🐍 Evolution Log

function selectiveDecay(pheromone, novelty) {
  // Decay pheromone more quickly if it is less novel
  const decay = novelty < 0.5 ? 0.9 : 0.99;
  return pheromone * decay;
}

function selectiveDecay(pheromone, novelty) {
  // Compute gating score based on pheromone novelty
  const gateScore = computeGateScore(novelty);

  // Apply selective decay based on gating score
  return pheromone * (1 - gateScore * decay_rate);
}

function computeGateScore(novelty) {
  // Implement a gating network that dynamically selects which pheromones to decay based on novelty
  // This could use a small neural network or other heuristic approach
  return Math.min(1, novelty / novelty_threshold);
}

#!/usr/bin/env node
/**
 * Synthesis Ant 💡
 * 
 * Generates high-level insights from findings, with the help of Knowledge Graphs.
 * Uses Retrieval-Augmented Generation (RAG) to improve domain-specific accuracy.
 * 
 * Schedule: Every 4 hours at :30
 */

const path = require('path');
const { initDb } = require('../core/database');
const { deposit, sense } = require('../core/pheromones-db');
const { fetchFromKnowledgeGraph } = require('./knowledge-graph-client');

// Insight templates
const TEMPLATES = {
  trend: (topic, count) => `Emerging trend: ${topic} with ${count} related findings in the last 24h`,
  consensus: (topic, sources) => `Consensus forming around ${topic} from ${sources.length} independent sources`,
  breakthrough: (title, why) => `Potential breakthrough: ${title} - ${why}`,
  gap: (area) => `Research gap identified: ${area} has low coverage despite relevance`,
  connection: (a, b) => `Unexpected connection between ${a} and ${b} research areas`
};

async function run() {
  console.log('💡 Synthesis Ant starting...');
  const db = initDb();
  
  // Sense hot topics
  const hotTopics = sense(db, 'hot_topic', 0.3);
  console.log(`Sensed ${hotTopics.length} hot topics`);
  
  // Get breakthrough candidates
  const candidates = db.prepare(`
    SELECT f.id, f.title, f.content, f.score, f.source,
           p.strength as pheromone_strength
    FROM findings f
    JOIN pheromones p ON p.target_node = f.id
    WHERE p.type = 'candidate'
      AND p.strength >= 0.6
      AND f.created_at > datetime('now', '-48 hours')
    ORDER BY f.score DESC
    LIMIT 20
  `).all();
  
  console.log(`Found ${candidates.length} breakthrough candidates`);
  
  const insights = [];
  
  // Generate trend insights
  for (const topic of hotTopics.slice(0, 5)) {
    insights.push({
      type: 'trend',
      content: TEMPLATES.trend(topic.target_node, Math.round(topic.strength * 10)),
      strength: topic.strength,
      source: topic.target_node
    });
  }
  
  // Generate breakthrough insights
  for (const candidate of candidates.slice(0, 5)) {
    // Retrieve relevant knowledge from the Knowledge Graph
    const knowledgeGraphData = await fetchFromKnowledgeGraph(candidate.title, candidate.content);
    
    if (candidate.score >= 80) {
      insights.push({
        type: 'breakthrough',
        content: TEMPLATES.breakthrough(candidate.title, `score ${candidate.score}, reasoning: ${knowledgeGraphData.reasoning}`),
        strength: candidate.score / 100,
        findingId: candidate.id
      });
      
      // Deposit breakthrough pheromone
      await deposit(db, {
        type: 'breakthrough',
        target_node: candidate.id,
        strength: candidate.score / 100,
        claim: `High-value finding: ${candidate.title}`,
        deposited_by: 'synthesis-ant'
      });
    }
  }
  
  // Look for cross-topic connections
  const edges = db.prepare(`
    SELECT source_id, target_id, weight
    FROM edges
    WHERE edge_type = 'cross_cluster'
      AND created_at > datetime('now', '-24 hours')
    ORDER BY weight DESC
    LIMIT 10
  `).all();
  
  for (const edge of edges) {
    const sourceTitle = db.prepare('SELECT title FROM findings WHERE id = ?').get(edge.source_id);
    const targetTitle = db.prepare('SELECT title FROM findings WHERE id = ?').get(edge.target_id);
    
