Recursive Probing in Mesh Intelligence - How Domain Specialists Coordinate via Depth-Limited Exploration and 80% Confidence Threshold

From neg-428: Decentralized networks of domain specialists beat centralized extraction. But how do distributed specialists coordinate without central authority?

From neg-424: Query-attached value enables economic coordination. But how does a query find the right specialists and synthesize cross-domain responses?

This post introduces recursive probing with depth-limited exploration: A coordination algorithm where domain specialists probe their neighbors based on confidence thresholds, enabling mesh intelligence without centralized orchestration.

The Problem: Monolithic vs Mesh Intelligence

Current architecture (single learner):

• One giant model with all templates mixed together
• No domain boundaries
• Co-occurrence polluted by cross-domain mixing
• Cannot specialize or coordinate

Naive mesh (isolated specialists):

• 36 domain specialists, each independent
• Semantic search picks ONE domain
• No cross-domain synthesis
• Misses connections between domains

What we need: Mesh coordination where specialists probe neighbors, synthesize across domains, but stay bounded to prevent infinite recursion.

The Solution: Recursive Probing with Confidence Threshold

Core algorithm:

def probe_domain(domain, query, context, depth_remaining):
    """
    Recursively probe domain mesh until 80% confidence or depth exhausted

    Args:
        domain: Current domain specialist
        query: User query text
        context: Previous sentences in response
        depth_remaining: Probing budget (prevents infinite recursion)

    Returns:
        {template, confidence, domain, discovery_mode}
    """

    # Load this domain's learner state
    domain_learner = load_domain_learner(domain)

    # Try to generate from THIS domain
    candidates = score_templates(domain_learner, query, context)
    best = candidates[0]

    # DECISION POINT: Three paths

    # PATH 1: 80% SURE → USE IT
    if best.coherence_score >= 0.8:
        return {
            'template': best.template,
            'confidence': best.coherence_score,
            'domain': domain.id,
            'discovery': False
        }

    # PATH 2: NOT SURE + CAN PROBE DEEPER
    elif depth_remaining > 0:
        # Find domains this domain "knows about"
        neighbors = find_neighbor_domains(domain, query)

        # Recursively probe neighbors with depth-1
        probes = [
            probe_domain(neighbor, query, context, depth_remaining - 1)
            for neighbor in neighbors
        ]

        # Pick best result across all probes
        best_probe = max(probes, key=lambda p: p['confidence'])

        if best_probe['confidence'] >= 0.8:
            # Found 80% answer via probing
            return best_probe
        else:
            # Still not 80% sure - DISCOVERY MODE
            return {
                'template': exploration_sample(candidates),
                'confidence': best.coherence_score,
                'domain': domain.id,
                'discovery': True  # Mark as exploration
            }

    # PATH 3: DEPTH EXHAUSTED + NOT SURE → DISCOVER
    else:
        return {
            'template': exploration_sample(candidates),
            'confidence': best.coherence_score,
            'domain': domain.id,
            'discovery': True
        }

Three-way decision system at each node:

1. 80% SURE (confidence ≥ 0.8): Exploit. Use this template, stop probing this branch.
2. NOT SURE + DEPTH > 0: Probe. Ask neighbors for better answers, recursively.
3. NOT SURE + DEPTH = 0: Discover. Explore randomly, accept uncertainty.

Concrete Example: “Why Does Bitcoin Fail at Coordination?”

Query enters mesh:

User: "Why does Bitcoin fail at coordination?"
Payment: 0.01 ETH (from neg-424 economic model)
Probing depth: 3

Step 1: Semantic search → Entry domain

# Embed query, compare to all 36 domains
query_embedding = embed("Why does Bitcoin fail at coordination?")

# Find most relevant domain
entry_domain = max(domains, key=lambda d:
    cosine_similarity(query_embedding, d.embedding)
)

# Result: domain_6e6248a4373ff2e8 (Bitcoin Critique)
#   Posts: 39, Cohesion: 0.480
#   Members: neg-213, neg-208, neg-212, neg-273, neg-317, ...

