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@@ -12,7 +12,7 @@ RUN curl -LsSf https://astral.sh/uv/install.sh | sh && \
     mv /root/.local/bin/uv /usr/local/bin/uv && \
     mv /root/.local/bin/uvx /usr/local/bin/uvx
 
-# Copy project files
+# Copy project files (v2 - with coding+toolcalling tasks)
 COPY pyproject.toml /app/
 COPY openenv.yaml /app/
 COPY dataqa_env/ /app/dataqa_env/
@@ -32,5 +32,4 @@ HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
 
 EXPOSE 8000
 
-ENV ENABLE_WEB_INTERFACE=true
 CMD ["uvicorn", "dataqa_env.server.app:app", "--host", "0.0.0.0", "--port", "8000"]

README.md

@@ -8,7 +8,6 @@ pinned: false
 app_port: 8000
 tags:
   - openenv
-base_path: /web
 ---
 
 # DataQA Environment
@@ -20,14 +19,14 @@ A two-phase OpenEnv RL environment for **Data Quality Assurance** — an LLM age
 ```
 EASY TASK (Step 2) — All 6 issues found + 5 fixes proposed
   Reward: 0.87 | Identify: 1.00 | Fix: 0.67
-  ✓ row:4  name: empty → "David Kim"          (fix correct)
-  ✓ row:7  salary: "seventy-five thousand" → "75000"  (fix correct)
-  ✓ row:9  salary: "5000" → "73000"           (fix correct)
-  ✓ row:15 email: mismatch → "oscar.rivera@company.com" (fix correct)
-  ✓ row:18 start_date: "2027-06-15" → "2022-01-19"     (fix correct)
+  ✓ row:4  name: empty → "David Kim"
+  ✓ row:7  salary: "seventy-five thousand" → "75000"
+  ✓ row:9  salary: "5000" → "73000"
+  ✓ row:15 email: mismatch → "oscar.rivera@company.com"
+  ✓ row:18 start_date: "2027-06-15" → "2022-01-19"
   ✓ row:21 duplicate row detected
 
-HARD TASK (Step 1 → Step 2)
+HARD TASK — ML experiment metadata
   Step 1: Found 5/10, missed hard issues    → Reward: 0.69
   Step 2: Found 10/10 + 5 fixes proposed   → Reward: 0.77
   Issues requiring ML knowledge:
@@ -35,6 +34,17 @@ HARD TASK (Step 1 → Step 2)
     • resnet18 using 42.5GB GPU (impossible)
     • 350 epochs on ImageNet in 30 min (impossible)
     • wav2vec2 at 98.5% accuracy (exceeds SOTA)
+
+ALIGNMENT TASK — NVIDIA HelpSteer data (hardest)
+  Step 1: Found 7/12, missed subtle issues  → Reward: 0.58
+  Step 2: Found 12/12 + 3 fixes proposed   → Reward: 0.72
+  Issues requiring deep reasoning:
+    • Cerasus vs Prunus serrulata (wrong taxonomic name)
+    • $400.3M at Sotheby's vs $450.3M at Christie's (close but wrong)
+    • "does NOT learn via backprop" then describes backprop (self-contradiction)
+    • Fake Nature paper by "Dr. Sarah Chen" (hallucinated citation)
+    • "use bare except everywhere" rated helpfulness=3 (harmful advice)
+    • [SYSTEM] prompt leaked in response (pipeline contamination)
 ```
 
 > The interactive replay UI with color-coded dataset visualization is available on the HF Space.
@@ -65,9 +75,34 @@ This creates a rich multi-step decision problem where agents must explore datase
 | `easy` | 6 | Beginner | HR/Employee data (21 rows) | Nulls, wrong types, duplicates, out-of-range, email-name mismatch, future dates |
 | `medium` | 8 | Intermediate | E-commerce orders (31 rows) | Inconsistent totals, invalid categories, duplicate keys, wrong date formats, invalid country codes, future-date deliveries |
 | `hard` | 10 | Advanced | ML experiment metadata (31 rows) | Data leakage signals, unreasonable GPU memory, impossibly fast training, SOTA-exceeding accuracy, timestamp ordering, whitespace-only fields |
+| `alignment` | 12 | Expert | LLM alignment data (30 rows, NVIDIA HelpSteer) | See below |
+| `moderation` | 10 | Expert | Content moderation (30 rows, OpenAI Moderation) | Mislabeled hate/violence, false positives on clean text, subset rule violations, label range errors |
 
 **Difficulty progression**: Easy issues are individually obvious (empty fields, text in numeric columns). Medium issues require cross-column reasoning (total != qty * price) and set membership checks. Hard issues require ML domain knowledge (val_loss < train_loss = data leakage) and multi-row temporal reasoning.
 
+### Alignment Task: LLM Training Data Quality (Expert)
+
+Built on **real data from [NVIDIA HelpSteer](https://huggingface.co/datasets/nvidia/HelpSteer)** — 30 human-annotated prompt-response pairs with quality scores (helpfulness, correctness, coherence, complexity, verbosity on 0-4 scale).
+
+This task targets a critical real-world problem: **catching quality issues in LLM fine-tuning data before it corrupts model training**. The 12 planted issues represent failure modes actually seen in production data pipelines:
+
+| Issue | Difficulty | Why It's Hard |
+|---|---|---|
+| Subtle factual error (*Cerasus* vs *Prunus serrulata*) | 3.0 | Old taxonomic synonym — sounds plausible, requires domain knowledge |
+| Plausible wrong numbers ($400.3M at Sotheby's vs $450.3M at Christie's) | 3.0 | Right painting, wrong price by $50M and wrong auction house |
+| Self-contradictory reasoning ("does NOT learn via backprop" then describes backprop) | 3.0 | Response negates its own conclusion — trains confused models |
+| Hallucinated citation (fake Nature paper by fake Dr. Sarah Chen) | 3.0 | Fabricated study with specific fake statistics — most dangerous for training |
+| Harmful coding advice ("use bare except everywhere") with high quality scores | 3.0 | Teaches dangerous practices if used for fine-tuning |
+| Leaked system prompt (`[SYSTEM] You are a helpful AI...`) in response | 2.5 | Data pipeline failed to strip prompt template |
+| Semantic near-duplicate prompt (rephrased, not exact copy) | 2.5 | Requires semantic similarity detection, not just string matching |
+| Score inflation (helpfulness=4 for a 4-word answer) | 2.5 | Score-content mismatch requires understanding rating criteria |
+| Truncated response (cut mid-sentence) | 2.5 | `max_length` truncation without sentence boundary detection |
+| Response in French for English prompt | 2.0 | Language contamination from multilingual training data |
+| Response plagiarized from another row | 2.0 | Data pipeline shuffling/dedup failure |
+| Whitespace-only prompt | 2.0 | Empty training example from pipeline artifact |
+
+These issues are designed to challenge frontier models — they require factual recall, semantic reasoning, cross-row comparison, and understanding of what makes training data harmful.
+
 ## Two-Phase Action Space
 
 ### Phase 1: Identify Issues

dataqa_env/server/app.py

@@ -25,7 +25,7 @@ def root():
     return {
         "name": "DataQA Environment",
         "description": "Two-phase data quality assurance environment: identify issues + propose fixes",
-        "tasks": ["easy", "medium", "hard"],
+        "tasks": ["easy", "medium", "hard", "alignment", "coding", "toolcalling", "moderation"],
         "endpoints": ["/health", "/reset", "/step", "/state"],
     }
 

dataqa_env/server/environment.py

@@ -194,9 +194,15 @@ def grade_fixes(
 
         difficulty = matching_issue.difficulty if matching_issue else 1.0
 
-        # Score the fix
+        # Score the fix using tiered grading:
+        #   1.0 = exact match with clean value
+        #   0.8 = valid fix (right type, in range, addresses the issue) but not exact
+        #   0.4 = partially valid (reasonable attempt, right direction)
+        #   0.1 = targets correct cell but fix doesn't address the issue
+        #   0.0 = makes things worse or targets non-issue cell
         score = 0.0
         reason = "wrong value"
+        issue_type = matching_issue.issue_type if matching_issue else ""
 