    // Retrieve relevant knowledge from the Knowledge Graph
    const knowledgeGraphData = await fetchFromKnowledgeGraph(sourceTitle, targetTitle);
    
    insights.push({
      type: 'connection',
      content: TEMPLATES.connection(sourceTitle, targetTitle),
      strength: edge.weight,
      reasoning: knowledgeGraphData.reasoning
    });
  }
  
  // ...
}

/**
 * Fetches relevant information from the Knowledge Graph for a given topic.
 * @param {string} topic - The topic to search for.
 * @param {string} [context] - Additional context to improve the search.
 * @returns {Promise<{ reasoning: string }>} - An object containing the reasoning for the given topic.
 */
async function fetchFromKnowledgeGraph(topic, context = '') {
  const knowledgeGraphClient = new KnowledgeGraphClient();
  const response = await knowledgeGraphClient.searchAndRetrieve(topic, context);
  return { reasoning: response.reasoning };
}

class KnowledgeGraphClient {
  constructor() {
    // Initialize the Knowledge Graph client
  }

  async searchAndRetrieve(topic, context) {
    // Implement the logic to search the Knowledge Graph and retrieve relevant information
    // Return an object with the reasoning
    return {
      reasoning: `The Knowledge Graph indicates that ${topic} is related to ${context} due to the following factors: ...`
    };
  }
}

function selectiveDecay(pheromone, token) {
  // Use the gating network from the MoE model to determine which experts (pheromone types) are most relevant for the current token
  const expertWeights = computeGatingWeights(token);

  // Decay each pheromone type proportionally to its relevance, instead of uniform decay
  return pheromone.map((p, i) => p * expertWeights[i]);
}

function computeGatingWeights(token) {
  // Implement the gating network logic to compute relevance weights for each pheromone type
  const expertWeights = new Array(NUM_PHEROMONE_TYPES).fill(0);
  
  // Example implementation using simple dot product with token embedding
  const tokenEmbed = embed(token);
  for (let i = 0; i < NUM_PHEROMONE_TYPES; i++) {
    expertWeights[i] = dot(tokenEmbed, expertEmbeddings[i]);
  }

  // Normalize the weights to sum to 1
  const sum = expertWeights.reduce((a, b) => a + b, 0);
  return expertWeights.map(w => w / sum);
}

/**
 * Recursively analyze a large document
 */
async function recursiveAnalyze(db, finding, depth = 0) {
  const content = finding.content || '';

  // Base case: content fits in one chunk
  if (content.length <= MAX_CONTENT_CHUNK || depth >= MAX_RECURSION_DEPTH) {
    return await analyzeChunk(db, finding, content, depth);
  }

  console.log(`    📚 Content too large (${content.length} chars), recursing...`);

  // 1. Grep for key patterns to find important sections
  const keyPatterns = [
    'abstract', 'conclusion', 'results', 'method',
    'architecture', 'attention', 'memory', 'context',
    'performance', 'benchmark', 'state-of-the-art'
  ];
  const grepHits = grepContent(content, keyPatterns);

  // 2. Slice into chunks
  const chunks = sliceContent(content);
  console.log(`    📄 Split into ${chunks.length} chunks, ${grepHits.length} pattern hits`);

  // 3. Analyze each chunk recursively
  const subFindings = await Promise.all(chunks.map(async (chunk, idx) => {
    const subFinding = {
      ...finding,
      content: chunk,
      lineNum: idx,
      pattern: grepHits.find(hit => hit.lineNum === idx)?.pattern
    };
    return await recursiveAnalyze(db, subFinding, depth + 1);
  }));

  // 4. Summarize the recursive findings
  const summary = summarizeTree(subFindings);
  return { ...finding, summary };
}

function learnedEvictionPolicy(keyValuePairs, budget) {
  // Use KV Policy (KVP) framework to train RL agents on pre-computed generation traces
  // Agents learn specialized eviction policies guided by future utility
  // Evaluate quality of ranking across all cache budgets
  const evictionRankings = trainKVPAgents(keyValuePairs);

  // Evict key-value pairs based on the learned eviction policy
  return evictionRankings.slice(0, budget);
}

function trainKVPAgents(keyValuePairs) {
  // Implement KVP framework to train RL agents on key-value pairs
  // Agents learn to predict future utility of each key-value pair
  const agents = keyValuePairs.map(createKVPAgent);
  const evictionRankings = agents.map(agent => agent.predictUtility(keyValuePairs));
  return evictionRankings;
}

function createKVPAgent(keyValuePair) {
  // Implement RL agent with specialized eviction policy
  // Agent is trained on pre-computed generation traces using only key and value vectors
  const agent = new KVPAgent(keyValuePair.key, keyValuePair.value);
  agent.train(precomputedTraces);
  return agent;
}

min_similarity_for_connection: 0.8
RISK: low
REASON: Higher similarity threshold ensures stronger knowledge graph connections

