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.

{
  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.

{
  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.

{
  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.

{
  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.

{
  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:

// 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

// 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

// 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

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

{
  "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

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Last Updated: 2026-01-24