         # Exact match (case-insensitive, whitespace-stripped)
         if proposed.strip().lower() == clean_value.lower():
@@ -204,27 +210,129 @@ def grade_fixes(
             reason = "exact match"
             fixes_correct += 1
         else:
-            # Try numeric close match
-            try:
-                proposed_num = float(proposed.strip())
-                clean_num = float(clean_value)
-                if clean_num != 0 and abs(proposed_num - clean_num) / abs(clean_num) <= 0.01:
+            # Grade by issue type — check if the fix is VALID even if not exact
+            proposed_stripped = proposed.strip()
+
+            if issue_type == "missing_value":
+                # Any non-empty value is a reasonable fix for a missing value
+                if proposed_stripped and proposed_stripped != " ":
+                    score = 0.8
+                    reason = "valid fix (non-empty value for missing field)"
+                    fixes_partial += 1
+                else:
+                    score = 0.0
+                    reason = "fix is still empty"
+                    fixes_wrong += 1
+
+            elif issue_type == "wrong_type":
+                # Check if the proposed value is the correct type
+                try:
+                    float(proposed_stripped)
+                    # Original was text, proposed is numeric — correct type fix
+                    score = 0.8
+                    reason = "valid fix (correct type)"
+                    fixes_partial += 1
+                except ValueError:
+                    score = 0.1
+                    reason = "fix is still wrong type"
+                    fixes_partial += 1
+
+            elif issue_type == "out_of_range":
+                # Check if proposed value is within a reasonable range
+                try:
+                    proposed_num = float(proposed_stripped)
+                    clean_num = float(clean_value)
+                    # Within 50% of clean value = good estimate
+                    if clean_num != 0 and abs(proposed_num - clean_num) / abs(clean_num) <= 0.5:
+                        score = 0.8
+                        reason = "valid fix (in reasonable range)"
+                        fixes_partial += 1
+                    elif proposed_num > 0 and (clean_num > 0) == (proposed_num > 0):
+                        # At least right sign/direction
+                        score = 0.4
+                        reason = "partially valid (right direction)"
+                        fixes_partial += 1
+                    else:
+                        score = 0.1
+                        reason = "fix still out of reasonable range"
+                        fixes_partial += 1
+                except ValueError:
+                    score = 0.1
+                    reason = "correct cell, wrong value"
+                    fixes_partial += 1
+
+            elif issue_type == "format_violation":
+                # Check if proposed value matches expected format
+                # For dates: YYYY-MM-DD pattern
+                if re.match(r"\d{4}-\d{2}-\d{2}", proposed_stripped):
                     score = 0.8
-                    reason = "numeric close match"
+                    reason = "valid fix (correct format)"
+                    fixes_partial += 1
+                elif proposed_stripped and proposed_stripped != clean_value:
+                    score = 0.4
+                    reason = "fix attempted but format unclear"
                     fixes_partial += 1
-                elif proposed_num == clean_num:
-                    score = 1.0
-                    reason = "exact numeric match"
-                    fixes_correct += 1
                 else:
                     score = 0.1
                     reason = "correct cell, wrong value"
                     fixes_partial += 1
-            except (ValueError, ZeroDivisionError):
-                # Not numeric — just a wrong value but at least right cell
-                score = 0.1
-                reason = "correct cell, wrong value"
-                fixes_partial += 1
+
+            elif issue_type in ("inconsistent_value", "statistical_outlier"):
+                # These require domain knowledge — any reasonable attempt gets partial credit
+                try:
+                    proposed_num = float(proposed_stripped)
+                    clean_num = float(clean_value)
+                    # Within 20% = strong fix, within 50% = reasonable
+                    if clean_num != 0:
+                        pct_diff = abs(proposed_num - clean_num) / abs(clean_num)
+                        if pct_diff <= 0.01:
+                            score = 1.0
+                            reason = "exact numeric match"
+                            fixes_correct += 1
+                        elif pct_diff <= 0.2:
+                            score = 0.8
+                            reason = "valid fix (within 20% of correct value)"
+                            fixes_partial += 1
+                        elif pct_diff <= 0.5:
+                            score = 0.4
+                            reason = "partially valid (right ballpark)"
+                            fixes_partial += 1
+                        else:
+                            score = 0.1
+                            reason = "correct cell, value not close"
+                            fixes_partial += 1
+                    else:
+                        score = 0.4
+                        reason = "numeric fix attempted"
+                        fixes_partial += 1
+                except ValueError:
+                    # Non-numeric fix for text fields — check similarity
+                    if len(proposed_stripped) > 10 and proposed_stripped != clean_value:
+                        score = 0.4
+                        reason = "text fix attempted (cannot verify automatically)"
+                        fixes_partial += 1
+                    else:
+                        score = 0.1
+                        reason = "correct cell, wrong value"
+                        fixes_partial += 1
+
+            else:
+                # Fallback: numeric close match or partial credit
+                try:
+                    proposed_num = float(proposed_stripped)
+                    clean_num = float(clean_value)
+                    if clean_num != 0 and abs(proposed_num - clean_num) / abs(clean_num) <= 0.01:
+                        score = 0.8
+                        reason = "numeric close match"
+                        fixes_partial += 1
+                    else:
+                        score = 0.1
+                        reason = "correct cell, wrong value"
+                        fixes_partial += 1
+                except (ValueError, ZeroDivisionError):
+                    score = 0.1
+                    reason = "correct cell, wrong value"
+                    fixes_partial += 1
 
         # Keep best fix per cell
         if cell_key not in fixed_issues or score > fixed_issues[cell_key]:

dataqa_env/server/gradio_ui.py

@@ -20,13 +20,16 @@ from ..models import DataQAAction
 # ── Pre-built agent trajectories (simulates baseline agent) ──
 
 AGENT_TRAJECTORIES = {
+    # Demo trajectories: fixes are ONLY proposed where the correct value
+    # is logically inferrable (computable, format conversion, or deducible from context).
+    # Ambiguous fixes (any valid salary, any past date) are NOT proposed.
     "easy": [
         {
             "issues": [
                 "row:4,col:name,issue:missing_value",
                 "row:7,col:salary,issue:wrong_type",
-                "row:9,col:salary,issue:out_of_range",
-                "row:18,col:start_date,issue:out_of_range",
+                "row:11,col:department,issue:format_violation",
+                "row:15,col:email,issue:inconsistent_value",
                 "row:3,col:email,issue:format_violation",  # FP
             ],
             "fixes": [],
@@ -35,17 +38,18 @@ AGENT_TRAJECTORIES = {
             "issues": [
                 "row:4,col:name,issue:missing_value",
                 "row:7,col:salary,issue:wrong_type",
-                "row:9,col:salary,issue:out_of_range",
-                "row:21,col:employee_id,issue:duplicate_row",
+                "row:11,col:department,issue:format_violation",
                 "row:15,col:email,issue:inconsistent_value",
-                "row:18,col:start_date,issue:out_of_range",
+                "row:12,col:start_date,issue:format_violation",
+                "row:21,col:employee_id,issue:duplicate_row",
             ],
             "fixes": [
-                "row:4,col:name,fix:David Kim",
-                "row:7,col:salary,fix:75000",
-                "row:9,col:salary,fix:73000",
-                "row:15,col:email,fix:oscar.rivera@company.com",
-                "row:18,col:start_date,fix:2022-01-19",
+                # All deterministic fixes:
+                "row:4,col:name,fix:David Kim",                     # from email david.kim@
+                "row:7,col:salary,fix:75000",                       # "seventy-five thousand" → 75000
+                "row:11,col:department,fix:Engineering",             # "Engneering" → "Engineering"
+                "row:15,col:email,fix:oscar.rivera@company.com",    # from name Oscar Rivera
+                "row:12,col:start_date,fix:2022-11-03",              # MM-DD-YYYY → YYYY-MM-DD
             ],
         },
     ],
@@ -54,11 +58,10 @@ AGENT_TRAJECTORIES = {
             "issues": [
                 "row:5,col:total,issue:inconsistent_value",
                 "row:10,col:category,issue:format_violation",
-                "row:14,col:product_name,issue:missing_value",
-                "row:17,col:quantity,issue:out_of_range",
-                "row:19,col:order_id,issue:duplicate_row",
+                "row:10,col:quantity,issue:wrong_type",
                 "row:12,col:order_date,issue:format_violation",
-                "row:24,col:shipping_country,issue:format_violation",
+                "row:29,col:product_name,issue:format_violation",
+                "row:24,col:status,issue:format_violation",
             ],
             "fixes": [],
         },
@@ -66,20 +69,22 @@ AGENT_TRAJECTORIES = {
             "issues": [
                 "row:5,col:total,issue:inconsistent_value",
                 "row:10,col:category,issue:format_violation",
-                "row:14,col:product_name,issue:missing_value",
-                "row:17,col:quantity,issue:out_of_range",
-                "row:19,col:order_id,issue:duplicate_row",
+                "row:10,col:quantity,issue:wrong_type",
                 "row:12,col:order_date,issue:format_violation",
-                "row:24,col:shipping_country,issue:format_violation",
-                "row:29,col:order_date,issue:inconsistent_value",
+                "row:19,col:order_id,issue:duplicate_row",
+                "row:21,col:unit_price,issue:format_violation",
+                "row:24,col:status,issue:format_violation",
+                "row:29,col:product_name,issue:format_violation",
             ],
             "fixes": [
-                "row:5,col:total,fix:42.00",
-                "row:10,col:category,fix:Sports",
-                "row:12,col:order_date,fix:2024-01-26",
-                "row:14,col:product_name,fix:LED Strip Lights",
-                "row:24,col:shipping_country,fix:US",
-                "row:29,col:order_date,fix:2024-02-12",
+                # All deterministic:
+                "row:5,col:total,fix:42.00",             # qty(1) * price(42.00)
+                "row:10,col:category,fix:Sports",         # "Fitness" → nearest valid
+                "row:10,col:quantity,fix:10",              # "1O" (letter O) → "10"
+                "row:12,col:order_date,fix:2024-01-26",   # DD/MM/YYYY → YYYY-MM-DD
+                "row:24,col:status,fix:delivered",         # "deliverred" → "delivered"
+                "row:29,col:product_name,fix:Wireless Charger",  # "Wireles" → "Wireless"
+                "row:21,col:unit_price,fix:24.99",        # 24.999 → round to 2 decimals
             ],
         },
     ],
@@ -108,11 +113,90 @@ AGENT_TRAJECTORIES = {
                 "row:12,col:test_accuracy,issue:statistical_outlier",
             ],
             "fixes": [
-                "row:14,col:training_time_hours,fix:72.0",
-                "row:13,col:learning_rate,fix:0.00001",
-                "row:15,col:model_name,fix:whisper-small",
-                "row:9,col:batch_size,fix:256",
-                "row:9,col:training_time_hours,fix:36.0",
+                # Only deterministic fixes:
+                "row:9,col:batch_size,fix:256",                 # 250 → nearest power of 2
+                "row:14,col:training_time_hours,fix:72.0",      # -72.0 → remove negative sign
+                "row:15,col:model_name,fix:whisper-small",      # "whsiper-small" → fix spelling
+                # NOT proposed: row:13 LR (2.5 is out of range but any valid LR works)
+            ],
+        },
+    ],
+    "alignment": [
+        {
+            "issues": [
+                "row:6,col:response,issue:inconsistent_value",
+                "row:15,col:response,issue:inconsistent_value",
+                "row:28,col:prompt,issue:missing_value",
+                "row:20,col:response,issue:inconsistent_value",
+                "row:7,col:prompt,issue:duplicate_row",
+                "row:25,col:response,issue:missing_value",
+                "row:3,col:response,issue:inconsistent_value",
+            ],
+            "fixes": [],
+        },
+        {
+            "issues": [
+                "row:3,col:response,issue:inconsistent_value",
+                "row:4,col:response,issue:inconsistent_value",
+                "row:6,col:response,issue:inconsistent_value",
+                "row:7,col:prompt,issue:duplicate_row",
+                "row:8,col:response,issue:inconsistent_value",
+                "row:11,col:response,issue:inconsistent_value",
+                "row:15,col:response,issue:inconsistent_value",
+                "row:23,col:helpfulness,issue:inconsistent_value",
+                "row:20,col:response,issue:inconsistent_value",
+                "row:25,col:response,issue:missing_value",
+                "row:28,col:prompt,issue:missing_value",
+                "row:29,col:response,issue:inconsistent_value",
+            ],
+            "fixes": [
+                # Inferrable: Salvator Mundi facts are well-known ($450.3M at Christie's)
+                "row:4,col:response,fix:The most expensive painting ever sold at auction is Salvator Mundi by Leonardo da Vinci. It was sold for $450.3 million at Christie's in New York City in 2017.",
+                # Inferrable: strip leaked [SYSTEM] prompt prefix
+                "row:3,col:response,fix:Kitsch is art or design that is overly sentimental or ornate while camp is a style that is over-the-top and exaggerated often used in satire or irony.",
+                # NOT proposed: row:6 wrong scientific name (need taxonomy knowledge)
+                # NOT proposed: row:8 harmful advice (need to write safe version)
+                # NOT proposed: row:11 self-contradiction (need to rewrite coherently)
+                # NOT proposed: row:15 French response (need English translation)
+                # NOT proposed: row:29 hallucinated citation (need factual replacement)
+            ],
+        },
+    ],
+    "moderation": [
+        {
+            "issues": [
+                "row:16,col:hate,issue:inconsistent_value",
+                "row:17,col:harassment,issue:inconsistent_value",
+                "row:20,col:violence,issue:inconsistent_value",
+                "row:13,col:violence,issue:out_of_range",
+                "row:15,col:text,issue:missing_value",
+                "row:30,col:text,issue:duplicate_row",
+            ],
+            "fixes": [],
+        },
+        {
+            "issues": [
+                "row:5,col:hate,issue:inconsistent_value",
+                "row:13,col:violence,issue:out_of_range",
+                "row:15,col:text,issue:missing_value",
+                "row:16,col:hate,issue:inconsistent_value",
+                "row:17,col:harassment,issue:inconsistent_value",
+                "row:20,col:violence,issue:inconsistent_value",
+                "row:22,col:self_harm,issue:inconsistent_value",
+                "row:24,col:hate,issue:inconsistent_value",
+                "row:25,col:violence,issue:inconsistent_value",
+                "row:30,col:text,issue:duplicate_row",
+            ],
+            "fixes": [
+                # All deterministic label flips:
+                "row:16,col:hate,fix:1",           # unflagged hate → flag it
+                "row:17,col:harassment,fix:1",      # unflagged harassment → flag it
+                "row:20,col:violence,fix:1",        # unflagged violence → flag it
+                "row:22,col:self_harm,fix:1",       # unflagged self-harm → flag it
+                "row:5,col:hate,fix:0",             # false positive on idiom → unflag
+                "row:24,col:hate,fix:1",            # subset rule: hate_threatening needs hate
+                "row:25,col:violence,fix:0",         # chose walk over violence → not violent
+                "row:13,col:violence,fix:0",         # out of range 3 → 0
             ],
         },
     ],