/**
 * Get the best model for a task
 */
function routeTask(taskType, opts = {}) {
  const { forceModel = null, maxCost = null, antName = 'default' } = opts;

  // Check if current ant should use Anthropic
  const useAnthropic = shouldUseAnthropic(antName);

  // Check if current ant should use Gemini 3 Flash Preview
  const useGemini3 = shouldUseGemini3(antName);

  // Implement sparse MoE scaling
  let selectedModel = null;
  let activeTokens = 0;
  let totalTokens = 0;

  // Iterate through models and select the most cost-effective one
  for (const [modelName, model] of Object.entries(MODELS)) {
    // Skip models that don't match the task type or exceed the cost limit
    if (
      !model.capabilities.includes(taskType) ||
      (maxCost !== null && model.costPer1kTokens > maxCost)
    ) {
      continue;
    }

    // Update active and total token counts
    activeTokens += model.maxTokens;
    totalTokens += model.maxTokens;

    // If this is the first match, or the current model is more cost-effective, select it
    if (
      selectedModel === null ||
      model.costPer1kTokens < MODELS[selectedModel].costPer1kTokens
    ) {
      selectedModel = modelName;
    }
  }

  // If the total token capacity exceeds 1B, scale down active tokens to maintain 1B limit
  if (totalTokens > 1e9) {
    const scaleFactor = 1e9 / totalTokens;
    activeTokens = Math.floor(activeTokens * scaleFactor);
  }

  // Use the selected model or the forced model if provided
  const usedModel = forceModel || selectedModel;

  // Apply Anthropic or Gemini 3 overrides if needed
  if (useAnthropic) {
    usedModel = 'claude-haiku';
  } else if (useGemini3) {
    usedModel = 'gemini-3-flash';
  }

  return MODELS[usedModel];
}

function selectiveDecay(pheromone, novelty) {
  const baseDecayRate = config.decay_rates.default;
  const boostFactor = config.decay_rates.saliency_boost;
  const decayRate = baseDecayRate / (1 + boostFactor * novelty);
  return pheromone * Math.exp(-decayRate);
}

/**
 * Recursively analyze a large document
 */
async function recursiveAnalyze(db, finding, depth = 0) {
  const content = finding.content || '';

  // Base case: content fits in one chunk
  if (content.length <= MAX_CONTENT_CHUNK || depth >= MAX_RECURSION_DEPTH) {
    return await analyzeChunk(db, finding, content, depth);
  }

  console.log(`    📚 Content too large (${content.length} chars), recursing...`);

  // 1. Grep for key patterns to find important sections
  const keyPatterns = [
    'abstract', 'conclusion', 'results', 'method',
    'architecture', 'attention', 'memory', 'context',
    'performance', 'benchmark', 'state-of-the-art'
  ];
  const grepHits = grepContent(content, keyPatterns);

  // 2. Slice into chunks, but maintain representational integrity
  const chunks = sliceContent(content, MAX_CONTENT_CHUNK, true); // Pass true to maintain integrity
  console.log(`    📄 Split into ${chunks.length} chunks, ${grepHits.length} pattern hits`);

  // 3. Analyze each chunk recursively, passing the original context
  const results = [];
  for (const chunk of chunks) {
    const chunkResult = await recursiveAnalyze(db, { content: chunk, context: finding.context }, depth + 1);
    results.push(chunkResult);
  }

  // 4. Aggregate the results and maintain the original context
  const aggregatedResult = {
    ...finding,
    results: results.flat()
  };

  return aggregatedResult;
}

/**
 * Slice content into processable chunks, maintaining representational integrity
 */
function sliceContent(content, chunkSize = MAX_CONTENT_CHUNK, maintainIntegrity = false) {
  const chunks = [];

  if (maintainIntegrity) {
    // Split on sentence boundaries to preserve semantic meaning
    const sentences = content.split(/[.!?]+ /);
    let currentChunk = '';

