PRD's 5-loop substrate names "drift" as loop 5: quantify when
historical decisions stop matching current reality. Distinct from
the rating+distillation loop because drift is MEASUREMENT, not
LEARNING. The learning loop says "this match worked, remember it";
the drift loop says "this 4-month-old playbook entry — does it
still match what the substrate would surface today?"
First-shipped drift shape: SCORER drift. When the deterministic
scorer's ScorerVersion bumps, historical ScoredRuns may no longer
match what the current scorer produces on the same EvidenceRecord.
internal/drift/drift.go:
- ScorerDriftInput — (EvidenceRecord, persisted_category) pair
- ScorerDriftEntry — one mismatch with current reasons attached
- CategoryShift — (from, to, count) cell in the shift matrix
- ScorerDriftReport — summary + sorted shift matrix + optional entries
- ComputeScorerDrift(inputs, includeEntries) — pure function;
re-runs ScoreRecord over each input and reports mismatches
Why this matters: without a drift quantifier, a scorer-rule change
silently invalidates the historical training data feeding the
learning loop. With drift quantification, a rule change surfaces
a concrete number ("847 of 4701 historical ScoredRuns now
disagree") that triggers a re-score-and-retrain cycle rather than
letting the substrate quietly rot.
Tests (6/6 PASS):
- No-drift: all 3 inputs match → 100% matched
- Shift detected: 5 inputs, 3 drift cases, drift_rate=0.6,
shift matrix shows accepted→partially_accepted x3
- Multiple shifts sorted by count desc
- includeEntries=false skips the per-mismatch list
- Empty input → all-zero report (no division-by-zero)
- ScorerVersion stamped on every report
Future drift shapes (deferred to follow-ups, named in package doc):
- PLAYBOOK drift: re-run playbook queries through current
matrix-search; recorded answer not in top-K = drift
- EMBEDDING drift: KS-test on vector distribution at T1 vs T2
- AUDIT BASELINE drift: matches Rust audit_baselines.jsonl
longitudinal signal
Pure compute. Materialization layer (read scored-runs jsonl + their
matching evidence jsonl + feed into ComputeScorerDrift) lands with
the distillation materialization commit.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
152 lines
6.1 KiB
Go
152 lines
6.1 KiB
Go
// Package drift quantifies when historical decisions stop matching
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// current reality. Per the PRD's 5-loop substrate, this is loop 5
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// (drift) — distinct from the rating+distillation loop because
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// drift is about MEASUREMENT, not learning. The learning loop says
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// "this match worked, remember it"; the drift loop says "the
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// playbook entry from 4 months ago — does it still match what the
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// substrate would surface today?"
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//
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// First-shipped drift shape: SCORER drift. When the deterministic
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// scorer's logic changes (ScorerVersion bumped), historical
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// ScoredRuns may no longer match what the current scorer would
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// produce on the same EvidenceRecord. ComputeScorerDrift re-runs
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// the current scorer over a slice of (EvidenceRecord, persisted
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// category) pairs and reports mismatches.
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//
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// Why this matters: the rating+distillation loop only learns
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// forward. Without a drift quantifier, a scorer-rule change
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// silently invalidates the historical training data feeding the
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// loop. With drift quantification, a rule change surfaces a
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// concrete number ("847 of 4701 historical scoredruns now
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// disagree") that triggers a re-score-and-retrain cycle rather
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// than letting the substrate quietly rot.
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//
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// Future drift shapes (not in this commit):
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// - PLAYBOOK drift: for each playbook entry, re-run its query
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// through current matrix-search; if the recorded answer is no
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// longer in top-K, the world has moved.
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// - EMBEDDING drift: KS-test on the distribution of embedding
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// vectors at T1 vs T2; large shifts = the corpus has changed
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// materially.
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// - AUDIT BASELINE drift: track how PR audit verdicts shift over
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// scorer/auditor versions; matches the Rust audit_baselines.jsonl
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// longitudinal signal.
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package drift
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import (
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"sort"
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"git.agentview.dev/profit/golangLAKEHOUSE/internal/distillation"
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)
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// ScorerDriftEntry is one mismatch — a historical (record, category)
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// pair where the current scorer disagrees with the persisted
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// verdict. Reasons captures the current scorer's explanation so
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// operators can see WHY the verdict changed.