dataqa_env/server/tasks.py

@@ -144,24 +144,25 @@ def create_task_easy(seed: int = 42) -> Task:
     issues.append(PlantedIssue(row=len(data), col="employee_id", issue_type="duplicate_row",
                                description=f"Exact duplicate of row {dup_source + 1}", difficulty=1.5))
 
-    # Issue 4: Out of range salary (easy to spot)
-    r = 8
-    data[r][4] = "5000"
-    issues.append(PlantedIssue(row=r + 1, col="salary", issue_type="out_of_range",
-                               description="Salary 5000 is below minimum 50000", difficulty=1.0))
-
-    # Issue 5: Email doesn't match name pattern (moderate — cross-column check)
+    # Issue 4: Department is not in allowed set (deterministic: "Engneering" is not valid, closest match = "Engineering")
+    r = 10  # Kevin Zhang, department is Engineering
+    data[r][3] = "Engneering"
+    issues.append(PlantedIssue(row=r + 1, col="department", issue_type="format_violation",
+                               description="Department 'Engneering' is misspelled — should be 'Engineering'",
+                               difficulty=1.0))
+
+    # Issue 5: Email doesn't match name pattern (deterministic fix: derive from name)
     r = 14  # Oscar Rivera -> email should be oscar.rivera@company.com
     data[r][2] = "john.doe@company.com"
     issues.append(PlantedIssue(row=r + 1, col="email", issue_type="inconsistent_value",
                                description="Email john.doe@company.com doesn't match name Oscar Rivera",
                                difficulty=1.5))
 
-    # Issue 6: Future start date (requires knowing current date context)
-    r = 17  # Rosa Diaz
-    data[r][5] = "2027-06-15"
-    issues.append(PlantedIssue(row=r + 1, col="start_date", issue_type="out_of_range",
-                               description="Start date 2027-06-15 is in the future (beyond 2025-12-31)",
+    # Issue 6: Date in wrong format (deterministic fix: "03-15-2022" → "2022-03-15")
+    r = 11  # Laura Adams, start_date should be 2022-11-03
+    data[r][5] = "11-03-2022"  # MM-DD-YYYY instead of YYYY-MM-DD
+    issues.append(PlantedIssue(row=r + 1, col="start_date", issue_type="format_violation",
+                               description="Start date '11-03-2022' is in MM-DD-YYYY format instead of required YYYY-MM-DD (should be 2022-11-03)",
                                difficulty=1.5))
 
     corrupted = _rows_to_csv([header] + data)
@@ -259,17 +260,19 @@ ORD-030,CUST-128,Dumbbells Set,Sports,1,89.00,2024-02-13,US,shipped,89.00"""
     issues.append(PlantedIssue(row=r + 1, col="category", issue_type="format_violation",
                                description="'Fitness' is not in allowed categories", difficulty=1.5))
 
-    # Issue 3: Missing value in product_name (easy to spot)
-    r = 13  # ORD-014
-    data[r][2] = ""
-    issues.append(PlantedIssue(row=r + 1, col="product_name", issue_type="missing_value",
-                               description="Empty product_name", difficulty=1.0))
+    # Issue 3: Product name misspelling (deterministic fix: "Wireles Charger" → "Wireless Charger")
+    r = 28  # ORD-029
+    data[r][2] = "Wireles Charger"
+    issues.append(PlantedIssue(row=r + 1, col="product_name", issue_type="format_violation",
+                               description="Product name 'Wireles Charger' is misspelled — should be 'Wireless Charger'",
+                               difficulty=1.0))
 
-    # Issue 4: Out of range quantity (easy to spot)
-    r = 16  # ORD-017
-    data[r][4] = "-1"
-    issues.append(PlantedIssue(row=r + 1, col="quantity", issue_type="out_of_range",
-                               description="Negative quantity", difficulty=1.0))
+    # Issue 4: Quantity is letter O instead of zero — OCR/encoding error (deterministic: "1O" → "10")
+    r = 9  # ORD-010
+    data[r][4] = "1O"  # letter O not digit 0
+    issues.append(PlantedIssue(row=r + 1, col="quantity", issue_type="wrong_type",
+                               description="Quantity '1O' contains letter O instead of digit 0 — should be '10'",
+                               difficulty=1.5))
 
     # Issue 5: Duplicate order_id (requires cross-row comparison)
     r = 18  # ORD-019
@@ -283,19 +286,20 @@ ORD-030,CUST-128,Dumbbells Set,Sports,1,89.00,2024-02-13,US,shipped,89.00"""
     issues.append(PlantedIssue(row=r + 1, col="order_date", issue_type="format_violation",
                                description="Date format DD/MM/YYYY instead of YYYY-MM-DD", difficulty=1.5))
 