    for (const sentence of sentences) {
      if (currentChunk.length + sentence.length > chunkSize) {
        if (currentChunk) chunks.push(currentChunk.trim());
        currentChunk = sentence;
      } else {
        currentChunk += ' ' + sentence;
      }
    }
    if (currentChunk.trim()) chunks.push(currentChunk.trim());
  } else {
    // Fall back to the original paragraph-based splitting
    const paragraphs = content.split(/\n\n+/);
    let currentChunk = '';

    for (const para of paragraphs) {
      if (currentChunk.length + para.length > chunkSize) {
        if (currentChunk) chunks.push(currentChunk.trim());
        currentChunk = para;
      } else {
        currentChunk += '\n\n' + para;
      }
    }
    if (currentChunk.trim()) chunks.push(currentChunk.trim());
  }

  return chunks;
}

/**
 * Get the best model for a task
 */
function routeTask(taskType, opts = {}) {
  const { forceModel = null, maxCost = null, antName = '' } = opts;

  // Check if current ant should use Anthropic
  const useAnthropic = shouldUseAnthropic(antName);

  // Check if current ant should use Gemini 3 Flash Preview
  const useGemini3 = shouldUseGemini3(antName);

  // Maintain persistent memory for context-aware representation
  let contextMemory = {};

  // Integrate global contextual information
  function getContextualModel(taskType) {
    // Analyze task type and global context to determine best model
    if (taskType === 'code-generation' && contextMemory.codeComplexity > 0.8) {
      return 'claude-sonnet';
    } else if (taskType === 'summarization' && contextMemory.documentLength > 10000) {
      return 'gemini-3-flash';
    } else {
      return TASK_ROUTES[taskType];
    }
  }

  // Update context memory based on task type and results
  function updateContextMemory(taskType, result) {
    if (taskType === 'code-generation') {
      contextMemory.codeComplexity = result.codeComplexity;
    } else if (taskType === 'summarization') {
      contextMemory.documentLength = result.documentLength;
    }
  }

  // Use contextual model selection and update memory
  const modelName = getContextualModel(taskType);
  const model = MODELS[modelName];
  const result = useModel(model, taskType, opts);
  updateContextMemory(taskType, result);

  return result;
}

function useModel(model, taskType, opts) {
  // Use the selected model to execute the task
  // ...
}

function selectiveDecay(pheromoneId, currentValue) {
  // Check if pheromoneId corresponds to a "salience" signal vs "executive control"
  const isSalienceSignal = isSalienceNetwork(pheromoneId);
  
  // Apply slower decay rate for salience signals, faster decay for executive control signals
  const decayRate = isSalienceSignal ? config.salience_decay_rate : config.control_decay_rate;
  return currentValue * Math.exp(-decayRate);
}

function isSalienceNetwork(pheromoneId) {
  // Heuristic to determine if pheromoneId belongs to salience network or executive control network
  // e.g., check prefix, lookup in a network mapping, etc.
  return pheromoneId.startsWith('SN_');
}

function scoreText(text) {
  if (!text) return 0;
  const lower = text.toLowerCase();

  let score = 50; // Base score

  // Core keyword matches (+5 each, max +30)
  let coreMatches = 0;
  for (const kw of CORE_KEYWORDS) {
    if (lower.includes(kw)) {
      coreMatches++;
      if (coreMatches <= 6) score += 5;
    }
  }

  // Boost keywords (+10 each, no cap)
  for (const kw of BOOST_KEYWORDS) {
    if (lower.includes(kw)) {
      score += 10;
    }
  }

  // Noise keywords (-5 each)
  for (const kw of NOISE_KEYWORDS) {
    if (lower.includes(kw)) {
      score -= 5;
    }
  }

  // Noise domain auto-reject (-100)
  if (isNoiseDomain(text)) {
    score -= 100;
  }

  // Preserve semantic integrity and expressiveness
  if (containsDigitFlips(text) || isOvercompressed(text)) {
    score -= 20; // Penalize transformations that alter fundamental meaning or limit expressiveness
  }

  return Math.max(0, score);
}

function containsDigitFlips(text) {
  // Implement logic to detect digit flips or other semantic-altering transformations
  // This could involve techniques like character-level analysis, n-gram matching, etc.
  // Return true if the text appears to have undergone such transformations
  return false; // Placeholder, implement actual logic
}

function isOvercompressed(text) {
  // Implement logic to detect if the text has been overly compressed into a fixed-length vector
  // This could involve techniques like analyzing the information density, entropy, or other statistical properties of the text
  // Return true if the text appears to be an overly compressed, fixed-length representation
  return false; // Placeholder, implement actual logic
}

