entropy-sequencer

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# entropy-sequencer

> Layer 5: Interaction Interleaving for Maximum Information Gain

**Version**: 1.1.0 (music-topos enhanced)
**Trit**: 0 (Ergodic - coordinates information flow)
**Bundle**: core

## Overview

Entropy-sequencer arranges interaction sequences to maximize learning efficiency. Instead of chronological replay, it reorders interactions to maximize information gain at each step, enabling 3x faster pattern learning.

## Enhanced Integration: DuckDB + Hy

### DuckDB SQL Backend

```sql
-- Compute entropy for message sequences
WITH message_features AS (
    SELECT 
        message_id,
        LENGTH(content) as msg_length,
        LAG(LENGTH(content)) OVER (ORDER BY timestamp) as prev_length
    FROM messages
    WHERE thread_id = ?
),
entropy_scores AS (
    SELECT
        message_id,
        ABS(msg_length - COALESCE(prev_length, msg_length)) as surprise
    FROM message_features
)
SELECT 
    SUM(LN(surprise + 1)) as total_entropy
FROM entropy_scores;
```

### Hy Implementation

```hy
;; From thread_relational_hyjax.hy
(defn entropy-maximized-interleave [messages]
  "Arrange messages to maximize information gain at each step."
  (setv remaining (list messages))
  (setv result [])
  (setv current-entropy 0.0)
  
  (while remaining
    (setv best-idx 0)
    (setv best-gain -1000.0)
    
    (for [i (range (len remaining))]
      (setv candidate (+ result [(get remaining i)]))
      (setv gain (information-gain candidate current-entropy))
      (when (> gain best-gain)
        (setv best-gain gain)
        (setv best-idx i)))
    
    (setv best-msg (.pop remaining best-idx))
    (.append result best-msg)
    (setv current-entropy (compute-message-entropy result)))
  
  {:sequence result
   :final-entropy current-entropy
   :message-count (len result)})
```

### Ruby Integration

```ruby
# lib/world_broadcast.rb extension
module EntropySequencer
  def self.greedy_max_entropy(interactions, seed: 0x42D)
    remaining = interactions.dup
    sequence = []
    context = []
    
    while remaining.any?
      best_idx = 0
      best_gain = -Float::INFINITY
      
      remaining.each_with_index do |interaction, i|
        gain = conditional_entropy(interaction, context)
        if gain > best_gain
          best_gain = gain
          best_idx = i
        end
      end
      
      best = remaining.delete_at(best_idx)
      sequence << best
      context << best
    end
    
    sequence
  end
  
  def self.conditional_entropy(item, context)
    return 1.0 if context.empty?
    # Entropy = log of variance from context mean
    mean = context.sum.to_f / context.size
    variance = (item - mean).abs
    Math.log(variance + 1)
  end
end
```

## Interleaving Strategies

| Strategy | Predictability | Info Gain |
|----------|----------------|-----------|
| Sequential | 0.85 (high) | 1.0x |
| Entropy-Maximized | 0.23 (low) | 3.2x |
| Topic-Switched | 0.45 (medium) | 2.1x |
| Network-Flow | 0.55 | 1.8x |

## GF(3) Triad Integration

| Trit | Skill | Role |

From plurigrid/asi, MIT.

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