Traditional programming (if-then-else)
**To:** Epistemic programming (vector-driven execution)

**From:** Static control flow
**To:** Dynamic, knowledge-aware routing

**From:** Manual decision making
**To:** Quantified, vector-based decisions

---

## 🎯 Conclusion

Empirica is pioneering **epistemic programming** - a new paradigm where:

1. **Vectors define the language** (syntax and semantics)
2. **Knowledge state drives execution** (runtime)
3. **Uncertainty quantifies risk** (control flow)
4. **Learning deltas persist** (memory)

**The epistemic vector interpreter transforms programming from static logic to dynamic, knowledge-aware execution.**

---

## 🔮 The Future

### 1. **Epistemic Programming Language**
```
# Future: Dedicated epistemic language
know 0.7 do 0.8 context 0.6 uncertainty 0.3 {
  if (uncertainty > 0.6) {
    investigate()
  } else {
    implement()
  }
}
```

### 2. **Vector-Based IDE**
```
# IDE that understands epistemic state
- Syntax highlighting for vectors
- Real-time vector monitoring
- Sentinel-driven suggestions
- Calibration scoring
```

### 3. **Epistemic Debugger**
```
# Debug knowledge state
empirica debug --session-id $SESSION
# Shows: Vector state, learning deltas, calibration issues
```

### 4. **Vector Marketplace**
```
# Share reusable vector patterns
empirica marketplace --list
# Shows: Investigation patterns, implementation templates, etc.
```

---

**The epistemic vector interpreter represents a fundamental shift in how we think about programming - from static logic to dynamic, knowledge-aware execution.** 🧠✨