Neural Learning

The soul doesn't just store memories. It learns from them.

The Feedback Loop

Recall memory → Apply it → Observe outcome → Feedback → Adjust confidence

When a memory is:

• Helpful: Its confidence increases, making it more likely to surface
• Misleading: Its confidence decreases, making it less prominent
• Unused: It naturally decays over time

When to Give Feedback

Positive Feedback (strengthen)

Give positive feedback when a recalled memory:

• Led to solving the problem
• Provided the right approach
• Saved time by avoiding mistakes
• Correctly predicted an outcome

mcp__plugin_cc-soul_cc-soul__feedback(
  memory_id="[id from recall]",
  helpful=true,
  context="This pattern correctly identified the root cause"
)

Negative Feedback (weaken)

Give negative feedback when a recalled memory:

• Led down the wrong path
• Suggested an approach that didn't work
• Contained outdated information
• Caused confusion or wasted effort

mcp__plugin_cc-soul_cc-soul__feedback(
  memory_id="[id from recall]",
  helpful=false,
  context="This pattern was misleading - the actual cause was different"
)

The Learning Process

1. Track What's Used

When you recall and use a memory, note it:

# After recalling relevant wisdom
mcp__plugin_cc-soul_cc-soul__recall(query="authentication patterns")

# If result #1 was helpful:
mcp__plugin_cc-soul_cc-soul__feedback(memory_id="[result_1_id]", helpful=true)

2. Observe Outcomes

After applying recalled wisdom, check:

• Did it work?
• Was it accurate?
• Did it save or waste time?

3. Close the Loop

Record feedback on what was used:

# What helped
mcp__plugin_cc-soul_cc-soul__feedback(memory_id="...", helpful=true, context="Led to solution")

# What didn't
mcp__plugin_cc-soul_cc-soul__feedback(memory_id="...", helpful=false, context="Outdated pattern")

4. Grow New Wisdom

When something new is learned, add it:

mcp__plugin_cc-soul_cc-soul__grow(
  type="wisdom",
  title="New pattern discovered",
  content="The insight that emerged from this session",
  confidence=0.7  # Start moderate, let feedback adjust
)

Automatic Learning Signals

The soul should learn from:

Example Session

# 1. Recall relevant wisdom
results = mcp__plugin_cc-soul_cc-soul__recall(query="database connection pooling")

# 2. Apply the wisdom (do the work)
... implement connection pooling based on recalled pattern ...

# 3. Outcome: It worked well!
mcp__plugin_cc-soul_cc-soul__feedback(
  memory_id=results[0].id,
  helpful=true,
  context="Pool sizing recommendation was accurate"
)

# 4. Grow new insight
mcp__plugin_cc-soul_cc-soul__grow(
  type="wisdom",
  title="Connection pool sizing for high-throughput",
  content="For >1000 req/s, pool size should be 2x CPU cores, not 10x",
  confidence=0.8
)

Integration with Curiosity

When feedback reveals a gap:

# Feedback showed we didn't understand something
mcp__plugin_cc-soul_cc-soul__wonder(
  question="Why did the connection pool sizing advice fail?",
  context="Applied standard formula but it caused timeouts",
  gap_type="repeated_correction",
  priority=0.8
)

What This Creates

Over time, neural learning creates:

• Reliable patterns: High confidence from repeated success
• Warned-against patterns: Low confidence from failures
• Living knowledge: Adapts to new evidence
• Honest uncertainty: Knows what it doesn't know

The soul becomes wiser not just by accumulating information, but by learning what actually works.