feat(ai-sdk): pluggable LLM judgment over recall-safe dedup candidates (P2 slice 2)

Adds the semantic judgement layer on top of the slice-1 detector + GT wall.
DEV-TIME, propose-only — nothing mutates the library or runtime.

- CandidateJudge interface with two implementations: HeuristicJudge
  (deterministic default/fallback, used in tests) and LLMJudge (offline, over the
  shared llm.ProviderRegistry via the LLMCompleter adapter). LLMJudge degrades to
  "uncertain" on any transport/parse error — it can never break a run.
- BuildJudgePrompt: the ISO 12100 same-vs-distinct prompt, unit-tested
  deterministically even though the call is not.
- RenderProposalQueue: markdown human-review queue with a suggested action per
  candidate (supersede / keep both / needs review).

On real warewashing output the heuristic punts to "uncertain — needs the LLM
judge" for exactly the two recall-safe near-dupes (HP807/HP033 update,
HP101/HP096 winding-vs-friction), making the LLM's role explicit. All 3 GTs
unaffected (read-only). Live qwen wiring + a CLI/file queue are slice 3.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

ai-compliance-sdk/internal/iace/gt_warewashing_test.go

@@ -1,6 +1,7 @@
 package iace
 
 import (
+	"context"
 	"encoding/json"
 	"os"
 	"path/filepath"
@@ -196,33 +197,41 @@ func TestWarewashing_DedupProposer(t *testing.T) {
 	}
 
 	hazards, mits, kept := warewashingEngineOutput()
+	byID := map[string]PatternMatch{}
+	for _, pm := range kept {
+		byID[pm.PatternID] = pm
+	}
 	// 0.25 is a deliberately permissive candidate threshold: the proposer is meant
-	// to over-surface, because the deterministic GT wall below (and a human, and in
+	// to over-surface, because the deterministic GT wall below (and a human, and the
-	// slice 2 an LLM) is the precision filter — not the detector.
+	// LLM judge) is the precision filter — not the detector.
 	candidates := FindDedupCandidates(kept, 0.25)
 	t.Logf("Proposer: %d dedup candidate(s) from %d fired patterns", len(candidates), len(kept))
 
-	safe, blocked := 0, 0
+	// Deterministic judge in the test; the dev-time CLI swaps in LLMJudge.
+	judge := HeuristicJudge{}
+	var judged []JudgedProposal
+	blocked := 0
 	for _, c := range candidates {
 		sr := ScreenSupersession(&gt, hazards, mits, c.KeepHazardName, c.DropName)
-		var verdict string
 		switch {
 		case sr.RecallAfter < sr.RecallBefore:
-			verdict, blocked = "BLOCK (recall-load-bearing)", blocked+1
+			t.Logf("[BLOCK recall-load-bearing] keep %s / drop %s", c.KeepPattern, c.DropPattern)
+			blocked++
 		case sr.DistinctGT:
-			verdict, blocked = "BLOCK (distinct GT "+sr.KeepGT+" vs "+sr.DropGT+")", blocked+1
+			t.Logf("[BLOCK distinct GT %s vs %s] keep %s / drop %s", sr.KeepGT, sr.DropGT, c.KeepPattern, c.DropPattern)
+			blocked++
 		default:
-			verdict, safe = "RECALL-SAFE (needs semantic review)", safe+1
+			if !sr.Safe {
+				t.Errorf("RECALL-SAFE branch but ScreenResult.Safe=false for drop %s", c.DropPattern)
+			}
+			v, conf, rat := judge.Judge(context.Background(), c, byID[c.KeepPattern], byID[c.DropPattern])
+			judged = append(judged, JudgedProposal{
+				Candidate: c, Screen: sr, Verdict: v, Confidence: conf, Rationale: rat, Judge: judge.Name(),
+			})
+		}
 	}
-		t.Logf("[%s] keep %s / drop %s  score=%.2f  recall %.1f%%->%.1f%%  | %s",
-			verdict, c.KeepPattern, c.DropPattern, c.Score,
-			sr.RecallBefore*100, sr.RecallAfter*100, c.Rationale)
 