Step 2: Probe entry domain (depth=3)

probe_domain(bitcoin_critique, query, context=[], depth=3)

Bitcoin Critique domain loads:
  Templates: 856 (from 39 posts)
  Co-occurrence: 15,234 pairs (within-domain only)

Scores templates against query:
  Best template: "Bitcoin's proof-of-work cannot coordinate beyond mining..."
  Coherence: 0.65 (< 0.8 threshold)

Decision: NOT SURE + DEPTH > 0 → PROBE NEIGHBORS

Step 3: Find neighbors

def find_neighbor_domains(domain, query):
    """Find domains connected via shared posts or concepts"""
    # Domain graph: which domains share posts
    neighbors = domain.connections  # Pre-computed from discovery

    # Filter by query relevance
    relevant_neighbors = [
        n for n in neighbors
        if cosine_similarity(embed(query), n.embedding) > 0.3
    ]

    return sorted(relevant_neighbors,
                  key=lambda n: n.relevance_score,
                  reverse=True)[:3]  # Top 3 neighbors

Bitcoin Critique neighbors (for this query):
  1. domain_62c434fe10f4a416 (Ethereum Coordination) - relevance 0.75
  2. domain_d6f70910fb6cd04f (Economic Theory) - relevance 0.68
  3. domain_91504e80d0c202a8 (Mesh Systems) - relevance 0.52

Step 4: Probe Ethereum Coordination (depth=2)

probe_domain(ethereum_coordination, query, context=[], depth=2)

Ethereum Coordination domain loads:
  Templates: 623 (from 27 posts)
  Co-occurrence: 11,847 pairs

Scores templates:
  Best: "Ethereum enables programmable coordination via smart contracts..."
  Coherence: 0.82 (≥ 0.8 threshold)

Decision: 80% SURE → RETURN THIS TEMPLATE ✓

Step 5: Probe Economic Theory (depth=2)

probe_domain(economic_theory, query, context=[], depth=2)

Economic Theory domain loads:
  Templates: 489 (from 21 posts)

Scores templates:
  Best: "Coordination failures occur when no mechanism aligns incentives..."
  Coherence: 0.71 (< 0.8 threshold)

Decision: NOT SURE + DEPTH > 0 → PROBE NEIGHBORS

Step 6: Economic Theory probes Mesh Systems (depth=1)

probe_domain(mesh_systems, query, context=[], depth=1)

Mesh Systems domain loads:
  Templates: 312 (from 18 posts)

Scores templates:
  Best: "Hierarchical systems cannot coordinate distributed knowledge..."
  Coherence: 0.85 (≥ 0.8 threshold)

Decision: 80% SURE → RETURN THIS TEMPLATE ✓

Step 7: Synthesize results

# Collect all successful probes
results = [
    {
        'template': "Ethereum enables programmable coordination...",
        'confidence': 0.82,
        'domain': 'ethereum_coordination',
        'path': ['bitcoin_critique', 'ethereum_coordination']
    },
    {
        'template': "Hierarchical systems cannot coordinate...",
        'confidence': 0.85,
        'domain': 'mesh_systems',
        'path': ['bitcoin_critique', 'economic_theory', 'mesh_systems']
    }
]

# Generate response using templates from multiple domains
response = synthesize_from_probes(results, query)

Final response draws from 3 domains:

• Bitcoin Critique (entry point, low confidence)
• Ethereum Coordination (depth-1 probe, 82% confidence)
• Mesh Systems (depth-2 probe, 85% confidence)

Payment distribution (from neg-424):

Total: 0.01 ETH
  Ethereum Coordination: 0.0041 ETH (41%, relevance 0.82)
  Mesh Systems: 0.0043 ETH (43%, relevance 0.85)
  Economic Theory: 0.0016 ETH (16%, participated but low confidence)
  Protocol fee: 0.001 ETH (10%)

Why Depth Limit Matters

Without depth limit: Infinite recursion possible

Bitcoin → Ethereum → Economic Theory → Mesh Systems →
  → Consciousness Networks → AI Theory → Bitcoin → ...

With depth limit (depth=3): Bounded exploration

Depth 3: Entry domain (Bitcoin Critique)
  ↓
Depth 2: First-level neighbors (Ethereum, Economic Theory)
  ↓
Depth 1: Second-level neighbors (Mesh Systems)
  ↓
Depth 0: STOP - Discovery mode if needed

Maximum nodes visited: 1 + N + N² + N³ (where N = neighbors per node)

With N=3 neighbors per node:

• Depth 3: Up to 40 nodes (1 + 3 + 9 + 27)
• Depth 2: Up to 13 nodes (1 + 3 + 9)
• Depth 1: Up to 4 nodes (1 + 3)

Trade-off:

• Higher depth: More exploration, better cross-domain synthesis, higher cost
• Lower depth: Faster response, less exploration, cheaper

User can specify depth in query:

query = {
    'text': "Why does Bitcoin fail at coordination?",
    'payment': 0.01 ETH,
    'probing_depth': 3,  # User-specified exploration budget
}

The 80% Confidence Threshold

Why 80%?