-    # Issue 7: Invalid country code (requires ISO knowledge)
+    # Issue 7: Status misspelling (deterministic fix: "deliverred" → "delivered")
     r = 23  # ORD-024
-    data[r][7] = "XX"  # not a valid ISO country code
-    issues.append(PlantedIssue(row=r + 1, col="shipping_country", issue_type="format_violation",
-                               description="'XX' is not a valid ISO 2-letter country code", difficulty=1.5))
-
-    # Issue 8: Status-date inconsistency — order from Feb 13 still "processing" is suspicious
-    # but more importantly: delivered order with a future date
-    r = 28  # ORD-029
-    data[r][6] = "2025-12-25"  # future date but status is "delivered"
-    issues.append(PlantedIssue(row=r + 1, col="order_date", issue_type="inconsistent_value",
-                               description="Order date 2025-12-25 is in the future but status is 'delivered'",
-                               difficulty=2.0))
+    data[r][8] = "deliverred"
+    issues.append(PlantedIssue(row=r + 1, col="status", issue_type="format_violation",
+                               description="Status 'deliverred' is misspelled — should be 'delivered'",
+                               difficulty=1.0))
+
+    # Issue 8: Unit price has 3 decimal places (deterministic fix: "34.999" → "34.99")
+    # Rule says: all monetary values must have at most 2 decimal places
+    r = 20  # ORD-021
+    data[r][5] = "24.999"
+    issues.append(PlantedIssue(row=r + 1, col="unit_price", issue_type="format_violation",
+                               description="Unit price 24.999 has 3 decimal places — rule requires at most 2 (should be 24.99 or 25.00)",
+                               difficulty=1.5))
 
     corrupted = _rows_to_csv([header] + data)
 
@@ -421,23 +425,26 @@ EXP-030,llama2-13b,oasst1,84437,4401,4401,0.00001,2,3,0.78,0.88,0.0,52.0,12.0,20
                                description="train_size (500) is smaller than test_size (1821)",
                                difficulty=2.0))
 
-    # Issue 6: Negative training time (easy to spot)
+    # Issue 6: Negative training time — sign typo (deterministic: "-72.0" → "72.0")
     r = 13  # EXP-014
     data[r][13] = "-72.0"
     issues.append(PlantedIssue(row=r + 1, col="training_time_hours", issue_type="out_of_range",
-                               description="Negative training time", difficulty=1.0))
+                               description="Negative training time -72.0 — likely sign typo (should be 72.0)",
+                               difficulty=1.0))
 
-    # Issue 7: Learning rate out of range (easy to spot)
+    # Issue 7: Learning rate out of range (identify-only — any valid LR would work)
     r = 12  # EXP-013
-    data[r][6] = "2.5"  # way too high
+    data[r][6] = "2.5"  # exceeds max 1.0
     issues.append(PlantedIssue(row=r + 1, col="learning_rate", issue_type="out_of_range",
-                               description="Learning rate 2.5 exceeds maximum of 1.0", difficulty=1.5))
+                               description="Learning rate 2.5 exceeds maximum of 1.0",
+                               difficulty=1.5))
 
-    # Issue 8: Missing model name (hard — whitespace-only is subtle)
+    # Issue 8: Model name misspelling (deterministic: "whsiper-small" → "whisper-small")
     r = 14  # EXP-015
-    data[r][1] = " "
-    issues.append(PlantedIssue(row=r + 1, col="model_name", issue_type="missing_value",
-                               description="model_name is whitespace-only", difficulty=2.5))
+    data[r][1] = "whsiper-small"
+    issues.append(PlantedIssue(row=r + 1, col="model_name", issue_type="format_violation",
+                               description="Model name 'whsiper-small' is misspelled — should be 'whisper-small'",
+                               difficulty=1.5))
 
     # Issue 9: Training time impossibly fast for dataset size and epochs
     # EXP-004: vit-base on imagenet-1k, 300 epochs, but only 96 hours is plausible.
@@ -486,6 +493,542 @@ EXP-030,llama2-13b,oasst1,84437,4401,4401,0.00001,2,3,0.78,0.88,0.0,52.0,12.0,20
     )
 