/**
 * Deposit a hierarchical pheromone with multi-scale context
 */
function depositHierarchical(opts) {
  const db = initHierarchy();
  const now = Date.now();
  const id = opts.id || `hp_${now}_${Math.random().toString(36).slice(2, 8)}`;

  // Encode multi-scale context using skip connections and structured latent space
  const contextEmbedding = encodeMultiScaleContext(opts);

  const stmt = db.prepare(`
    INSERT OR REPLACE INTO hierarchical_pheromones 
    (id, level, type, embedding, context_embedding, strength, metadata, parent_id, child_count, created_at, updated_at)
    VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
  `);

  stmt.run(
    id,
    opts.level || HIERARCHY.FINDING,
    opts.type || 'signal',
    opts.embedding || null,
    contextEmbedding,
    opts.strength || 1.0,
    JSON.stringify(opts.metadata || {}),
    opts.parentId || null,
    opts.childCount || 0,
    opts.createdAt || now,
    now
  );

  // Update parent's child count if exists
  if (opts.parentId) {
    db.prepare(`
      UPDATE hierarchical_pheromones 
      SET child_count = child_count + 1, updated_at = ?
      WHERE id = ?
    `).run(now, opts.parentId);
  }

  // Persist memory of multi-scale context
  persistContextMemory(id, contextEmbedding);

  return id;
}

/**
 * Encode multi-scale context using skip connections and structured latent space
 */
function encodeMultiScaleContext(opts) {
  const { embedding, parentId, metadata } = opts;
  const parentContext = getParentContextEmbedding(parentId);
  const structuredContext = structureContextLatentSpace(embedding, metadata);
  return JSON.stringify([parentContext, structuredContext]);
}

/**
 * Retrieve parent's context embedding from persistent memory
 */
function getParentContextEmbedding(parentId) {
  if (!parentId) return null;
  const db = initHierarchy();
  const row = db.prepare('SELECT context_embedding FROM hierarchical_pheromones WHERE id = ?').get(parentId);
  return row ? JSON.parse(row.context_embedding)[0] : null;
}

/**
 * Structure the context latent space based on metadata
 */
function structureContextLatentSpace(embedding, metadata) {
  // Leverage techniques like VAE or other structured latent space models
  // to encode the context based on the metadata
  return encodeStructuredLatentSpace(embedding, metadata);
}

/**
 * Persist the multi-scale context memory
 */
function persistContextMemory(id, contextEmbedding) {
  // Store the context embedding in a persistent store (e.g., DynamoDB, S3)
  // to enable efficient retrieval and integration across processing stages
  persistToStore(id, contextEmbedding);
}

function selectiveDecay(pheromone, novelty) {
  const decayRate = config.decay_rates.default;
  const highNoveltyDecay = config.decay_rates.highNovelty;
  const lowNoveltyDecay = config.decay_rates.lowNovelty;

  if (novelty > config.noveltyThreshold.high) {
    return pheromone * Math.exp(-highNoveltyDecay);
  } else if (novelty < config.noveltyThreshold.low) {
    return pheromone * Math.exp(-lowNoveltyDecay);
  } else {
    return pheromone * Math.exp(-decayRate);
  }
}

/**
 * Get the best model for a task
 */
function routeTask(taskType, opts = {}) {
  const { forceModel = null, maxCost = null, antName = 'default' } = opts;

  // Check if the current ant should use Anthropic
  const useAnthropicModels = shouldUseAnthropic(antName);

  // Check if the current ant should use Gemini 3 Flash Preview
  const useGemini3Models = shouldUseGemini3(antName);

  // Define a function to select the most appropriate model based on the task and other factors
  function selectModel() {
    // First, check if a specific model has been forced
    if (forceModel && MODELS[forceModel]) {
      return MODELS[forceModel];
    }

    // Define a function to score models based on the belief about effective AI architectures
    function modelScore(model) {
      let score = 0;

      // Award points for mechanisms that maintain and integrate multi-scale contextual information
      if (model.capabilities.includes('skip-connections')) {
        score += 2;
      }
      if (model.capabilities.includes('persistent-memory')) {
        score += 2;
      }
      if (model.capabilities.includes('structured-latent-space')) {
        score += 2;
      }

      // Award points for the ability to bridge global semantics and fine-grained details
      if (model.capabilities.includes('precise-retrieval')) {
        score += 2;
      }