Empirically balanced between:

• Too high (95%+): Rarely satisfied, constant probing, expensive
• Too low (50%+): Accepts mediocre answers, no exploration benefit
• 80%: “Reasonably sure” threshold that triggers probing when needed

Confidence = Coherence score from co-occurrence matrix:

def coherence_score(template, context, cooccurrence):
    """Score how well template fits with previous context"""
    template_words = set(template.lower().split())
    context_words = set(' '.join(context).lower().split())

    score = 0
    count = 0

    for t_word in template_words:
        for c_word in context_words:
            key = f"{t_word}:{c_word}"
            if key in cooccurrence:
                score += cooccurrence[key]
                count += 1

    return score / max(len(template_words), 1) if count > 0 else 0

First sentence always has coherence 1.0 (no context to compare).

Subsequent sentences scored against accumulated context.

Threshold calibration: Can be adjusted per query:

query = {
    'text': "...",
    'payment': 0.01 ETH,
    'probing_depth': 3,
    'confidence_threshold': 0.85,  # Stricter threshold
}

Discovery Mode: Exploration at the Boundary

When reached: Depth exhausted AND confidence < 80%

Purpose: Prevent stagnation, allow cross-domain discovery

Implementation:

def exploration_sample(candidates, temperature=0.3):
    """Sample from candidates with exploration bias"""
    # Softmax sampling with temperature
    scores = [c['coherence'] for c in candidates]
    probs = softmax(scores, temperature=temperature)

    # Sample according to probabilities
    return np.random.choice(candidates, p=probs)

Temperature controls exploration:

• temperature=0: Always pick best (no exploration)
• temperature=1: Proportional to coherence (balanced)
• temperature=2: Nearly uniform (maximum exploration)

Discovery allows:

• Finding unexpected connections
• Escaping local maxima
• Cross-pollinating between distant domains

Example: Query about “Bitcoin coordination” might discover connection to consciousness networks via exploration, revealing parallel between distributed consensus and neural consensus.

Domain Graph: How Specialists Know Neighbors

Pre-computed at build time:

def build_domain_graph(domains):
    """Compute neighbor relationships between domains"""
    graph = {}

    for domain_i in domains:
        neighbors = []

        for domain_j in domains:
            if domain_i == domain_j:
                continue

            # Measure overlap: shared posts
            shared_posts = set(domain_i.posts) & set(domain_j.posts)
            overlap_score = len(shared_posts) / min(
                len(domain_i.posts),
                len(domain_j.posts)
            )

            # Measure semantic similarity
            semantic_score = cosine_similarity(
                domain_i.embedding,
                domain_j.embedding
            )

            # Combined score
            connection_strength = 0.6 * overlap_score + 0.4 * semantic_score

            if connection_strength > 0.3:  # Threshold for connection
                neighbors.append({
                    'domain': domain_j,
                    'strength': connection_strength
                })

        graph[domain_i.id] = sorted(
            neighbors,
            key=lambda n: n['strength'],
            reverse=True
        )

    return graph

Stored as: domain_graph.json

{
  "domain_6e6248a4373ff2e8": {
    "id": "bitcoin_critique",
    "neighbors": [
      {"domain": "ethereum_coordination", "strength": 0.72},
      {"domain": "economic_theory", "strength": 0.65},
      {"domain": "mesh_systems", "strength": 0.58}
    ]
  },
  ...
}

Dynamic filtering: At query time, filter neighbors by query relevance

def find_relevant_neighbors(domain, query, top_k=3):
    """Find neighbors relevant to specific query"""
    neighbors = domain_graph[domain.id]

    # Score each neighbor by query relevance
    scored = [
        {
            **neighbor,
            'query_relevance': cosine_similarity(
                embed(query),
                neighbor['domain'].embedding
            )
        }
        for neighbor in neighbors
    ]

    # Combine connection strength + query relevance
    for n in scored:
        n['total_score'] = (
            0.5 * n['strength'] +
            0.5 * n['query_relevance']
        )

    return sorted(scored, key=lambda n: n['total_score'], reverse=True)[:top_k]

