learning-systems

Learning Systems

The swarm plugin learns from task outcomes to improve decomposition quality over time. Three interconnected systems track pattern effectiveness: implicit feedback scoring, confidence decay, and pattern maturity progression.

Implicit Feedback Scoring

Convert task outcomes into learning signals without explicit user feedback.

What Gets Scored

Duration signals:

• Fast (<5 min) = helpful (1.0)
• Medium (5-30 min) = neutral (0.6)
• Slow (>30 min) = harmful (0.2)

Error signals:

• 0 errors = helpful (1.0)
• 1-2 errors = neutral (0.6)
• 3+ errors = harmful (0.2)

Retry signals:

• 0 retries = helpful (1.0)
• 1 retry = neutral (0.7)
• 2+ retries = harmful (0.3)

Success signal:

• Success = 1.0 (40% weight)
• Failure = 0.0

Weighted Score Calculation

rawScore = success * 0.4 + duration * 0.2 + errors * 0.2 + retries * 0.2;

Thresholds:

• rawScore >= 0.7 → helpful
• rawScore <= 0.4 → harmful
• 0.4 < rawScore < 0.7 → neutral

Recording Outcomes

Call swarm_record_outcome after subtask completion:

swarm_record_outcome({
  bead_id: "bd-123.1",
  duration_ms: 180000, // 3 minutes
  error_count: 0,
  retry_count: 0,
  success: true,
  files_touched: ["src/auth.ts"],
  strategy: "file-based",
});

Fields tracked:

• bead_id - subtask identifier
• duration_ms - time from start to completion
• error_count - errors encountered (from ErrorAccumulator)
• retry_count - number of retry attempts
• success - whether subtask completed successfully
• files_touched - modified file paths
• strategy - decomposition strategy used (optional)
• failure_mode - classification if success=false (optional)
• failure_details - error context (optional)

Confidence Decay

Evaluation criteria weights fade unless revalidated. Prevents stale patterns from dominating future decompositions.

Half-Life Formula

decayed_value = raw_value * 0.5^(age_days / 90)

Decay timeline:

• Day 0: 100% weight
• Day 90: 50% weight
• Day 180: 25% weight
• Day 270: 12.5% weight

Criterion Weight Calculation

Aggregate decayed feedback events:

helpfulSum = sum(helpful_events.map((e) => e.raw_value * decay(e.timestamp)));
harmfulSum = sum(harmful_events.map((e) => e.raw_value * decay(e.timestamp)));
weight = max(0.1, helpfulSum / (helpfulSum + harmfulSum));

Weight floor: minimum 0.1 prevents complete zeroing

Revalidation

Recording new feedback resets decay timer for that criterion:

{
  criterion: "type_safe",
  weight: 0.85,
  helpful_count: 12,
  harmful_count: 3,
  last_validated: "2024-12-12T00:00:00Z",  // Reset on new feedback
  half_life_days: 90,
}

When Criteria Get Deprecated

total = helpful_count + harmful_count;
harmfulRatio = harmful_count / total;

if (total >= 3 && harmfulRatio > 0.3) {
  // Deprecate criterion - reduce impact to 0
}

Pattern Maturity States

Patterns progress through lifecycle based on feedback accumulation:

candidate → established → proven (or deprecated)

State Transitions

candidate (initial state):

• Total feedback < 3 events
• Not enough data to judge
• Multiplier: 0.5x

established:

• Total feedback >= 3 events
• Has track record but not proven
• Multiplier: 1.0x

proven:

• Decayed helpful >= 5 AND
• Harmful ratio < 15%
• Multiplier: 1.5x

deprecated:

• Harmful ratio > 30% AND
• Total feedback >= 3 events
• Multiplier: 0x (excluded)

Decay Applied to State Calculation

State determination uses decayed counts, not raw counts:

const { decayedHelpful, decayedHarmful } =
  calculateDecayedCounts(feedbackEvents);
const total = decayedHelpful + decayedHarmful;
const harmfulRatio = decayedHarmful / total;

// State logic applies to decayed values

Old feedback matters less. Pattern must maintain recent positive signal to stay proven.

Manual State Changes

Promote to proven:

promotePattern(maturity); // External validation confirms effectiveness

Deprecate:

deprecatePattern(maturity, "Causes file conflicts in 80% of cases");

Cannot promote deprecated patterns. Must reset.

Multipliers in Decomposition

Apply maturity multiplier to pattern scores:

const multipliers = {
  candidate: 0.5,
  established: 1.0,
  proven: 1.5,
  deprecated: 0,
};

pattern_score = base_score * multipliers[maturity.state];

Proven patterns get 50% boost, deprecated patterns excluded entirely.

Anti-Pattern Inversion

Failed patterns auto-convert to anti-patterns at >60% failure rate.

