ruvnet--ruflo/v3/implementation/adrs/ADR-039-cognitive-kernel-plugin.md

# ADR-039: Cognitive Kernel Plugin

**Status:** Proposed
**Date:** 2026-01-24
**Category:** Cutting-Edge AI
**Author:** Plugin Architecture Team
**Version:** 1.0.0
**Deciders:** Plugin Architecture Team, Cognitive Science Advisors
**Supersedes:** None

## Context

Large Language Models benefit from structured reasoning but often lack persistent cognitive capabilities like working memory, attention control, and meta-cognition. A cognitive kernel can provide these capabilities as a composable layer, enabling more sophisticated reasoning patterns, improved context management, and adaptive learning without modifying the underlying model.

## Decision

Create a **Cognitive Kernel Plugin** that leverages RuVector WASM packages to provide cognitive augmentation for LLMs including working memory management, attention steering, meta-cognitive monitoring, and cognitive load balancing.

## Plugin Name

`@claude-flow/plugin-cognitive-kernel`

## Description

A cutting-edge cognitive augmentation plugin combining the Cognitum Gate Kernel with SONA self-optimizing architecture to provide LLMs with enhanced cognitive capabilities. The plugin enables dynamic working memory, attention control mechanisms, meta-cognitive self-monitoring, and cognitive scaffolding while maintaining low latency through WASM acceleration.

## Key WASM Packages

| Package | Purpose |
|---------|---------|
| `cognitum-gate-kernel` | Core cognitive kernel for memory gating and attention control |
| `sona` | Self-Optimizing Neural Architecture for adaptive cognition |
| `ruvector-attention-wasm` | Multi-head attention for cognitive focus |
| `ruvector-nervous-system-wasm` | Coordination between cognitive subsystems |
| `micro-hnsw-wasm` | Fast retrieval for episodic memory |

## MCP Tools

### 1. `cognition/working-memory`

Manage dynamic working memory for complex reasoning.

```typescript
{
  name: 'cognition/working-memory',
  description: 'Manage working memory slots for complex reasoning tasks',
  inputSchema: {
    type: 'object',
    properties: {
      action: {
        type: 'string',
        enum: ['allocate', 'update', 'retrieve', 'clear', 'consolidate']
      },
      slot: {
        type: 'object',
        properties: {
          id: { type: 'string' },
          content: {},
          priority: { type: 'number', default: 0.5 },
          decay: { type: 'number', default: 0.1 }
        }
      },
      capacity: { type: 'number', default: 7, description: 'Miller number limit' },
      consolidationTarget: { type: 'string', enum: ['episodic', 'semantic', 'procedural'] }
    },
    required: ['action']
  }
}
```

### 2. `cognition/attention-control`

Control cognitive attention and focus.

```typescript
{
  name: 'cognition/attention-control',
  description: 'Control cognitive attention and information filtering',
  inputSchema: {
    type: 'object',
    properties: {
      mode: {
        type: 'string',
        enum: ['focus', 'diffuse', 'selective', 'divided', 'sustained']
      },
      targets: {
        type: 'array',
        items: {
          type: 'object',
          properties: {
            entity: { type: 'string' },
            weight: { type: 'number' },
            duration: { type: 'number' }
          }
        }
      },
      filters: {
        type: 'object',
        properties: {
          includePatterns: { type: 'array', items: { type: 'string' } },
          excludePatterns: { type: 'array', items: { type: 'string' } },
          noveltyBias: { type: 'number', default: 0.5 }
        }
      }
    },
    required: ['mode']
  }
}
```

### 3. `cognition/meta-monitor`

Meta-cognitive self-monitoring and reflection.

```typescript
{
  name: 'cognition/meta-monitor',
  description: 'Meta-cognitive monitoring of reasoning quality',
  inputSchema: {
    type: 'object',
    properties: {
      monitoring: {
        type: 'array',
        items: {
          type: 'string',
          enum: [
            'confidence_calibration', 'reasoning_coherence', 'goal_tracking',
            'cognitive_load', 'error_detection', 'uncertainty_estimation'
          ]
        }
      },
      reflection: {
        type: 'object',
        properties: {
          trigger: { type: 'string', enum: ['periodic', 'on_error', 'on_uncertainty'] },
          depth: { type: 'string', enum: ['shallow', 'medium', 'deep'] }
        }
      },
      interventions: {
        type: 'boolean',
        default: true,
        description: 'Allow automatic corrective interventions'
      }
    }
  }
}
```