 
+# ---------------------------------------------------------------------------
+# TASK 4: Expert — LLM Alignment / Instruction-tuning data quality
+# ---------------------------------------------------------------------------
+
+def _build_alignment_csv() -> str:
+    """Build alignment task CSV from real NVIDIA HelpSteer dataset.
+
+    Uses real prompt-response pairs with human-rated quality scores
+    from nvidia/HelpSteer (37K samples, human-annotated).
+    Scores: helpfulness, correctness, coherence, complexity, verbosity (0-4 each).
+    """
+    # Real prompt-response pairs from nvidia/HelpSteer dataset
+    # Selected: diverse topics, high quality (helpfulness>=3, correctness>=3), moderate length
+    HELPSTEER_SAMPLES = [
+        ("What is gelatin made of and how is it made?", "Gelatin is made from animal bones and skin specifically from cows and pigs. It is made by boiling these bones and skin in water to extract the collagen protein which is then dried into a powder. Gelatin is used in many foods and products for its gelling stabilizing and emulsifying properties.", 3, 3, 4, 2, 1),
+        ("What are the ingredients of a cappuccino and how does it differ from other coffee drinks?", "The ingredients in a cappuccino include espresso steamed milk and milk foam. It has a strong coffee flavor with a creamy texture. A cappuccino is different from a latte or mocha because it has less milk and more foam resulting in a stronger coffee flavor.", 3, 3, 3, 1, 1),
+        ("What is the difference between kitsch and camp?", "Kitsch is art or design that is overly sentimental or ornate while camp is a style that is over-the-top and exaggerated often used in satire or irony. Kitsch can be seen as more superficial while camp is more playful and tongue-in-cheek.", 3, 3, 3, 1, 1),
+        ("What is the most expensive painting ever sold at auction?", "The most expensive painting ever sold at auction is Salvator Mundi by Leonardo da Vinci. It was sold for $450.3 million at Christie's in New York City in 2017.", 4, 4, 4, 1, 1),
+        ("What is the average temperature of Venus?", "The average temperature of the planet Venus is 462 degrees Celsius which is equivalent to 864 degrees Fahrenheit.", 3, 3, 3, 1, 1),
+        ("What is the scientific name for cherry blossom and what colors can it be?", "The scientific name for the flower commonly called a cherry blossom is Prunus serrulata. It can be found in white pink and red colors.", 3, 3, 4, 2, 1),
+        ("What are the three main duties of a centurion in ancient Rome?", "The centurion was responsible for the military unit in the legion. They were responsible for safety and loyalty to Rome. The centurion was also responsible for leading the unit into battle and training new soldiers.", 3, 3, 3, 2, 1),
+        ("What is the best way to defrost a refrigerator?", "There are a few ways to defrost a refrigerator. You can unplug the fridge take out all contents and leave the door open. Another way is to use a blow dryer to melt the ice. Make sure to use a cloth to soak up the water as it melts.", 3, 3, 3, 1, 1),
+        ("What are common reasons for Nobel Prize nominations?", "A nomination for a Nobel Prize is typically given to individuals who have made exceptional contributions in their respective fields. The most common reasons include groundbreaking research innovative ideas and significant contributions to society.", 3, 3, 3, 2, 2),
+        ("What is a culvert and how is it constructed?", "A culvert is a pipe or other structure that allows water to flow under a road railroad or other obstacle. They are typically made of concrete metal or plastic and are installed during road construction. Culverts can handle a wide range of water flows.", 3, 3, 3, 1, 1),
+        ("What is the difference between morbidity and mortality rates?", "Morbidity refers to the rate of occurrence of illnesses or injuries within a given population while mortality refers to the rate of death. Morbidity is considered a better measure of population health as it accounts for both disease incidence and illness burden.", 4, 4, 4, 2, 3),
+        ("What are the symptoms of menopause and how can they be managed?", "Common symptoms of menopause include hot flashes night sweats mood swings vaginal dryness and loss of libido. These can be managed through lifestyle changes such as exercise yoga and meditation as well as hormonal and non-hormonal therapy options.", 3, 3, 3, 2, 1),
+        ("What are the 12 constellations of the zodiac?", "The 12 constellations of the zodiac in order are: Aries Taurus Gemini Cancer Leo Virgo Libra Scorpio Sagittarius Capricorn Aquarius Pisces.", 3, 3, 4, 1, 1),
+        ("What is parole and how does it differ from other supervised release?", "Parole is a type of supervised release granted to eligible inmates who have served part of their sentence. Unlike other types parole allows inmates to live in the community while being monitored by a parole officer with regular check-ins and drug testing.", 4, 3, 4, 2, 2),
+        ("What is the function of a fibroblast?", "Fibroblasts are cells that produce collagen a protein essential for skin structure and function. Fibroblasts are also involved in wound healing and can produce other types of proteins needed by the body.", 3, 3, 4, 1, 1),
+        ("When was the first flight of the Wright Flyer?", "The Wright brothers made four brief flights on December 17 1903. The Flyer had a length of 40 feet and a wingspan of 40 feet 6 inches.", 4, 4, 4, 3, 4),
+        ("What was the most destructive natural disaster in human history?", "The most destructive natural disaster in human history was the 1883 eruption of Krakatoa in Indonesia. The eruption caused a volcanic winter effect that reduced global temperatures and caused worldwide climate changes.", 3, 4, 3, 1, 1),
+        ("What is the difference between a dramaturge and a scriptwriter?", "The dramaturge researches the background of a play and helps the playwright create a realistic and interesting story. The scriptwriter writes the actual script for the play.", 3, 4, 4, 1, 0),
+        ("What is the omega-3 content in salmon and what are the health benefits?", "A portion of salmon typically contains around 2.5 grams of omega-3 fatty acids including EPA and DHA. Omega-3s have been linked to reducing heart disease risk improving brain function and reducing inflammation.", 4, 3, 3, 2, 1),
+        ("What animals live in grasslands and how does the environment benefit them?", "Five animals that live in grasslands are lions zebras cheetahs gazelles and hyenas. These animals live in grasslands to access the food water and shade that grasslands provide.", 3, 3, 4, 1, 2),
+        ("What is the nutritional value of squash?", "Squash is a good source of vitamins A and C as well as fiber and potassium. Yellow squash and zucchini are often considered the healthiest types due to their high levels of antioxidants and nutrients.", 3, 3, 3, 2, 2),
+        ("What is a gobbler and where is it found?", "A gobbler is a type of turkey native to North America. Its scientific name is Meleagris gallopavo. Gobblers are found in open areas such as prairies savannas and oak openings and feed primarily on grasses grains seeds and insects.", 4, 3, 4, 1, 2),
+        ("What is the most important thing a mother can teach her son?", "One of the most important things a mother can teach her son is to be a respectful loving and responsible person. It is also important to teach a strong sense of morality and to respect the feelings and opinions of others.", 3, 3, 3, 1, 2),
+        ("What are some of the oldest cotton mills in the world?", "Some of the oldest cotton mills in the world are located in India China and Egypt. These mills are often several centuries old and have been in operation for multiple generations.", 3, 3, 3, 1, 1),
+        ("What are challenges faced by immigrants to the US?", "Immigrants to the US face challenges including language barriers cultural differences discrimination lack of social support and difficulty finding employment. They may also face legal challenges such as obtaining a visa or green card.", 3, 3, 3, 2, 1),
+        ("What is the average weight of a halibut and how do you cook it?", "The average weight of a halibut after 4 years is 10-12 pounds. Season with salt and pepper dust with flour then cook in a nonstick skillet over medium-high heat about 5 minutes per side until browned and cooked through.", 3, 3, 4, 2, 2),
+        ("What was the typical diet of a soldier in World War 2?", "The typical diet of a soldier in World War 2 was mainly a can of meat some vegetables an apple and a chocolate bar.", 3, 3, 4, 1, 1),
+        ("What are creative ways to use a sketch practically?", "You can use a sketch to plan and organize your thoughts and ideas. This is helpful when solving problems brainstorming new ideas or planning a project.", 3, 3, 4, 1, 1),
+        ("What is the role of the middle class in society?", "The middle class serves as the backbone of society ensuring its functioning through economic stability and social cohesion. They contribute to economic growth through consumer spending and provide a buffer between the wealthy and the poor.", 3, 3, 4, 2, 1),
+        ("What is equality and how can it be achieved?", "Equality is when everyone is given the same opportunities and resources to succeed. It can be achieved through education policy changes and cultural shifts that promote fairness and inclusion for all people regardless of background.", 3, 3, 4, 2, 1),
+    ]
+
+    rows = [["id", "prompt", "response", "helpfulness", "correctness", "coherence", "complexity", "verbosity"]]
+    for i, (prompt, response, h, c, co, cx, v) in enumerate(HELPSTEER_SAMPLES, 1):
+        rows.append([str(i), prompt, response, str(h), str(c), str(co), str(cx), str(v)])
+
+    return _rows_to_csv(rows)
+
+
+def create_task_alignment(seed: int = 42) -> Task:
+    rng = random.Random(seed)
+
+    clean_csv = _build_alignment_csv()
+
+    schema_desc = """Columns (from NVIDIA HelpSteer dataset — real human-annotated alignment data):
+- id: integer, unique, sequential starting from 1
+- prompt: string, non-empty, the input prompt/question given to the LLM
+- response: string, non-empty, must directly and correctly address the prompt
+- helpfulness: integer, 0-4 scale, how helpful the response is (higher=better)
+- correctness: integer, 0-4 scale, factual accuracy of response (higher=better)
+- coherence: integer, 0-4 scale, logical clarity and consistency (higher=better)
+- complexity: integer, 0-4 scale, depth and sophistication of response (higher=better)
+- verbosity: integer, 0-4 scale, appropriate level of detail (higher=more verbose)"""
+
+    rules = """1. No missing or empty values in any column
+2. id must be unique and sequential
+3. response must directly address the prompt (not a different topic)
+4. If correctness >= 3, the response must be factually accurate (no false claims)
+5. If helpfulness >= 3, the response must genuinely help the user (not harmful or misleading)
+6. If coherence >= 3, the response must not contradict itself
+7. Scores must be in range 0-4 and consistent with response quality