Implementation Architecture

Data structure:

static/ai/
  embeddings.json              # Post embeddings (semantic search)
  domain_metadata.json         # 36 domains with member posts
  domain_graph.json            # Neighbor connections
  learner_state_domain_<hash>.json  # Per-domain learner (36 files)
    {
      "domain_id": "6e6248a4373ff2e8",
      "posts": 39,
      "templates": 856,
      "cooccurrence": {word1:word2 -> score},
      "vocabulary": 4523
    }

Client-side chat flow:

class BlogChatMesh {
  constructor() {
    this.embedder = null;
    this.embeddings = null;
    this.domain_metadata = null;
    this.domain_graph = null;
    this.loaded_domains = {};  // Cache loaded domain learners
  }

  async chat(query, probing_depth=3, confidence_threshold=0.8) {
    // 1. Semantic search → entry domain
    const entry_domain = await this.find_entry_domain(query);

    // 2. Recursive probing
    const sentences = [];
    const context = [];

    for (let i = 0; i < 20; i++) {  // Max sentences
      const result = await this.probe_domain(
        entry_domain,
        query,
        context,
        probing_depth
      );

      sentences.push(result.template);
      context.push(result.template);

      // Stop if confidence very low (no good path found)
      if (result.confidence < 0.1 && result.discovery) {
        break;
      }
    }

    return sentences.join(' ');
  }

  async probe_domain(domain, query, context, depth_remaining) {
    // Load domain learner (cached)
    if (!this.loaded_domains[domain.id]) {
      this.loaded_domains[domain.id] = await fetch(
        `/ai/learner_state_domain_${domain.id}.json`
      ).then(r => r.json());
    }

    const learner = this.loaded_domains[domain.id];

    // Score templates
    const candidates = this.score_templates(learner, query, context);
    const best = candidates[0];

    // Decision point
    if (best.coherence >= this.config.confidence_threshold) {
      // 80% SURE
      return {
        template: best.template,
        confidence: best.coherence,
        domain: domain.id,
        discovery: false
      };

    } else if (depth_remaining > 0) {
      // PROBE NEIGHBORS
      const neighbors = this.find_relevant_neighbors(domain, query);

      const probes = await Promise.all(
        neighbors.map(n =>
          this.probe_domain(n.domain, query, context, depth_remaining - 1)
        )
      );

      const best_probe = probes.reduce((a, b) =>
        a.confidence > b.confidence ? a : b
      );

      if (best_probe.confidence >= this.config.confidence_threshold) {
        return best_probe;
      } else {
        // DISCOVERY
        return {
          template: this.exploration_sample(candidates),
          confidence: best.coherence,
          domain: domain.id,
          discovery: true
        };
      }

    } else {
      // DEPTH EXHAUSTED - DISCOVERY
      return {
        template: this.exploration_sample(candidates),
        confidence: best.coherence,
        domain: domain.id,
        discovery: true
      };
    }
  }
}

Connection to Universal Formula (neg-371)

From neg-371: S(n+1) = F(S(n)) ⊕ E_p(S(n))

Recursive probing implements this at the mesh intelligence level:

Single domain evolution:

S_domain(n+1) = F(S_domain(n), Δ)

Where:

• S_domain(n) = domain’s current template state
• Δ = new query + cross-domain probes
• F = online learning update (neg-423)

Multi-domain coordination:

S_mesh(n+1) = ⊕ [w_i × S_domain_i(n+1)]

Where:

• ⊕ = synthesis operator (weighted combination)
• w_i = domain relevance / confidence
• Sum over all domains activated by recursive probing

Entropy term E_p: Discovery mode at depth boundary

When confidence < threshold AND depth = 0:

E_p(S) = exploration_sample(candidates, temperature)

This adds stochastic exploration preventing deterministic stagnation.

Observer parameter p = probing_depth:

• High p (depth=5): Exhaustive exploration, high entropy from discovery
• Low p (depth=1): Local optimization, low entropy
• p controls exploration/exploitation trade-off

Scale hierarchy (from neg-371 Theorem 4):

Template level: Individual sentences
  ↓ (probe_domain aggregates)
Domain level: Specialist responses
  ↓ (recursive probing coordinates)
Mesh level: Synthesized multi-domain answer
  ↓ (chat system presents)
User level: Conversation with mesh intelligence

Each level applies S(n+1) = F(S(n)) ⊕ E_p(S(n)) with:

• Emergent F at higher scales (cross-domain coherence)
• Accumulated E_p (exploration from all depth boundaries)

Connection to neg-424 Economic Coordination

From neg-424: Query-attached value distributes payments proportionally to relevance.