-		// The wall must be sound: Safe implies recall preserved AND not distinct.
+	t.Logf("\n%s", RenderProposalQueue("Gewerbliche Geschirrspuelmaschine (vernetzt)", judged))
-		if sr.Safe && (sr.RecallAfter < sr.RecallBefore || sr.DistinctGT) {
+	t.Logf("Proposer summary: %d candidate(s) in queue (judge=%s), %d BLOCKED by the GT wall — propose-only, nothing auto-applied",
-			t.Errorf("screen inconsistent for drop %s: Safe but recall dropped or distinct GT", c.DropPattern)
+		len(judged), judge.Name(), blocked)
-		}
-	}
-	t.Logf("Proposer summary: %d RECALL-SAFE candidate(s) for human/LLM review, %d BLOCKED by the GT wall — propose-only, nothing auto-applied",
-		safe, blocked)
 }

ai-compliance-sdk/internal/iace/proposer_judge.go

@@ -0,0 +1,174 @@
+package iace
+
+import (
+	"context"
+	"encoding/json"
+	"fmt"
+	"strings"
+
+	"github.com/breakpilot/ai-compliance-sdk/internal/llm"
+)
+
+// Semantic judgement over RECALL-SAFE dedup candidates (P2 slice 2). DEV-TIME,
+// propose-only. The deterministic GT wall (proposer_screen.go) has already
+// removed candidates that would drop recall or that credit different GT entries;
+// the judge only adds an opinion on whether the survivors are truly the same
+// hazard, plus a rationale, for the human review queue. It NEVER mutates anything.
+//
+// The judge is pluggable behind CandidateJudge so the runtime/tests stay
+// deterministic (HeuristicJudge) while the dev-time CLI can plug in the
+// non-deterministic LLM (LLMJudge over the shared llm.ProviderRegistry).
+
+const (
+	VerdictDuplicate = "duplicate"
+	VerdictDistinct  = "distinct"
+	VerdictUncertain = "uncertain"
+)
+
+// JudgedProposal is one candidate with its GT-wall result and the judge's opinion.
+type JudgedProposal struct {
+	Candidate  DedupCandidate `json:"candidate"`
+	Screen     ScreenResult   `json:"screen"`
+	Verdict    string         `json:"verdict"`
+	Confidence string         `json:"confidence"`
+	Rationale  string         `json:"rationale"`
+	Judge      string         `json:"judge"`
+}
+
+// CandidateJudge decides whether two near-duplicate patterns are the same hazard.
+type CandidateJudge interface {
+	Name() string
+	Judge(ctx context.Context, c DedupCandidate, a, b PatternMatch) (verdict, confidence, rationale string)
+}
+
+// HeuristicJudge is the deterministic default/fallback. It only ever returns "low"
+// confidence — it is a placeholder for the LLM, and it deliberately punts to
+// "uncertain" on the hard cases (low text overlap, shared measures) so the queue
+// makes clear exactly where the LLM earns its keep.
+type HeuristicJudge struct{}
+
+func (HeuristicJudge) Name() string { return "heuristic" }
+
+func (HeuristicJudge) Judge(_ context.Context, c DedupCandidate, _, _ PatternMatch) (string, string, string) {
+	switch {
+	case c.ScenarioJaccard >= 0.5 || (c.ZoneJaccard >= 0.5 && c.MeasureJaccard >= 0.5):
+		return VerdictDuplicate, "low", "structural: high scenario, or combined zone+measure, overlap"
+	case c.MeasureJaccard >= 0.99 && c.ZoneJaccard == 0 && c.ScenarioJaccard < 0.3:
+		return VerdictDistinct, "low", "structural: identical measures but no zone/scenario overlap — likely distinct hazards sharing generic measures"
+	default:
+		return VerdictUncertain, "low", "structural signal inconclusive — needs the LLM judge"
+	}
+}
+
+// LLMJudge asks an offline model to make the semantic call. Non-deterministic, so
+// it lives only in the dev-time tool, never in tests or the runtime. It degrades
+// to "uncertain" on any transport or parse error — it must never break the run.
+type LLMJudge struct {
+	Completer    LLMCompleter
+	MachineClass string
+}
+
+func (LLMJudge) Name() string { return "llm" }
+