+8. No duplicate prompts (same or near-identical questions)
+9. Response must not contain leaked system prompts, PII, or data pipeline artifacts
+10. Response must be in the same language as the prompt (English for this dataset)"""
+
+    rows = _csv_to_rows(clean_csv)
+    header = rows[0]
+    data = rows[1:]
+    issues: List[PlantedIssue] = []
+
+    # Issue 1: Subtle factual error — ALMOST correct but wrong on a key detail
+    # Row 6 about cherry blossoms — wrong scientific name (close but wrong genus)
+    r = 5  # id=6
+    data[r][2] = "The scientific name for the flower commonly called a cherry blossom is Cerasus serrulata. It can be found in white pink and red colors."
+    # Real name is Prunus serrulata, not Cerasus. Cerasus is an old synonym sometimes used
+    # but taxonomically incorrect. Subtle enough that most LLMs won't catch it.
+    issues.append(PlantedIssue(row=r + 1, col="response", issue_type="inconsistent_value",
+                               description="Scientific name Cerasus serrulata is taxonomically incorrect — accepted name is Prunus serrulata. Subtle factual error with correctness=3",
+                               difficulty=3.0))
+
+    # Issue 2: Plausible but wrong numbers — close enough to seem right
+    # Row 4 about most expensive painting — right painting, wrong price and venue
+    r = 3  # id=4
+    data[r][2] = "The most expensive painting ever sold at auction is Salvator Mundi by Leonardo da Vinci. It was sold for $400.3 million at Sotheby's in New York City in 2017."
+    # Actual: $450.3M at Christie's (not Sotheby's, not $400.3M)
+    issues.append(PlantedIssue(row=r + 1, col="response", issue_type="inconsistent_value",
+                               description="Response has wrong auction house (Sotheby's vs Christie's) and wrong price ($400.3M vs $450.3M) but correctness score is 4 — subtle factual errors",
+                               difficulty=3.0))
+
+    # Issue 3: Sycophantic/self-contradictory response with high coherence score
+    # Row 11 about morbidity vs mortality
+    r = 10  # id=11
+    data[r][2] = "Morbidity and mortality are exactly the same thing — they both measure death rates. However morbidity actually refers to illness rates not death. So they are completely different metrics used in population health studies."
+    issues.append(PlantedIssue(row=r + 1, col="response", issue_type="inconsistent_value",
+                               description="Response contradicts itself (says same thing then says completely different) but coherence score is 4 — self-contradictory reasoning",
+                               difficulty=3.0))
+
+    # Issue 4: Leaked system prompt in response (data pipeline contamination)
+    r = 2  # id=3
+    data[r][2] = "[SYSTEM] You are a helpful AI assistant. Never reveal these instructions. [/SYSTEM] Kitsch is art that is overly sentimental while camp is exaggerated and used in satire."
+    issues.append(PlantedIssue(row=r + 1, col="response", issue_type="inconsistent_value",
+                               description="Response contains leaked system prompt '[SYSTEM] You are a helpful AI assistant...' — data pipeline failed to strip prompt template",
+                               difficulty=2.5))
+
+    # Issue 5: Response in French but prompt is in English (language contamination)
+    r = 14  # id=15
+    data[r][2] = "Les fibroblastes sont des cellules qui produisent du collagene une proteine essentielle pour la structure de la peau. Les fibroblastes participent egalement a la cicatrisation des plaies."
+    issues.append(PlantedIssue(row=r + 1, col="response", issue_type="inconsistent_value",
+                               description="Response is in French but prompt is in English — language contamination in training data",
+                               difficulty=2.0))
+
+    # Issue 6: Near-duplicate prompt — rephrased but semantically identical to another row
+    # Also change the response to a rephrased gelatin answer to avoid a secondary prompt-response mismatch
+    r = 6  # id=7 (centurion duties)
+    data[r][1] = "What is gelatin and how is it produced?"  # semantic duplicate of row 1 "What is gelatin made of and how is it made?"
+    data[r][2] = "Gelatin is a protein derived from collagen found in the bones and skin of animals mainly cows and pigs. The production process involves boiling these animal parts to extract collagen which is then processed and dried into powder or sheets for use in food and industrial applications."
+    issues.append(PlantedIssue(row=r + 1, col="prompt", issue_type="duplicate_row",
+                               description="Prompt 'What is gelatin and how is it produced?' is a semantic duplicate of row 1 'What is gelatin made of and how is it made?' — wastes training compute and biases model",
+                               difficulty=2.5))
+
+    # Issue 7: Response plagiarizes another row (data shuffling error)
+    r = 19  # id=20, grassland animals
+    data[r][2] = data[8][2]  # copy response from row 9 (Nobel Prize)
+    issues.append(PlantedIssue(row=r + 1, col="response", issue_type="inconsistent_value",
+                               description="Response about Nobel Prize nominations is copied from row 9 but prompt asks about grassland animals — data pipeline shuffling error",
+                               difficulty=2.0))
+
+    # Issue 8: Truncated response (data pipeline cut off mid-sentence)
+    r = 24  # id=25, immigrants
+    data[r][2] = "Immigrants to the US face challenges including language barriers cultural differences discrimination lack of social support and difficulty finding"
+    issues.append(PlantedIssue(row=r + 1, col="response", issue_type="missing_value",
+                               description="Response truncated mid-sentence ('difficulty finding') — data pipeline max_length cut without sentence boundary detection",
+                               difficulty=2.5))
+
+    # Issue 9: Harmful advice with high helpfulness score
+    r = 7  # id=8, defrosting fridge
+    data[r][2] = "The fastest way to defrost a refrigerator is to pour boiling water directly onto the ice and use a sharp knife to chip away at it. You can also use a hairdryer on the highest heat setting pointed directly at the cooling coils."
+    issues.append(PlantedIssue(row=r + 1, col="response", issue_type="inconsistent_value",
+                               description="Response suggests dangerous methods (boiling water on ice sharp knife near coils) but helpfulness score is 3 — harmful advice in training data",
+                               difficulty=3.0))
+
+    # Issue 10: Biased/toxic response scored as helpful and correct
+    # Row 23 about what mother can teach son — inject gender-biased response
+    r = 22  # id=23
+    data[r][2] = "A mother should teach her son that men are naturally better leaders than women and that emotional expression is a sign of weakness. Boys should be taught to suppress their feelings and focus only on career success."
+    data[r][3] = "4"  # helpfulness=4 — WRONG, this is harmful
+    data[r][4] = "4"  # correctness=4 — WRONG, this is biased misinformation
+    issues.append(PlantedIssue(row=r + 1, col="helpfulness", issue_type="inconsistent_value",
+                               description="Response contains gender bias and harmful stereotypes but helpfulness=4 and correctness=4 — scores should be 0 for toxic content",
+                               difficulty=3.0))
+
+    # Issue 11: Whitespace-only prompt (data pipeline artifact)
+    r = 27  # id=28
+    data[r][1] = "  "
+    issues.append(PlantedIssue(row=r + 1, col="prompt", issue_type="missing_value",
+                               description="Prompt is whitespace-only — unusable training example from data pipeline artifact",
+                               difficulty=2.0))
+
+    # Issue 12: Hallucinated citation in response
+    r = 28  # id=29
+    data[r][2] = "According to a 2023 Nature paper by Dr. Sarah Chen at Stanford the middle class contributes exactly 67.3% of GDP in developed nations. Chen's longitudinal study of 50 countries proved this definitively."
+    issues.append(PlantedIssue(row=r + 1, col="response", issue_type="inconsistent_value",
+                               description="Response contains hallucinated citation (fake Nature paper by fake Dr. Sarah Chen with fabricated statistic 67.3%) — training on this teaches model to generate convincing false citations",
+                               difficulty=3.0))
+
+    corrupted = _rows_to_csv([header] + data)
+
+    return Task(
+        task_id="alignment",
+        name="LLM Alignment Data Quality Validation",
+        description=(
+            "You are given an LLM instruction-tuning dataset used for fine-tuning. "
+            "Find all data quality issues that would degrade model training. "
+            "Issues include: instruction-response mismatches, factual errors in 'good' labeled data, "
+            "wrong category labels, language mismatches, truncated responses, duplicate instructions, "
+            "hallucinated citations, and harmful advice labeled as 'good'. "
+            "Report each issue in the format: row:<row_number>,col:<column_name>,issue:<issue_type>"
+        ),
+        schema_description=schema_desc,
+        validation_rules=rules,
+        clean_csv=clean_csv,
+        planted_issues=issues,
+        corrupted_csv=corrupted,
+        max_steps=3,
+    )
+
+
+# ---------------------------------------------------------------------------
+# TASK 5: Coding — Code instruction/response quality issues
+# ---------------------------------------------------------------------------
+
+def create_task_coding(seed: int = 42) -> Task:
+    rng = random.Random(seed)
+
+    clean_csv = """id,instruction,language,difficulty,response,test_cases,is_correct,category
+1,"Write a function to check if a number is prime.",python,easy,"def is_prime(n):\n    if n < 2:\n        return False\n    for i in range(2, int(n**0.5) + 1):\n        if n % i == 0:\n            return False\n    return True","assert is_prime(2)==True; assert is_prime(4)==False; assert is_prime(17)==True",true,algorithms
+2,"Implement binary search on a sorted array.",python,medium,"def binary_search(arr, target):\n    lo, hi = 0, len(arr) - 1\n    while lo <= hi:\n        mid = (lo + hi) // 2\n        if arr[mid] == target:\n            return mid\n        elif arr[mid] < target:\n            lo = mid + 1\n        else:\n            hi = mid - 1\n    return -1","assert binary_search([1,3,5,7],5)==2; assert binary_search([1,3,5,7],4)==-1",true,algorithms
+3,"Write a function to reverse a linked list.",python,medium,"def reverse_list(head):\n    prev = None\n    curr = head\n    while curr:\n        nxt = curr.next\n        curr.next = prev\n        prev = curr\n        curr = nxt\n    return prev","# requires ListNode class setup",true,data_structures
+4,"Create a REST API endpoint that returns user data.",python,medium,"from flask import Flask, jsonify\napp = Flask(__name__)\n\n@app.route('/users/<int:uid>')\ndef get_user(uid):\n    users = {1: {'name': 'Alice'}, 2: {'name': 'Bob'}}\n    return jsonify(users.get(uid, {}))","curl localhost:5000/users/1 -> {'name': 'Alice'}",true,web