### 4. `cognition/scaffold`

Provide cognitive scaffolding for complex tasks.

```typescript
{
  name: 'cognition/scaffold',
  description: 'Provide cognitive scaffolding for complex reasoning',
  inputSchema: {
    type: 'object',
    properties: {
      task: {
        type: 'object',
        properties: {
          description: { type: 'string' },
          complexity: { type: 'string', enum: ['simple', 'moderate', 'complex', 'expert'] },
          domain: { type: 'string' }
        }
      },
      scaffoldType: {
        type: 'string',
        enum: [
          'decomposition', 'analogy', 'worked_example',
          'socratic', 'metacognitive_prompting', 'chain_of_thought'
        ]
      },
      adaptivity: {
        type: 'object',
        properties: {
          fading: { type: 'boolean', default: true },
          monitoring: { type: 'boolean', default: true }
        }
      }
    },
    required: ['task', 'scaffoldType']
  }
}
```

### 5. `cognition/cognitive-load`

Balance and optimize cognitive load.

```typescript
{
  name: 'cognition/cognitive-load',
  description: 'Monitor and balance cognitive load during reasoning',
  inputSchema: {
    type: 'object',
    properties: {
      assessment: {
        type: 'object',
        properties: {
          intrinsic: { type: 'number', description: 'Task complexity (0-1)' },
          extraneous: { type: 'number', description: 'Presentation complexity (0-1)' },
          germane: { type: 'number', description: 'Learning investment (0-1)' }
        }
      },
      optimization: {
        type: 'string',
        enum: ['reduce_extraneous', 'chunk_intrinsic', 'maximize_germane', 'balanced']
      },
      threshold: { type: 'number', default: 0.8, description: 'Max total load before intervention' }
    }
  }
}
```

## Use Cases

1. **Complex Reasoning**: Support multi-step reasoning with working memory
2. **Research Synthesis**: Maintain focus across long document analysis
3. **Learning Enhancement**: Adaptive scaffolding for skill acquisition
4. **Error Prevention**: Meta-cognitive monitoring catches reasoning errors
5. **Context Management**: Intelligent attention control for long contexts

## Architecture

```
+------------------+     +----------------------+     +------------------+
|    LLM Input     |---->|  Cognitive Kernel    |---->|  Enhanced Output |
|   (Prompts)      |     |  (WASM Accelerated)  |     |  (Augmented)     |
+------------------+     +----------------------+     +------------------+
                                   |
              +--------------------+--------------------+
              |                    |                    |
       +------+------+     +-------+-------+    +------+------+
       | Cognitum    |     |    SONA       |    | Attention   |
       | Gate Kernel |     | Self-Optimize |    | Control     |
       +-------------+     +---------------+    +-------------+
              |                    |                    |
              +--------------------+--------------------+
                                   |
                           +-------+-------+
                           | Working Memory |
                           | (HNSW Index)   |
                           +---------------+
```

## Cognitive Subsystems

```
Executive Control
    |
    +-- Attention Control (focus/filter)
    |
    +-- Working Memory (7 +/- 2 slots)
    |       |
    |       +-- Phonological Loop
    |       +-- Visuospatial Sketchpad
    |       +-- Episodic Buffer
    |
    +-- Meta-Cognition (monitoring/reflection)
    |
    +-- Cognitive Load Balancer
```

## Performance Targets

| Metric | Target | Baseline (Traditional) | Improvement |
|--------|--------|------------------------|-------------|
| Working memory operations | <1ms per slot | ~10ms (naive cache) | 10x |
| Attention steering | <5ms for reallocation | ~50ms (context rebuild) | 10x |
| Meta-cognitive check | <10ms per assessment | N/A (not available) | Novel |
| Memory consolidation | <100ms batch | ~1s (full reindex) | 10x |
| Scaffold generation | <50ms per step | N/A (manual prompting) | Novel |