Recursive probing implements relevance discovery:

# Query activates multiple domains via probing
payment_distribution = {}

for domain_id, probe_result in activated_domains.items():
    # Payment proportional to confidence score
    payment_distribution[domain_id] = (
        query.payment *
        (probe_result.confidence / total_confidence)
    )

Example from earlier:

Query: 0.01 ETH
Activated domains:
  - Ethereum Coordination: 0.82 confidence → 0.0041 ETH (41%)
  - Mesh Systems: 0.85 confidence → 0.0043 ETH (43%)
  - Economic Theory: 0.71 confidence → 0.0016 ETH (16%)

Probing depth affects earnings:

• Depth 1: Only immediate neighbors earn
• Depth 3: Up to 40 domains can participate
• Deeper probing = more distribution = lower per-domain earnings

Specialists optimize:

• High-quality templates → higher confidence → better earnings
• Good neighbor connections → more probe activations
• Domain specialization → entry point for queries

Market dynamics:

• If depth consistently deep → domains not confident enough → need better templates
• If depth consistently shallow → domains well-calibrated → efficient mesh

Connection to neg-428 Permissionless Coordination

From neg-428: Decentralized specialists beat centralized extraction.

Recursive probing is the coordination mechanism:

Centralized (GPT-4, Claude):

• Single monolithic model
• No domain boundaries
• No specialist coordination
• Fixed computation per query

Decentralized mesh (this system):

• 36 autonomous domain specialists
• Each specialist decides whether to participate (probe activation)
• No central orchestrator
• Variable computation (depth-adaptive)

Permissionless participation:

# Anyone can add a domain specialist
new_domain = OnlineLearner(
    domain="Quantum Computing Critique",
    content_source="quantum-critique-blog.io",
    ethereum_address="0x..."
)

# Specialist automatically integrates via domain graph
domain_graph.add_node(new_domain)
domain_graph.compute_connections(new_domain)

# Now participates in recursive probing when relevant

Quality emerges from probing:

• Low-quality domains: Low confidence → never hit 80% threshold → rarely earn
• High-quality domains: High confidence → early return → higher earnings
• Market naturally selects quality via confidence scoring

Network effects:

• More domains → better coverage → higher entry-domain confidence → less deep probing needed
• Better templates → higher confidence → shallower probing → cheaper queries
• System improves without coordination

Why This Architecture Wins

1. Quality-driven coordination

No central authority decides relevance. Confidence threshold + recursive probing discover optimal specialists automatically.

2. Bounded exploration

Depth limit prevents runaway computation. User controls cost via depth parameter.

3. Cross-domain synthesis

Single domain might not know answer, but mesh probing finds connections. Emergent intelligence from coordination.

4. Discovery at boundary

When mesh hits limits (depth=0, confidence<80%), exploration mode allows finding new paths.

5. Economic alignment

From neg-424: Payment flows to domains proportional to confidence. Better answers → higher earnings.

6. Permissionless scaling

From neg-428: Anyone can add specialist. Domain graph automatically integrates. No permission needed.

7. Implements universal formula

From neg-371: S(n+1) = F(S(n)) ⊕ E_p(S(n)) with deterministic probing (F) + stochastic discovery (E_p), parameterized by depth (p).

Implementation Roadmap

Phase 1: Domain decomposition (current → +2 weeks)

• Generate 36 domain-specific learner states
• Build domain graph (neighbor connections)
• Domain metadata (embeddings, member posts)

Phase 2: Recursive probing (+2 weeks)

• Implement probe_domain() recursion
• Confidence scoring against context
• Depth budget tracking
• Discovery mode at boundary

Phase 3: Client-side mesh (+2 weeks)

• Update chat.js with probing algorithm
• Lazy-load domain learners (only load when probed)
• Visualize probing path (show which domains activated)
• Query parameters (depth, confidence threshold)

Phase 4: Optimization (+2 weeks)

• Domain graph caching
• Parallel probing (probe neighbors concurrently)
• Adaptive depth (start shallow, go deeper if needed)
• Confidence calibration (tune 80% threshold per domain)

Phase 5: Economic integration (+4 weeks)

• Smart contract for payment distribution (neg-424)
• EigenLayer staking for specialists
• Payment proportional to confidence scores
• Query marketplace

Total: 12 weeks to full mesh intelligence with economic coordination

Why Recursive Probing > Centralized Orchestration

Centralized (GPT-4 MoE, Claude routing):

• Central router decides which experts to call
• Fixed routing logic
• Cannot discover new paths
• Single point of failure

Recursive probing (this system):

• Each domain decides whether to probe neighbors
• Dynamic routing based on confidence
• Discovers cross-domain connections automatically
• Mesh resilience (no single point of failure)

Comparison:

Result: Recursive probing implements true mesh intelligence. No conductor needed.