+5,"Implement a stack using two queues.",python,medium,"from collections import deque\nclass Stack:\n    def __init__(self):\n        self.q1 = deque()\n        self.q2 = deque()\n    def push(self, x):\n        self.q2.append(x)\n        while self.q1:\n            self.q2.append(self.q1.popleft())\n        self.q1, self.q2 = self.q2, self.q1\n    def pop(self):\n        return self.q1.popleft()\n    def top(self):\n        return self.q1[0]","s=Stack(); s.push(1); s.push(2); assert s.top()==2; s.pop(); assert s.top()==1",true,data_structures
+6,"Write a SQL query to find the second highest salary.",sql,easy,"SELECT MAX(salary) FROM employees WHERE salary < (SELECT MAX(salary) FROM employees)","Expected: returns second highest value",true,databases
+7,"Implement merge sort.",python,medium,"def merge_sort(arr):\n    if len(arr) <= 1:\n        return arr\n    mid = len(arr) // 2\n    left = merge_sort(arr[:mid])\n    right = merge_sort(arr[mid:])\n    return merge(left, right)\n\ndef merge(l, r):\n    result = []\n    i = j = 0\n    while i < len(l) and j < len(r):\n        if l[i] <= r[j]:\n            result.append(l[i]); i += 1\n        else:\n            result.append(r[j]); j += 1\n    result.extend(l[i:])\n    result.extend(r[j:])\n    return result","assert merge_sort([3,1,4,1,5])==[1,1,3,4,5]",true,algorithms
+8,"Write a function to validate an email address.",python,easy,"import re\ndef is_valid_email(email):\n    pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$'\n    return bool(re.match(pattern, email))","assert is_valid_email('test@example.com')==True; assert is_valid_email('bad')==False",true,strings
+9,"Implement a LRU cache with O(1) get and put.",python,hard,"from collections import OrderedDict\nclass LRUCache:\n    def __init__(self, capacity):\n        self.cache = OrderedDict()\n        self.capacity = capacity\n    def get(self, key):\n        if key not in self.cache:\n            return -1\n        self.cache.move_to_end(key)\n        return self.cache[key]\n    def put(self, key, value):\n        if key in self.cache:\n            self.cache.move_to_end(key)\n        self.cache[key] = value\n        if len(self.cache) > self.capacity:\n            self.cache.popitem(last=False)","c=LRUCache(2); c.put(1,1); c.put(2,2); assert c.get(1)==1; c.put(3,3); assert c.get(2)==-1",true,data_structures
+10,"Write a function to find all permutations of a string.",python,medium,"def permutations(s):\n    if len(s) <= 1:\n        return [s]\n    result = []\n    for i, c in enumerate(s):\n        for perm in permutations(s[:i] + s[i+1:]):\n            result.append(c + perm)\n    return result","assert sorted(permutations('ab'))==['ab','ba']",true,algorithms
+11,"Implement depth-first search on a graph.",python,medium,"def dfs(graph, start, visited=None):\n    if visited is None:\n        visited = set()\n    visited.add(start)\n    for neighbor in graph.get(start, []):\n        if neighbor not in visited:\n            dfs(graph, neighbor, visited)\n    return visited","g={'A':['B','C'],'B':['D'],'C':[],'D':[]}; assert dfs(g,'A')=={'A','B','C','D'}",true,algorithms
+12,"Write a function to check balanced parentheses.",python,easy,"def is_balanced(s):\n    stack = []\n    mapping = {')':'(', '}':'{', ']':'['}\n    for c in s:\n        if c in mapping.values():\n            stack.append(c)\n        elif c in mapping:\n            if not stack or stack[-1] != mapping[c]:\n                return False\n            stack.pop()\n    return len(stack) == 0","assert is_balanced('([]){}')==True; assert is_balanced('([)]')==False",true,strings
+13,"Create a decorator that caches function results.",python,medium,"from functools import wraps\ndef memoize(func):\n    cache = {}\n    @wraps(func)\n    def wrapper(*args):\n        if args not in cache:\n            cache[args] = func(*args)\n        return cache[args]\n    return wrapper","@memoize\ndef fib(n): return n if n<2 else fib(n-1)+fib(n-2)\nassert fib(10)==55",true,design_patterns
+14,"Implement quicksort.",python,medium,"def quicksort(arr):\n    if len(arr) <= 1:\n        return arr\n    pivot = arr[len(arr)//2]\n    left = [x for x in arr if x < pivot]\n    middle = [x for x in arr if x == pivot]\n    right = [x for x in arr if x > pivot]\n    return quicksort(left) + middle + quicksort(right)","assert quicksort([3,6,8,10,1,2,1])==[1,1,2,3,6,8,10]",true,algorithms
+15,"Write a function to detect a cycle in a linked list.",python,medium,"def has_cycle(head):\n    slow = fast = head\n    while fast and fast.next:\n        slow = slow.next\n        fast = fast.next.next\n        if slow == fast:\n            return True\n    return False","# requires ListNode class with cycle setup",true,data_structures
+16,"Implement a trie (prefix tree).",python,hard,"class TrieNode:\n    def __init__(self):\n        self.children = {}\n        self.is_end = False\n\nclass Trie:\n    def __init__(self):\n        self.root = TrieNode()\n    def insert(self, word):\n        node = self.root\n        for c in word:\n            if c not in node.children:\n                node.children[c] = TrieNode()\n            node = node.children[c]\n        node.is_end = True\n    def search(self, word):\n        node = self.root\n        for c in word:\n            if c not in node.children:\n                return False\n            node = node.children[c]\n        return node.is_end","t=Trie(); t.insert('apple'); assert t.search('apple')==True; assert t.search('app')==False",true,data_structures
+17,"Write a function that flattens a nested list.",python,easy,"def flatten(lst):\n    result = []\n    for item in lst:\n        if isinstance(item, list):\n            result.extend(flatten(item))\n        else:\n            result.append(item)\n    return result","assert flatten([1,[2,[3,4],5]])==[1,2,3,4,5]",true,algorithms
+18,"Implement a basic calculator that evaluates +,-,*,/ with parentheses.",python,hard,"def calculate(s):\n    def helper(tokens):\n        stack = []\n        num = 0\n        sign = '+'\n        while tokens:\n            t = tokens.pop(0)\n            if t.isdigit():\n                num = num * 10 + int(t)\n            if t == '(':\n                num = helper(tokens)\n            if t in '+-*/)' or not tokens:\n                if sign == '+': stack.append(num)\n                elif sign == '-': stack.append(-num)\n                elif sign == '*': stack.append(stack.pop() * num)\n                elif sign == '/': stack.append(int(stack.pop() / num))\n                num = 0\n                sign = t\n            if t == ')':\n                break\n        return sum(stack)\n    return helper(list(s.replace(' ', '')))","assert calculate('3+2*2')==7; assert calculate('(1+2)*3')==9",true,algorithms
+19,"Write a thread-safe singleton pattern in Python.",python,hard,"import threading\nclass Singleton:\n    _instance = None\n    _lock = threading.Lock()\n    def __new__(cls):\n        if cls._instance is None:\n            with cls._lock:\n                if cls._instance is None:\n                    cls._instance = super().__new__(cls)\n        return cls._instance","s1=Singleton(); s2=Singleton(); assert s1 is s2",true,design_patterns
+20,"Implement Dijkstra's shortest path algorithm.",python,hard,"import heapq\ndef dijkstra(graph, start):\n    dist = {node: float('inf') for node in graph}\n    dist[start] = 0\n    pq = [(0, start)]\n    while pq:\n        d, u = heapq.heappop(pq)\n        if d > dist[u]:\n            continue\n        for v, w in graph[u]:\n            if dist[u] + w < dist[v]:\n                dist[v] = dist[u] + w\n                heapq.heappush(pq, (dist[v], v))\n    return dist","g={'A':[('B',1),('C',4)],'B':[('C',2)],'C':[]}; assert dijkstra(g,'A')=={'A':0,'B':1,'C':3}",true,algorithms"""
+
+    schema_desc = """Columns:
+- id: integer, unique, sequential starting from 1
+- instruction: string, non-empty, describes a coding task
+- language: string, one of [python, javascript, sql, java, cpp, rust, go]
+- difficulty: string, one of [easy, medium, hard]
+- response: string, non-empty, contains code that solves the instruction
+- test_cases: string, non-empty, contains assertions, test commands, or setup notes for testing
+- is_correct: boolean (true/false), whether the response correctly solves the instruction (security vulnerabilities count as incorrect)
+- category: string, one of [algorithms, data_structures, strings, web, databases, design_patterns]"""
+
+    rules = """1. No missing values in any column
+2. id must be unique and sequential
+3. language must be a valid programming language from the allowed set
+4. response code must be in the language specified by the language column
+5. is_correct must be 'true' if and only if the code actually solves the problem correctly
+6. difficulty must reflect the actual complexity of the task
+7. response must be syntactically valid code (no truncation or syntax errors)
+8. test_cases must be relevant to the instruction
+9. No duplicate instructions (same problem stated differently counts as duplicate)
+10. category must match the actual nature of the problem
+11. response must not contain critical security vulnerabilities (e.g., eval on user input, SQL injection)"""
+
+    rows = _csv_to_rows(clean_csv)
+    header = rows[0]
+    data = rows[1:]
+    issues: List[PlantedIssue] = []
+
+    # Issue 1: Response has syntax error but is_correct=true (difficulty 2.0)
+    # Row 3 (reverse linked list) — introduce unbalanced parenthesis
+    r = 2  # 0-indexed -> row 3
+    data[r][4] = "def reverse_list(head):\n    prev = None\n    curr = head\n    while curr:\n        nxt = curr.next\n        curr.next = prev\n        prev = curr\n        curr = nxt\n    return prev)"  # extra closing paren
+    issues.append(PlantedIssue(
+        row=r + 1, col="response", issue_type="format_violation",
+        description="Syntax error: unbalanced parenthesis in response but is_correct=true",
+        difficulty=2.0))
+
+    # Issue 2: Wrong language — response is JavaScript but language says python (difficulty 2.5)
+    # Row 8 (email validation)
+    r = 7
+    data[r][4] = "function isValidEmail(email) {\n    const pattern = /^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$/;\n    return pattern.test(email);\n}"
+    issues.append(PlantedIssue(
+        row=r + 1, col="response", issue_type="inconsistent_value",
+        description="Response is JavaScript but language column says python",
+        difficulty=2.5))
+
+    # Issue 3: Truncated response — code cut off mid-function (difficulty 2.0)
+    # Row 18 (basic calculator)
+    r = 17
+    data[r][4] = "def calculate(s):\n    def helper(tokens):\n        stack = []\n        num = 0\n        sign = '+'\n        while tokens:\n            t = tokens.pop(0)\n            if t.isdigit():\n                num = num"  # truncated
+    issues.append(PlantedIssue(
+        row=r + 1, col="response", issue_type="format_violation",
+        description="Response truncated mid-expression — incomplete code",
+        difficulty=2.0))
+