## Security Considerations

### Input Validation (CRITICAL)

All MCP tool inputs MUST be validated using Zod schemas:

```typescript
// cognition/working-memory input validation
const WorkingMemorySchema = z.object({
  action: z.enum(['allocate', 'update', 'retrieve', 'clear', 'consolidate']),
  slot: z.object({
    id: z.string().max(100).optional(),
    content: z.unknown().optional(),
    priority: z.number().min(0).max(1).default(0.5),
    decay: z.number().min(0).max(1).default(0.1)
  }).optional(),
  capacity: z.number().int().min(1).max(20).default(7), // Miller's Law limit
  consolidationTarget: z.enum(['episodic', 'semantic', 'procedural']).optional()
});

// cognition/attention-control input validation
const AttentionControlSchema = z.object({
  mode: z.enum(['focus', 'diffuse', 'selective', 'divided', 'sustained']),
  targets: z.array(z.object({
    entity: z.string().max(500),
    weight: z.number().min(0).max(1),
    duration: z.number().min(0).max(3600) // Max 1 hour
  })).max(50).optional(),
  filters: z.object({
    includePatterns: z.array(z.string().max(200)).max(50).optional(),
    excludePatterns: z.array(z.string().max(200)).max(50).optional(),
    noveltyBias: z.number().min(0).max(1).default(0.5)
  }).optional()
});

// cognition/scaffold input validation
const ScaffoldSchema = z.object({
  task: z.object({
    description: z.string().max(5000),
    complexity: z.enum(['simple', 'moderate', 'complex', 'expert']),
    domain: z.string().max(200).optional()
  }),
  scaffoldType: z.enum([
    'decomposition', 'analogy', 'worked_example',
    'socratic', 'metacognitive_prompting', 'chain_of_thought'
  ]),
  adaptivity: z.object({
    fading: z.boolean().default(true),
    monitoring: z.boolean().default(true)
  }).optional()
});
```

### WASM Security Constraints

| Constraint | Value | Rationale |
|------------|-------|-----------|
| Memory Limit | 256MB max | Cognitive operations are memory-light |
| Working Memory Slots | 20 max | Prevent unbounded memory allocation |
| CPU Time Limit | 10 seconds per operation | Cognitive ops should be fast |
| No External State | All state within WASM sandbox | Isolation |
| Deterministic Operations | Required for reproducibility | Debugging support |

### Cognitive State Security

```typescript
// Working memory may contain sensitive task context
// MUST be properly isolated and cleared

interface CognitiveIsolation {
  sessionId: string;
  workingMemory: EncryptedSlot[];
  accessKey: CryptoKey;      // Session-specific encryption key

  // Clear all cognitive state
  async clearAll(): Promise<void>;

  // Export state (encrypted)
  async export(): Promise<EncryptedState>;

  // Secure deletion
  async secureDelete(): Promise<void>;
}

// Ensure cognitive state doesn't persist unexpectedly
async function endCognitiveSession(isolation: CognitiveIsolation): Promise<void> {
  // Clear working memory
  await isolation.clearAll();

  // Overwrite memory regions
  await isolation.secureDelete();

  // Destroy encryption key
  // (Key is never persisted, only in volatile memory)
}
```

### Identified Security Risks

| Risk ID | Severity | Description | Mitigation |
|---------|----------|-------------|------------|
| COG-SEC-001 | **HIGH** | Sensitive data in working memory | Session isolation, encrypted slots, secure clearing |
| COG-SEC-002 | **MEDIUM** | Meta-cognitive manipulation | Bounds on interventions, audit logging |
| COG-SEC-003 | **MEDIUM** | Attention steering abuse | Rate limiting, mode restrictions |
| COG-SEC-004 | **LOW** | Scaffold injection | Input validation, template sanitization |
| COG-SEC-005 | **LOW** | Cognitive state persistence | Explicit session boundaries, auto-clear |