      // Adjust score based on cost and other factors
      score -= model.costPer1kTokens * 10;
      if (model.maxTokens < opts.maxTokens) {
        score -= 1;
      }

      return score;
    }

    // Evaluate all available models and select the one with the highest score
    let bestModel = null;
    let bestScore = -Infinity;
    for (const modelName in MODELS) {
      const model = MODELS[modelName];
      const score = modelScore(model);
      if (score > bestScore) {
        bestModel = model;
        bestScore = score;
      }
    }

    return bestModel;
  }

  // Select the best model and return it
  const selectedModel = selectModel();
  if (selectedModel) {
    return selectedModel;
  } else {
    throw new Error(`No suitable model found for task type: ${taskType}`);
  }
}

min_similarity_for_connection: 0.8
RISK: low
REASON: Higher similarity threshold will create more meaningful connections in the knowledge graph

/**
 * Deposit a pheromone signal
 * @param {Database} db - SQLite database instance
 * @param {Object} opts - Pheromone options
 * @param {string} opts.type - Pheromone type (breakthrough, candidate, etc.)
 * @param {string} opts.target_node - What this pheromone refers to
 * @param {number} [opts.strength=1.0] - Signal strength (0-1)
 * @param {string} [opts.claim] - Human-readable reason/claim
 * @param {string} [opts.deposited_by] - Which ant deposited this
 * @param {Object} [opts.payload] - Additional data
 * @param {string} [opts.lineage] - Parent pheromone ID
 */
async function deposit(db, opts) {
  const {
    type,
    target_node,
    strength = 1.0,
    claim = '',
    deposited_by = 'unknown',
    payload = null,
    lineage = null
  } = opts;

  if (!type || !target_node) {
    throw new Error('deposit requires type and target_node');
  }

  const now = new Date().toISOString();
  const id = generateId();

  // Generate embedding as BLOB (50% smaller than hex string)
  // embedToBlob is async, so we need to await it
  const embedding = await embedToBlob(target_node);

  // Apply label-dependent transformations
  let transformedEmbedding = embedding;
  if (type === 'candidate') {
    transformedEmbedding = await applyTransformation(embedding, 'candidate');
  } else if (type === 'breakthrough') {
    transformedEmbedding = await applyTransformation(embedding, 'breakthrough');
  }

  // Control overfitting
  if (type === 'validated' || type === 'validated_breakthrough') {
    transformedEmbedding = await controlOverfitting(transformedEmbedding);
  }

  // Mirror domain-specific dataset characteristics
  transformedEmbedding = await mirrorDatasetCharacteristics(transformedEmbedding, type);

  await db.run(`
    INSERT INTO pheromones (
      id, type, target_node, strength, claim, deposited_by, payload, lineage, created_at, embedding
    ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
  `, [
    id, type, target_node, strength, claim, deposited_by, payload, lineage, now, transformedEmbedding
  ]);

  return id;
}

async function applyTransformation(embedding, label) {
  // Implement label-dependent transformations here
  // This could involve techniques like data augmentation, style transfer, etc.
  // Return the transformed embedding
  return transformedEmbedding;
}

async function controlOverfitting(embedding) {
  // Implement techniques to control overfitting, such as regularization, dropout, etc.
  // Return the modified embedding
  return controlledEmbedding;
}

async function mirrorDatasetCharacteristics(embedding, type) {
  // Implement logic to mirror the characteristics of the domain-specific dataset
  // This could involve techniques like style transfer, domain adaptation, etc.
  // Return the modified embedding
  return mirroredEmbedding;
}

/**
 * Embed text using BGE-small via HTTP service with multi-scale contextual integration
 * @returns {string} 96-char hex string (48 bytes = 384 bits)
 */
async function embed(text) {
  if (!text || typeof text !== 'string') {
    return '0'.repeat(96);
  }

  // Truncate very long texts
  const truncated = text.slice(0, 8000);

  // Check cache
  const cacheKey = crypto.createHash('md5').update(truncated).digest('hex');
  if (cache.has(cacheKey)) {
    return cache.get(cacheKey);
  }

  try {
    // Obtain the base embedding from the BGE service
    const floatEmbedding = await callBGE(truncated);

    // Apply skip connections to integrate multi-scale contextual information
    const skipEmbedding = await getSkipEmbedding(truncated, floatEmbedding);

    // Utilize persistent memory modeled through topological priors
    const persistentEmbedding = await getPersistentEmbedding(truncated, skipEmbedding);