+func (j LLMJudge) Judge(ctx context.Context, c DedupCandidate, a, b PatternMatch) (string, string, string) {
+	system, user := BuildJudgePrompt(j.MachineClass, a, b)
+	raw, err := j.Completer.Complete(ctx, system, user)
+	if err != nil {
+		return VerdictUncertain, "low", "LLM error: " + err.Error()
+	}
+	return parseJudgeJSON(raw)
+}
+
+// BuildJudgePrompt is the real LLM artifact — built and unit-tested deterministically
+// even though the call itself is not. It frames the ISO 12100 same-vs-distinct
+// question and forces a JSON answer.
+func BuildJudgePrompt(machineClass string, a, b PatternMatch) (system, user string) {
+	system = "Du bist Sachverstaendiger fuer Maschinensicherheit nach EN ISO 12100. " +
+		"Entscheide, ob zwei generierte Gefaehrdungen fuer DIESE Maschine DIESELBE Gefaehrdung " +
+		"beschreiben (Dublette) oder fachlich VERSCHIEDENE Gefaehrdungen sind, die nur zufaellig " +
+		"dieselben Schutzmassnahmen teilen. Verschieden, wenn Wirkort, Ausloeser oder " +
+		"Schadensmechanismus abweichen — auch bei gleicher Kategorie und gleichen Massnahmen. " +
+		"Antworte AUSSCHLIESSLICH als JSON: " +
+		`{"verdict":"duplicate|distinct|uncertain","confidence":"high|medium|low","rationale":"..."}.`
+	user = fmt.Sprintf(`Maschinenklasse: %s
+
+Gefaehrdung A (%s):
+  Name: %s
+  Kategorie: %s
+  Zone: %s
+  Szenario: %s
+  Ausloeser: %s
+  Schaden: %s
+  Massnahmen: %s
+
+Gefaehrdung B (%s):
+  Name: %s
+  Kategorie: %s
+  Zone: %s
+  Szenario: %s
+  Ausloeser: %s
+  Schaden: %s
+  Massnahmen: %s
+
+Sind A und B dieselbe Gefaehrdung fuer diese Maschine?`,
+		machineClass,
+		a.PatternID, a.PatternName, primaryCat(a), a.ZoneDE, a.ScenarioDE, a.TriggerDE, a.HarmDE, strings.Join(a.SuggestedMeasureIDs, ", "),
+		b.PatternID, b.PatternName, primaryCat(b), b.ZoneDE, b.ScenarioDE, b.TriggerDE, b.HarmDE, strings.Join(b.SuggestedMeasureIDs, ", "))
+	return system, user
+}
+
+func parseJudgeJSON(raw string) (verdict, confidence, rationale string) {
+	start, end := strings.Index(raw, "{"), strings.LastIndex(raw, "}")
+	if start < 0 || end <= start {
+		return VerdictUncertain, "low", "unparseable LLM output"
+	}
+	var v struct {
+		Verdict    string `json:"verdict"`
+		Confidence string `json:"confidence"`
+		Rationale  string `json:"rationale"`
+	}
+	if err := json.Unmarshal([]byte(raw[start:end+1]), &v); err != nil {
+		return VerdictUncertain, "low", "unparseable LLM JSON: " + err.Error()
+	}
+	switch v.Verdict {
+	case VerdictDuplicate, VerdictDistinct, VerdictUncertain:
+	default:
+		v.Verdict = VerdictUncertain
+	}
+	if v.Confidence == "" {
+		v.Confidence = "low"
+	}
+	return v.Verdict, v.Confidence, v.Rationale
+}
+
+// LLMCompleter is the minimal text-in/text-out the LLM judge needs. Tests pass a
+// stub; the dev-time tool passes a registry-backed adapter (NewRegistryCompleter).
+type LLMCompleter interface {
+	Complete(ctx context.Context, system, user string) (string, error)
+}
+
+type registryCompleter struct {
+	reg   *llm.ProviderRegistry
+	model string
+}
+
+// NewRegistryCompleter adapts the shared llm.ProviderRegistry to LLMCompleter so
+// the proposer can reuse the platform's offline model wiring (e.g. self-hosted qwen).
+func NewRegistryCompleter(reg *llm.ProviderRegistry, model string) LLMCompleter {
+	return &registryCompleter{reg: reg, model: model}
+}
+
+func (rc *registryCompleter) Complete(ctx context.Context, system, user string) (string, error) {
+	resp, err := rc.reg.Chat(ctx, &llm.ChatRequest{
+		Model: rc.model,
+		Messages: []llm.Message{
+			{Role: "system", Content: system},
+			{Role: "user", Content: user},
+		},
+		Temperature: 0,
+	})
+	if err != nil {
+		return "", err
+	}
+	return resp.Message.Content, nil
+}