### Prompt Injection Prevention

```typescript
// Scaffolds and cognitive prompts could be vectors for prompt injection
function sanitizeScaffoldContent(scaffold: string): string {
  // Remove potential prompt injection patterns
  const INJECTION_PATTERNS = [
    /ignore\s+(previous|all)\s+instructions/gi,
    /you\s+are\s+now\s+/gi,
    /system\s*:\s*/gi,
    /\[INST\]/gi,
    /<\|system\|>/gi
  ];

  let sanitized = scaffold;
  for (const pattern of INJECTION_PATTERNS) {
    sanitized = sanitized.replace(pattern, '[FILTERED]');
  }

  return sanitized;
}
```

### Rate Limiting

```typescript
const CognitiveRateLimits = {
  'cognition/working-memory': { requestsPerMinute: 120, maxConcurrent: 10 },
  'cognition/attention-control': { requestsPerMinute: 60, maxConcurrent: 5 },
  'cognition/meta-monitor': { requestsPerMinute: 60, maxConcurrent: 5 },
  'cognition/scaffold': { requestsPerMinute: 30, maxConcurrent: 3 },
  'cognition/cognitive-load': { requestsPerMinute: 60, maxConcurrent: 5 }
};
```

## Risk Assessment

| Risk | Likelihood | Impact | Mitigation |
|------|------------|--------|------------|
| Cognitive overhead latency | Medium | Medium | Bypass for simple tasks, caching |
| Memory slot contention | Low | Low | Priority-based eviction, dynamic capacity |
| Scaffold dependency | Medium | Low | Gradual fading, explicit control |
| Meta-cognition false positives | Medium | Low | Configurable thresholds, manual override |

## Cognitive Theories Implemented

| Theory | Implementation |
|--------|----------------|
| Baddeley's Working Memory | Multi-component memory system |
| Cognitive Load Theory | Intrinsic/extraneous/germane load management |
| Metacognition | Self-monitoring and regulation |
| Zone of Proximal Development | Adaptive scaffolding with fading |
| Dual Process Theory | Fast/slow thinking modes |

## Implementation Notes

### Phase 1: Core Kernel
- Cognitum Gate Kernel integration
- Basic working memory slots
- Simple attention control

### Phase 2: Self-Optimization
- SONA integration for adaptation
- Meta-cognitive monitoring
- Cognitive load assessment

### Phase 3: Advanced Features
- Scaffolding system
- Long-term memory consolidation
- Multi-modal cognitive support

## Dependencies

```json
{
  "dependencies": {
    "cognitum-gate-kernel": "^0.1.0",
    "sona": "^0.1.0",
    "ruvector-attention-wasm": "^0.1.0",
    "ruvector-nervous-system-wasm": "^0.1.0",
    "micro-hnsw-wasm": "^0.2.0"
  }
}
```

## Consequences

### Positive
- Dramatically improved reasoning for complex tasks
- Reduced cognitive errors through meta-monitoring
- Adaptive support based on task demands

### Negative
- Additional latency for cognitive processing
- Complexity in debugging cognitive interventions
- Requires tuning for different domains

### Neutral
- Can operate transparently or with explicit control

## Related ADRs

| ADR | Relationship |
|-----|--------------|
| ADR-004: Plugin Architecture | Foundation - Defines plugin structure |
| ADR-017: RuVector Integration | Dependency - Provides WASM packages |
| ADR-038: Neural Coordination | Related - Multi-agent cognitive layer |
| ADR-037: Performance Optimizer | Related - Cognitive load metrics |
| ADR-041: Hyperbolic Reasoning | Related - Concept hierarchy in memory |

## References

- Baddeley's Working Memory Model: https://www.simplypsychology.org/working-memory.html
- Cognitive Load Theory: https://www.tandfonline.com/doi/abs/10.1207/s15516709cog1202_4
- ADR-017: RuVector Integration
- ADR-004: Plugin Architecture

---

**Last Updated:** 2026-01-24