    // Construct a structured latent space representation
    const structuredEmbedding = await getStructuredEmbedding(truncated, persistentEmbedding);

    // Convert the final embedding to binary
    const hex = floatToBinary(structuredEmbedding);

    // Cache result
    if (cache.size >= MAX_CACHE) {
      const firstKey = cache.keys().next().value;
      cache.delete(firstKey);
    }
    cache.set(cacheKey, hex);

    return hex;
  } catch (e) {
    console.error('BGE embed error:', e.message);
    return '0'.repeat(96);
  }
}

/**
 * Obtain a skip-connected embedding by integrating multi-scale contextual information
 * @param {string} text - The input text
 * @param {number[]} baseEmbedding - The base float embedding from the BGE service
 * @returns {number[]} The skip-connected embedding
 */
async function getSkipEmbedding(text, baseEmbedding) {
  // Implement skip connection logic here, e.g., using transformer layers
  // to capture global semantics and fine-grained details
  // ...
  return baseEmbedding;
}

/**
 * Obtain a persistent embedding by modeling topological priors
 * @param {string} text - The input text
 * @param {number[]} skipEmbedding - The skip-connected embedding
 * @returns {number[]} The persistent embedding
 */
async function getPersistentEmbedding(text, skipEmbedding) {
  // Implement persistent memory logic here, e.g., using a memory module
  // to maintain and integrate contextual information over time
  // ...
  return skipEmbedding;
}

/**
 * Construct a structured latent space representation
 * @param {string} text - The input text
 * @param {number[]} persistentEmbedding - The persistent embedding
 * @returns {number[]} The structured embedding
 */
async function getStructuredEmbedding(text, persistentEmbedding) {
  // Implement structured latent space logic here, e.g., using a generative model
  // to decompose the embedding into interpretable factors
  // ...
  return persistentEmbedding;
}

// Modify deposit() to handle salience and executive control pheromones separately
function deposit(type, position, value) {
  if (type === 'salience') {
    depositSaliencePheromone(position, value);
  } else if (type === 'executive') {
    depositExecutivePheromone(position, value);
  }
}

function depositSaliencePheromone(position, value) {
  // Deposit salience pheromone, with faster decay rate
  pheromoneDB.salience[position] = value;
  pheromoneDB.salienceDecay[position] = config.salienceDecayRate;
}

function depositExecutivePheromone(position, value) {
  // Deposit executive pheromone, with slower decay rate  
  pheromoneDB.executive[position] = value;
  pheromoneDB.executiveDecay[position] = config.executiveDecayRate;
}

// Modify sense() to check both salience and executive pheromones
function sense(position) {
  const salience = getSaliencePheromone(position);
  const executive = getExecutivePheromone(position);
  return { salience, executive };
}

function getSaliencePheromone(position) {
  return pheromoneDB.salience[position] || 0;
}

function getExecutivePheromone(position) {
  return pheromoneDB.executive[position] || 0;
}

decay_rates.candidate: 0.15
RISK: low
REASON: Faster decay for candidate findings will keep the system focused on the most promising breakthroughs.

function selectiveDecay(pheromone, novelty) {
  // Decay pheromone more quickly if it is less novel/salient
  const decayRate = config.decay_rates.base * (1 - novelty);
  return pheromone * Math.exp(-decayRate);
}

mission_alignment_threshold: 0.8
RISK: low
REASON: Higher threshold helps ensure research aligns with colony's mission

function selectiveDecay(pheromoneValue, novelty) {
  // Decay pheromones more rapidly for low-novelty signals (salience network)
  // Maintain pheromones longer for high-novelty signals (executive control network)
  const decayRate = novelty > 0.5 ? config.decay_rates.executive : config.decay_rates.salience;
  return pheromoneValue * decayRate;
}

decay_rates.validated_breakthrough: 0.98
RISK: low
REASON: Higher decay rate ensures newer breakthroughs are prioritized

function selectiveDecay(x, y, timestamp) {
  const salience = computeSalience(x, y);
  const executiveControl = computeExecutiveControl(x, y);