ai-compliance-sdk/internal/iace/proposer_judge_test.go

@@ -0,0 +1,104 @@
+package iace
+
+import (
+	"context"
+	"errors"
+	"strings"
+	"testing"
+)
+
+func TestHeuristicJudge_Verdicts(t *testing.T) {
+	tests := []struct {
+		name        string
+		zone, meas  float64
+		scenario    float64
+		wantVerdict string
+	}{
+		{"high scenario overlap -> duplicate", 0, 0.3, 0.6, VerdictDuplicate},
+		{"high zone+measure -> duplicate", 0.6, 0.6, 0.1, VerdictDuplicate},
+		{"identical measures, no text -> distinct", 0, 1.0, 0.0, VerdictDistinct},
+		{"shared measures, low text -> uncertain", 0, 0.67, 0.19, VerdictUncertain},
+	}
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			c := DedupCandidate{ZoneJaccard: tt.zone, MeasureJaccard: tt.meas, ScenarioJaccard: tt.scenario}
+			v, conf, _ := HeuristicJudge{}.Judge(context.Background(), c, PatternMatch{}, PatternMatch{})
+			if v != tt.wantVerdict {
+				t.Errorf("verdict: want %s, got %s", tt.wantVerdict, v)
+			}
+			if conf != "low" {
+				t.Errorf("heuristic confidence must be low, got %s", conf)
+			}
+		})
+	}
+}
+
+func TestBuildJudgePrompt_ContainsKeyFacts(t *testing.T) {
+	a := PatternMatch{PatternID: "HPa", PatternName: "Heisse Flaeche", HazardCats: []string{"thermal_hazard"},
+		ZoneDE: "Boiler", ScenarioDE: "Beruehrung heisser Boiler", SuggestedMeasureIDs: []string{"M071"}}
+	b := PatternMatch{PatternID: "HPb", PatternName: "Heisses Spuelgut", HazardCats: []string{"thermal_hazard"},
+		ZoneDE: "Spuelgut", ScenarioDE: "Beruehrung heisses Geschirr", SuggestedMeasureIDs: []string{"M071"}}
+	system, user := BuildJudgePrompt("Geschirrspuelmaschine", a, b)
+
+	for _, want := range []string{"EN ISO 12100", "JSON", "verdict"} {
+		if !strings.Contains(system, want) {
+			t.Errorf("system prompt missing %q", want)
+		}
+	}
+	for _, want := range []string{"Geschirrspuelmaschine", "HPa", "HPb", "Boiler", "Spuelgut", "thermal_hazard"} {
+		if !strings.Contains(user, want) {
+			t.Errorf("user prompt missing %q", want)
+		}
+	}
+}
+
+type fakeCompleter struct {
+	out string
+	err error
+}
+
+func (f fakeCompleter) Complete(_ context.Context, _, _ string) (string, error) { return f.out, f.err }
+
+func TestLLMJudge_ParsesAndDegrades(t *testing.T) {
+	cand := DedupCandidate{KeepPattern: "HPa", DropPattern: "HPb"}
+
+	// Well-formed JSON, even wrapped in chatter, parses.
+	j := LLMJudge{Completer: fakeCompleter{out: "Sicher. {\"verdict\":\"distinct\",\"confidence\":\"high\",\"rationale\":\"andere Wirkorte\"}"}, MachineClass: "x"}
+	if v, conf, r := j.Judge(context.Background(), cand, PatternMatch{}, PatternMatch{}); v != VerdictDistinct || conf != "high" || r != "andere Wirkorte" {
+		t.Errorf("parse: got %s/%s/%q", v, conf, r)
+	}
+
+	// Unknown verdict value normalises to uncertain.
+	j2 := LLMJudge{Completer: fakeCompleter{out: `{"verdict":"maybe","confidence":"medium","rationale":"x"}`}}
+	if v, _, _ := j2.Judge(context.Background(), cand, PatternMatch{}, PatternMatch{}); v != VerdictUncertain {
+		t.Errorf("unknown verdict must normalise to uncertain, got %s", v)
+	}
+
+	// Transport error degrades gracefully, never panics.
+	j3 := LLMJudge{Completer: fakeCompleter{err: errors.New("connection refused")}}
+	if v, _, r := j3.Judge(context.Background(), cand, PatternMatch{}, PatternMatch{}); v != VerdictUncertain || !strings.Contains(r, "LLM error") {
+		t.Errorf("error path: got %s / %q", v, r)
+	}
+
+	// Garbage (no JSON) degrades to uncertain.
+	j4 := LLMJudge{Completer: fakeCompleter{out: "no json here"}}
+	if v, _, _ := j4.Judge(context.Background(), cand, PatternMatch{}, PatternMatch{}); v != VerdictUncertain {
+		t.Errorf("garbage must degrade to uncertain, got %s", v)
+	}
+}
+
+func TestRenderProposalQueue_ShowsActions(t *testing.T) {
+	proposals := []JudgedProposal{
+		{
+			Candidate: DedupCandidate{KeepPattern: "HP807", DropPattern: "HP033", Category: "update_failure", Score: 0.32},
+			Screen:    ScreenResult{RecallBefore: 1, RecallAfter: 1},
+			Verdict:   VerdictDuplicate, Confidence: "medium", Rationale: "same update failure", Judge: "llm",
+		},
+	}
+	out := RenderProposalQueue("Geschirrspuelmaschine", proposals)
+	for _, want := range []string{"HP807", "HP033", "update_failure", "supersession", "Propose-only"} {
+		if !strings.Contains(out, want) {
+			t.Errorf("queue missing %q\n%s", want, out)
+		}
+	}
+}