Inversion Threshold

const total = pattern.success_count + pattern.failure_count;

if (total >= 3 && pattern.failure_count / total >= 0.6) {
  invertToAntiPattern(pattern, reason);
}

Minimum observations: 3 total (prevents hasty inversion) Failure ratio: 60% (3+ failures in 5 attempts)

Inversion Process

Original pattern:

{
  id: "pattern-123",
  content: "Split by file type",
  kind: "pattern",
  is_negative: false,
  success_count: 2,
  failure_count: 5,
}

Inverted anti-pattern:

{
  id: "anti-pattern-123",
  content: "AVOID: Split by file type. Failed 5/7 times (71% failure rate)",
  kind: "anti_pattern",
  is_negative: true,
  success_count: 2,
  failure_count: 5,
  reason: "Failed 5/7 times (71% failure rate)",
}

Recording Observations

Track pattern outcomes to accumulate success/failure counts:

recordPatternObservation(
  pattern,
  success: true,  // or false
  beadId: "bd-123.1",
)

// Returns:
{
  pattern: updatedPattern,
  inversion?: {
    original: pattern,
    inverted: antiPattern,
    reason: "Failed 5/7 times (71% failure rate)",
  }
}

Pattern Extraction

Auto-detect strategies from decomposition descriptions:

extractPatternsFromDescription(
  "We'll split by file type, one file per subtask",
);

// Returns: ["Split by file type", "One file per subtask"]

Detected strategies:

• Split by file type
• Split by component
• Split by layer (UI/logic/data)
• Split by feature
• One file per subtask
• Handle shared types first
• Separate API routes
• Tests alongside implementation
• Tests in separate subtask
• Maximize parallelization
• Sequential execution order
• Respect dependency chain

Using Anti-Patterns in Prompts

Format for decomposition prompt inclusion:

formatAntiPatternsForPrompt(patterns);

Output:

## Anti-Patterns to Avoid

Based on past failures, avoid these decomposition strategies:

- AVOID: Split by file type. Failed 12/15 times (80% failure rate)
- AVOID: One file per subtask. Failed 8/10 times (80% failure rate)

Error Accumulator

Track errors during subtask execution for retry prompts and outcome scoring.

Error Types

type ErrorType =
  | "validation" // Schema/type errors
  | "timeout" // Task exceeded time limit
  | "conflict" // File reservation conflicts
  | "tool_failure" // Tool invocation failed
  | "unknown"; // Unclassified

Recording Errors

errorAccumulator.recordError(
  beadId: "bd-123.1",
  errorType: "validation",
  message: "Type error in src/auth.ts",
  options: {
    stack_trace: "...",
    tool_name: "typecheck",
    context: "After adding OAuth types",
  }
)

Generating Error Context

Format accumulated errors for retry prompts:

const context = await errorAccumulator.getErrorContext(
  beadId: "bd-123.1",
  includeResolved: false,
)

Output:

## Previous Errors

The following errors were encountered during execution:

### validation (2 errors)

- **Type error in src/auth.ts**
  - Context: After adding OAuth types
  - Tool: typecheck
  - Time: 12/12/2024, 10:30 AM

- **Missing import in src/session.ts**
  - Tool: typecheck
  - Time: 12/12/2024, 10:35 AM

**Action Required**: Address these errors before proceeding. Consider:

- What caused each error?
- How can you prevent similar errors?
- Are there patterns across error types?

Resolving Errors

Mark errors resolved after fixing:

await errorAccumulator.resolveError(errorId);

Resolved errors excluded from retry context by default.

Error Statistics

Get error counts for outcome tracking:

const stats = await errorAccumulator.getErrorStats("bd-123.1")

// Returns:
{
  total: 5,
  unresolved: 2,
  by_type: {
    validation: 3,
    timeout: 1,
    tool_failure: 1,
  }
}

Use total for error_count in outcome signals.

Using the Learning System

Integration Points

1. During decomposition (swarm_plan_prompt):

• Query CASS for similar tasks
• Load pattern maturity records
• Include proven patterns in prompt
• Exclude deprecated patterns

2. During execution:

• ErrorAccumulator tracks errors
• Record retry attempts
• Track duration from start to completion

3. After completion (swarm_complete):

• Record outcome signals
• Score implicit feedback
• Update pattern observations
• Check for anti-pattern inversions
• Update maturity states

Full Workflow Example

// 1. Decomposition phase
const cass_results = cass_search({ query: "user authentication", limit: 5 });
const patterns = loadPatterns(); // Get maturity records
const prompt = swarm_plan_prompt({
  task: "Add OAuth",
  context: formatPatternsWithMaturityForPrompt(patterns),
  query_cass: true,
});

// 2. Execution phase
const errorAccumulator = new ErrorAccumulator();
const startTime = Date.now();

try {
  // Work happens...
  await implement_subtask();
} catch (error) {
  await errorAccumulator.recordError(
    bead_id,
    classifyError(error),
    error.message,
  );
  retryCount++;
}

// 3. Completion phase
const duration = Date.now() - startTime;
const errorStats = await errorAccumulator.getErrorStats(bead_id);

swarm_record_out

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