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type ScorerDriftEntry struct {
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EvidenceRunID string `json:"evidence_run_id"`
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EvidenceTaskID string `json:"evidence_task_id"`
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PersistedCategory distillation.ScoreCategory `json:"persisted_category"`
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CurrentCategory distillation.ScoreCategory `json:"current_category"`
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CurrentReasons []string `json:"current_reasons"`
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SourceFile string `json:"source_file"`
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}
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// CategoryShift is one cell in the drift matrix — "X persisted
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// records that NOW classify as Y." e.g. "12 records that were
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// 'rejected' yesterday are 'partially_accepted' today."
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type CategoryShift struct {
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From distillation.ScoreCategory `json:"from"`
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To distillation.ScoreCategory `json:"to"`
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Count int `json:"count"`
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}
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// ScorerDriftReport is the summary returned by ComputeScorerDrift.
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// The shape is intentionally machine-readable so a downstream
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// dashboard / alerting layer can threshold on Drifted / TotalChecked
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// without parsing the entries list.
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type ScorerDriftReport struct {
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ScorerVersion string `json:"scorer_version"` // current scorer's version
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TotalChecked int `json:"total_checked"`
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Matched int `json:"matched"` // current == persisted
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Drifted int `json:"drifted"` // current != persisted
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DriftRate float64 `json:"drift_rate"` // Drifted / TotalChecked
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ShiftMatrix []CategoryShift `json:"shift_matrix,omitempty"`
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Entries []ScorerDriftEntry `json:"entries,omitempty"` // mismatches only
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}
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// ScorerDriftInput is one (record, persisted_category) pair to check.
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// Caller is responsible for materializing these from disk; this
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// package is pure compute.
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type ScorerDriftInput struct {
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Record distillation.EvidenceRecord
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PersistedCategory distillation.ScoreCategory
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}
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// ComputeScorerDrift re-runs distillation.ScoreRecord over each
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// input and reports mismatches. Pure function — no I/O. The caller
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// supplies the inputs (typically by reading a directory of
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// scored-runs JSONL alongside the corresponding evidence JSONL).
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//
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// IncludeEntries controls whether the per-mismatch detail list is
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// populated. For large corpora (e.g. 4,701 fill events) the
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// summary numbers may be all the caller needs; setting this to
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// false avoids allocating the entries slice.
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func ComputeScorerDrift(inputs []ScorerDriftInput, includeEntries bool) ScorerDriftReport {
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report := ScorerDriftReport{
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ScorerVersion: distillation.ScorerVersion,
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TotalChecked: len(inputs),
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}
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shiftCounts := make(map[[2]distillation.ScoreCategory]int)
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for _, in := range inputs {
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out := distillation.ScoreRecord(in.Record)
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if out.Category == in.PersistedCategory {
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report.Matched++
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continue
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}
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report.Drifted++
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shiftCounts[[2]distillation.ScoreCategory{in.PersistedCategory, out.Category}]++
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if includeEntries {
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report.Entries = append(report.Entries, ScorerDriftEntry{
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EvidenceRunID: in.Record.RunID,
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EvidenceTaskID: in.Record.TaskID,
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PersistedCategory: in.PersistedCategory,
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CurrentCategory: out.Category,
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CurrentReasons: out.Reasons,
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SourceFile: in.Record.Provenance.SourceFile,
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})
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}
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}
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if report.TotalChecked > 0 {
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report.DriftRate = float64(report.Drifted) / float64(report.TotalChecked)
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}
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if len(shiftCounts) > 0 {
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report.ShiftMatrix = make([]CategoryShift, 0, len(shiftCounts))
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for k, v := range shiftCounts {
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report.ShiftMatrix = append(report.ShiftMatrix, CategoryShift{
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From: k[0], To: k[1], Count: v,
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})
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}
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// Sort: largest shifts first, then alphabetical-ish for ties.
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// Stable ordering matters for downstream display and JSON
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// determinism in tests.
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sort.Slice(report.ShiftMatrix, func(i, j int) bool {
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a, b := report.ShiftMatrix[i], report.ShiftMatrix[j]
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if a.Count != b.Count {
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return a.Count > b.Count
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}
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if a.From != b.From {
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return string(a.From) < string(b.From)
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}
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return string(a.To) < string(b.To)
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})
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}
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return report
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}
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