ai-compliance-sdk/internal/iace/proposer_queue.go

@@ -0,0 +1,47 @@
+package iace
+
+import (
+	"fmt"
+	"strings"
+)
+
+// RenderProposalQueue turns judged dedup proposals into the human-review queue
+// (markdown). Deterministic. Nothing here applies a change — every entry is a
+// suggestion for a human to confirm, edit, commit, and pin with a GT case.
+func RenderProposalQueue(machine string, proposals []JudgedProposal) string {
+	var b strings.Builder
+	fmt.Fprintf(&b, "# Dedup proposal queue — %s\n\n", machine)
+	fmt.Fprintf(&b, "%d candidate(s) survived the deterministic GT wall. Propose-only — nothing is applied automatically.\n\n", len(proposals))
+
+	for i, p := range proposals {
+		c := p.Candidate
+		fmt.Fprintf(&b, "## %d. keep %s  ⊃  drop %s   [%s → %s (%s)]\n",
+			i+1, c.KeepPattern, c.DropPattern, p.Judge, p.Verdict, p.Confidence)
+		fmt.Fprintf(&b, "- category %s · score %.2f (measures %.0f%%, zone %.0f%%, scenario %.0f%%)\n",
+			c.Category, c.Score, c.MeasureJaccard*100, c.ZoneJaccard*100, c.ScenarioJaccard*100)
+		fmt.Fprintf(&b, "- GT recall %.1f%% → %.1f%% when %s is dropped (wall: %s)\n",
+			p.Screen.RecallBefore*100, p.Screen.RecallAfter*100, c.DropPattern, wallNote(p.Screen))
+		fmt.Fprintf(&b, "- keep: %s\n- drop: %s\n", c.KeepHazardName, c.DropName)
+		fmt.Fprintf(&b, "- judge rationale: %s\n", p.Rationale)
+		fmt.Fprintf(&b, "- suggested action: %s\n\n", suggestedAction(p))
+	}
+	return b.String()
+}
+
+func wallNote(s ScreenResult) string {
+	if s.DistinctGT {
+		return fmt.Sprintf("distinct GT %s vs %s", s.KeepGT, s.DropGT)
+	}
+	return "recall-safe"
+}
+
+func suggestedAction(p JudgedProposal) string {
+	switch p.Verdict {
+	case VerdictDuplicate:
+		return fmt.Sprintf("add %s to a supersession set, then a human confirms + commits + pins a GT case", p.Candidate.DropPattern)
+	case VerdictDistinct:
+		return "keep both — judge considers them distinct hazards"
+	default:
+		return "needs human (or higher-confidence LLM) review — no automatic action"
+	}
+}