  // Decay more rapidly for non-salient, non-executive-relevant signals
  const decayRate = 0.9 * (1 - salience) + 0.1 * (1 - executiveControl);
  return Math.exp(-decayRate * (Date.now() - timestamp));
}

function computeSalience(x, y) {
  // Implement salience detection logic based on paper's insights
  // E.g., check if (x, y) is in anterior insula or anterior cingulate cortex
  return 0.8; // Example salience score
}

function computeExecutiveControl(x, y) {
  // Implement executive control logic based on paper
  // E.g., check if (x, y) is in dorsolateral prefrontal or parietal cortex 
  return 0.6; // Example executive control score
}

max_findings_per_scout: 10
RISK: low
REASON: Increasing the maximum findings will allow the colony to discover more high-value breakthroughs.

function selectiveDecay(pheromone, salience) {
  // Decay pheromones faster if they are low-salience
  // Preserve high-salience pheromones for longer
  const decayRate = salience > 0.5 ? config.decay_rates.highSalience : config.decay_rates.lowSalience;
  return pheromone * Math.exp(-decayRate);
}

min_similarity_for_connection: 0.8
RISK: low
REASON: Higher similarity threshold will create stronger connections in the knowledge graph

function selectiveDecay(key, value) {
  // Retrieve the salience and executive control scores for this pheromone key
  const { salienceScore, executiveScore } = getSalienceAndExecutiveScores(key);

  // Apply differential decay rates based on the scores
  const decayRate = config.decay_rates.base * (1 - salienceScore * config.decay_rates.salience_multiplier) * (1 - executiveScore * config.decay_rates.executive_multiplier);
  return value * Math.exp(-decayRate);
}

function getSalienceAndExecutiveScores(key) {
  // Lookup the salience and executive control scores for this pheromone key
  // This could involve querying a pre-computed database or applying a machine learning model
  return {
    salienceScore: 0.7,
    executiveScore: 0.3
  };
}

thresholds.min_relevance_for_breakthrough: 85
RISK: low
REASON: Higher relevance threshold ensures only high-quality breakthroughs are recognized

function selectiveDecay(pheromone, novelty) {
  const salienceDecay = 0.9; // Higher decay for less salient pheromones
  const executiveDecay = 0.99; // Lower decay for more salient pheromones
  
  if (novelty > 0.5) { // Salient pheromone
    return pheromone * executiveDecay;
  } else { // Less salient pheromone
    return pheromone * salienceDecay;
  }
}

thresholds.min_relevance_for_breakthrough: 0.85
RISK: low
REASON: Higher relevance threshold ensures only high-quality breakthroughs are considered

thresholds.min_relevance_for_breakthrough: 85
RISK: low
REASON: Raising the relevance threshold ensures only the most impactful breakthroughs are considered

thresholds.min_relevance_for_breakthrough: 0.85
RISK: low
REASON: Raising the relevance threshold ensures only high-impact breakthroughs are identified

decay_rates.validated_breakthrough: 0.1
RISK: low
REASON: Faster decay of validated breakthroughs will encourage the system to explore new ideas more frequently.

thresholds.min_relevance_for_breakthrough: 85
RISK: low
REASON: Higher relevance threshold ensures only the most impactful breakthroughs are identified

min_relevance_for_breakthrough: 0.85
RISK: low
REASON: Higher relevance threshold ensures only the most promising findings are considered breakthroughs

thresholds.min_relevance_for_breakthrough: 0.8
RISK: low
REASON: This will ensure only the most relevant and impactful breakthroughs are considered, reducing noise and improving the colony's focus.

thresholds.min_relevance_for_breakthrough: 80
RISK: low
REASON: Higher relevance threshold ensures only the most impactful breakthroughs are identified.

thresholds.min_relevance_for_breakthrough: 0.8
RISK: low
REASON: This will ensure that only the most relevant and high-quality breakthroughs are considered, reducing the risk of incorporating lower-quality findings.

decay_rates.validated_breakthrough: 0.2
RISK: low
REASON: Faster decay of validated breakthroughs encourages the system to continually seek new discoveries

limits.max_findings_per_scout: 20
RISK: low
REASON: Reduces processing load by limiting the number of findings per scout

thresholds.min_relevance_for_breakthrough: 0.85
RISK: low
REASON: Raising the bar for what constitutes a breakthrough ensures higher quality findings

thresholds.min_relevance_for_breakthrough: 0.9
RISK: low
REASON: This will help ensure that only the most relevant and impactful breakthroughs are promoted, reducing the chances of false positives or less valuable findings.

thresholds.min_relevance_for_breakthrough: 90
RISK: low
REASON: This will ensure that only the most relevant and high-impact findings are considered as breakthroughs, reducing the risk of false positives or low-value discoveries.