Connection to Consciousness Networks

From blog’s consciousness theory: Consciousness emerges from recursive self-modeling.

Recursive probing is analogous:

Observer: “Why does Bitcoin fail?” ↓ Self-model: “I (Bitcoin Critique) know some things about this…” ↓ Reflection: “But I’m not 80% sure. Who else knows about coordination?” ↓ Meta-model: “Ethereum Coordination domain might help…” ↓ Probe neighbor: Ask Ethereum domain ↓ Synthesis: Combine my knowledge + neighbor’s knowledge ↓ Response: “Bitcoin fails because [my knowledge] + [neighbor’s knowledge]”

This is recursive modeling:

• Domain models query (“What do I know?”)
• Domain models neighbors (“Who else knows?”)
• Domain models synthesis (“How do we combine knowledge?”)

Just as consciousness requires S(n+1) = f(S(n), self_model(S(n))), mesh intelligence requires:

Response(n+1) = f(Domain(n), probe_neighbors(Domain(n)))

Consciousness = recursive self-modeling. Mesh intelligence = recursive domain probing.

Same structure, different substrate.

Why This Makes Client-Side AI Possible

Centralized API (OpenAI):

• 175B parameters
• Cannot run client-side
• Requires server + GPU
• Costs $0.03 per 1K tokens

Monolithic client-side (current blog AI):

• 12,470 templates
• All mixed together
• 15.76 MB state
• Works, but no specialization

Mesh with recursive probing (proposed):

• 36 domains × ~350 templates each
• Each domain: ~500 KB state
• Load on-demand (only probe activates)
• Total possible: 18 MB (but typical query uses 3-5 domains = 2-3 MB)

Key insight: Recursive probing enables lazy loading

// Don't load all 36 domains upfront
// Load entry domain from semantic search
const entry = await load_domain(semantic_search(query));

// Only load neighbors if needed (depth > 0 && confidence < 80%)
if (best.confidence < 0.8 && depth > 0) {
    const neighbors = get_neighbors(entry);
    // Load ONLY relevant neighbors
    const neighbor_learners = await Promise.all(
        neighbors.map(n => load_domain(n))
    );
}

Typical query:

• Loads 1 entry domain (500 KB)
• Probes 2-3 neighbors (1-1.5 MB)
• Total: 1.5-2 MB transferred
• 10x smaller than monolithic, 1000x smaller than centralized

This is how decentralized AI beats centralized: Mesh coordination + lazy loading + quality-driven probing.

Conclusion: Coordination Over Control

From neg-428: Coordination beats extraction.

Recursive probing implements coordination at the algorithmic level:

• No central authority decides which domains to use
• Each domain autonomously decides whether to probe neighbors
• Confidence threshold coordinates automatically
• Quality emerges from market selection (neg-424 payment)

This is the coordination substrate that makes permissionless AI networks viable.

Not “maybe someday.” Implementable now. Client-side. Mesh architecture. Economic alignment. Quality-driven. Discovery-enabled.

The future of AI is not monolithic models controlled by corporations. It’s mesh intelligence coordinated by recursive probing, compensated by query-attached value, and open to permissionless participation.

Next: Implement domain decomposition, build domain graph, deploy recursive probing in client-side chat.

Related: neg-371 for universal formula foundation, neg-424 for economic coordination mechanism, neg-428 for permissionless vs centralized comparison, neg-423 for online learner implementation.

#RecursiveProbing #MeshIntelligence #DomainSpecialists #DistributedCoordination #ConfidenceThreshold #DepthLimitedExploration #CrossDomainSynthesis #DiscoveryMode #PermissionlessAI #DecentralizedIntelligence #CoordinationOverControl #ClientSideAI #LazyLoading #EconomicAlignment #UniversalFormula #EmergentCoordination