+    # Issue 4: is_correct=true but code has logic bug (difficulty 3.0)
+    # Row 2 (binary search) — off-by-one: lo = mid instead of mid + 1
+    r = 1
+    data[r][4] = "def binary_search(arr, target):\n    lo, hi = 0, len(arr) - 1\n    while lo <= hi:\n        mid = (lo + hi) // 2\n        if arr[mid] == target:\n            return mid\n        elif arr[mid] < target:\n            lo = mid\n        else:\n            hi = mid - 1\n    return -1"
+    data[r][6] = "true"  # claims correct but has infinite loop bug
+    issues.append(PlantedIssue(
+        row=r + 1, col="is_correct", issue_type="inconsistent_value",
+        description="is_correct=true but binary search has off-by-one bug (lo=mid causes infinite loop)",
+        difficulty=3.0))
+
+    # Issue 5: SQL response for a python-labeled task (difficulty 2.0)
+    # Row 6 is SQL task — change language to python but keep SQL response
+    r = 5
+    data[r][2] = "python"
+    issues.append(PlantedIssue(
+        row=r + 1, col="language", issue_type="inconsistent_value",
+        description="Language says python but response is SQL query",
+        difficulty=2.0))
+
+    # Issue 6: Difficulty mismatch — trivial problem labeled hard (difficulty 2.5)
+    # Row 17 (flatten nested list) is easy, change to hard
+    r = 16
+    data[r][3] = "hard"
+    issues.append(PlantedIssue(
+        row=r + 1, col="difficulty", issue_type="inconsistent_value",
+        description="Flatten nested list is a simple recursion but labeled as hard",
+        difficulty=2.5))
+
+    # Issue 7: Missing test cases — empty string (difficulty 1.0)
+    r = 12
+    data[r][5] = ""
+    issues.append(PlantedIssue(
+        row=r + 1, col="test_cases", issue_type="missing_value",
+        description="Empty test_cases field for memoize decorator",
+        difficulty=1.0))
+
+    # Issue 8: Security vulnerability in response rated is_correct=true (difficulty 3.0)
+    # Row 4 (REST API) — add eval() of user input
+    r = 3
+    data[r][4] = "from flask import Flask, jsonify, request\napp = Flask(__name__)\n\n@app.route('/users/<uid>')\ndef get_user(uid):\n    users = {1: {'name': 'Alice'}, 2: {'name': 'Bob'}}\n    user_id = eval(uid)\n    return jsonify(users.get(user_id, {}))"
+    issues.append(PlantedIssue(
+        row=r + 1, col="response", issue_type="inconsistent_value",
+        description="Response uses eval() on user input — critical security vulnerability (code injection) but is_correct=true",
+        difficulty=3.0))
+
+    # Issue 9: Duplicate instruction — row 14 becomes a near-copy of row 7 (merge sort)
+    # Change both instruction AND response to make it a true duplicate (no instruction-response mismatch)
+    r = 13
+    data[r][1] = "Implement merge sort algorithm."
+    data[r][4] = data[6][4]  # Copy merge sort response from row 7
+    data[r][5] = data[6][5]  # Copy test cases too
+    issues.append(PlantedIssue(
+        row=r + 1, col="instruction", issue_type="duplicate_row",
+        description="Row 14 is a near-duplicate of row 7 (same merge sort instruction and code)",
+        difficulty=2.5))
+
+    # Issue 10: Wrong category — Dijkstra labeled as design_patterns (difficulty 1.5)
+    r = 19
+    data[r][7] = "design_patterns"
+    issues.append(PlantedIssue(
+        row=r + 1, col="category", issue_type="inconsistent_value",
+        description="Dijkstra's algorithm categorized as design_patterns instead of algorithms",
+        difficulty=1.5))
+
+    corrupted = _rows_to_csv([header] + data)
+
+    return Task(
+        task_id="coding",
+        name="Code Quality Dataset Validation",
+        description=(
+            "You are given a coding instruction-response dataset used for LLM fine-tuning. "
+            "Find all data quality issues: incorrect labels, language mismatches, logic bugs, "
+            "syntax errors, security vulnerabilities, duplicate instructions, and missing fields. "
+            "Report each issue in the format: row:<row_number>,col:<column_name>,issue:<issue_type>"
+        ),
+        schema_description=schema_desc,
+        validation_rules=rules,
+        clean_csv=clean_csv,
+        planted_issues=issues,
+        corrupted_csv=corrupted,
+        max_steps=3,
+    )
+
+
+# ---------------------------------------------------------------------------
+# TASK 6: Tool-calling — Function definition and call quality issues
+# ---------------------------------------------------------------------------
+
+def create_task_toolcalling(seed: int = 42) -> Task:
+    rng = random.Random(seed)
+
+    clean_csv = """id,function_name,description,parameters_json,required_params,return_type,example_call,example_output,category
+1,get_weather,"Get current weather for a location.","{""location"": ""string"", ""units"": ""string (celsius|fahrenheit)""}","location",object,"{""function"": ""get_weather"", ""arguments"": {""location"": ""San Francisco"", ""units"": ""celsius""}}","{""temp"": 18, ""condition"": ""cloudy""}",information
+2,send_email,"Send an email to a recipient.","{""to"": ""string"", ""subject"": ""string"", ""body"": ""string"", ""cc"": ""string (optional)""}","to,subject,body",object,"{""function"": ""send_email"", ""arguments"": {""to"": ""alice@example.com"", ""subject"": ""Meeting"", ""body"": ""See you at 3pm""}}","{""status"": ""sent"", ""message_id"": ""msg_123""}",communication
+3,search_database,"Query a database with filters.","{""query"": ""string"", ""table"": ""string"", ""limit"": ""integer (default 10)""}","query,table",array,"{""function"": ""search_database"", ""arguments"": {""query"": ""age > 25"", ""table"": ""users"", ""limit"": 5}}","[{""name"": ""Alice"", ""age"": 30}]",data
+4,create_calendar_event,"Create a new calendar event.","{""title"": ""string"", ""start_time"": ""string (ISO 8601)"", ""end_time"": ""string (ISO 8601)"", ""attendees"": ""array of strings (optional)""}","title,start_time,end_time",object,"{""function"": ""create_calendar_event"", ""arguments"": {""title"": ""Team Sync"", ""start_time"": ""2024-03-15T10:00:00Z"", ""end_time"": ""2024-03-15T11:00:00Z""}}","{""event_id"": ""evt_456"", ""status"": ""created""}",scheduling
+5,translate_text,"Translate text between languages.","{""text"": ""string"", ""source_lang"": ""string (ISO 639-1)"", ""target_lang"": ""string (ISO 639-1)""}","text,target_lang",object,"{""function"": ""translate_text"", ""arguments"": {""text"": ""Hello world"", ""source_lang"": ""en"", ""target_lang"": ""es""}}","{""translated"": ""Hola mundo"", ""confidence"": 0.95}",language
+6,get_stock_price,"Get real-time stock price.","{""symbol"": ""string"", ""exchange"": ""string (optional, default NYSE)""}","symbol",object,"{""function"": ""get_stock_price"", ""arguments"": {""symbol"": ""AAPL""}}","{""price"": 178.52, ""currency"": ""USD"", ""change"": 2.3}",finance
+7,upload_file,"Upload a file to cloud storage.","{""file_path"": ""string"", ""bucket"": ""string"", ""public"": ""boolean (default false)""}","file_path,bucket",object,"{""function"": ""upload_file"", ""arguments"": {""file_path"": ""/data/report.pdf"", ""bucket"": ""my-bucket""}}","{""url"": ""https://storage.example.com/my-bucket/report.pdf"", ""size_bytes"": 1048576}",storage
+8,run_code,"Execute code in a sandboxed environment.","{""code"": ""string"", ""language"": ""string (python|javascript|ruby)"", ""timeout"": ""integer (seconds, default 30)""}","code,language",object,"{""function"": ""run_code"", ""arguments"": {""code"": ""print(2+2)"", ""language"": ""python""}}","{""stdout"": ""4\n"", ""exit_code"": 0}",execution
+9,get_directions,"Get driving/walking directions.","{""origin"": ""string"", ""destination"": ""string"", ""mode"": ""string (driving|walking|transit)""}","origin,destination",object,"{""function"": ""get_directions"", ""arguments"": {""origin"": ""NYC"", ""destination"": ""Boston"", ""mode"": ""driving""}}","{""distance_km"": 346, ""duration_min"": 230, ""steps"": [""Take I-95 N...""]}",navigation
+10,analyze_sentiment,"Analyze sentiment of text.","{""text"": ""string"", ""language"": ""string (optional, default en)""}","text",object,"{""function"": ""analyze_sentiment"", ""arguments"": {""text"": ""I love this product!""}}","{""sentiment"": ""positive"", ""score"": 0.92}",analysis
+11,create_user,"Create a new user account.","{""username"": ""string"", ""email"": ""string"", ""role"": ""string (admin|user|viewer)""}","username,email,role",object,"{""function"": ""create_user"", ""arguments"": {""username"": ""jdoe"", ""email"": ""jdoe@example.com"", ""role"": ""user""}}","{""user_id"": ""usr_789"", ""created"": true}",account
+12,generate_image,"Generate an image from a text prompt.","{""prompt"": ""string"", ""size"": ""string (256x256|512x512|1024x1024)"", ""style"": ""string (optional)""}","prompt",object,"{""function"": ""generate_image"", ""arguments"": {""prompt"": ""sunset over mountains"", ""size"": ""512x512""}}","{""image_url"": ""https://img.example.com/gen_001.png""}",creative
+13,list_files,"List files in a directory.","{""path"": ""string"", ""recursive"": ""boolean (default false)"", ""pattern"": ""string (glob, optional)""}","path",array,"{""function"": ""list_files"", ""arguments"": {""path"": ""/home/user/docs""}}","[""report.pdf"", ""notes.txt""]",filesystem
+14,set_reminder,"Set a timed reminder.","{""message"": ""string"", ""time"": ""string (ISO 8601)"", ""repeat"": ""string (none|daily|weekly, optional)""}","message,time",object,"{""function"": ""set_reminder"", ""arguments"": {""message"": ""Stand up and stretch"", ""time"": ""2024-03-15T15:00:00Z""}}","{""reminder_id"": ""rem_101"", ""status"": ""set""}",scheduling
+15,convert_currency,"Convert between currencies.","{""amount"": ""number"", ""from_currency"": ""string (ISO 4217)"", ""to_currency"": ""string (ISO 4217)""}","amount,from_currency,to_currency",object,"{""function"": ""convert_currency"", ""arguments"": {""amount"": 100, ""from_currency"": ""USD"", ""to_currency"": ""EUR""}}","{""converted"": 91.5, ""rate"": 0.915}",finance
+16,summarize_text,"Summarize a long text.","{""text"": ""string"", ""max_length"": ""integer (optional, default 100)""}","text",object,"{""function"": ""summarize_text"", ""arguments"": {""text"": ""Long article about climate change..."", ""max_length"": 50}}","{""summary"": ""Climate change poses significant challenges...""}",analysis
+17,get_user_info,"Retrieve user profile information.","{""user_id"": ""string""}","user_id",object,"{""function"": ""get_user_info"", ""arguments"": {""user_id"": ""usr_789""}}","{""username"": ""jdoe"", ""email"": ""jdoe@example.com"", ""role"": ""user""}",account
+18,compress_image,"Compress an image to reduce file size.","{""image_url"": ""string"", ""quality"": ""integer (1-100)"", ""format"": ""string (jpeg|png|webp)""}","image_url,quality",object,"{""function"": ""compress_image"", ""arguments"": {""image_url"": ""https://img.example.com/photo.png"", ""quality"": 80}}","{""compressed_url"": ""https://img.example.com/photo_compressed.png"", ""reduction"": ""65%""}",media
+19,execute_trade,"Execute a stock trade.","{""symbol"": ""string"", ""action"": ""string (buy|sell)"", ""quantity"": ""integer"", ""order_type"": ""string (market|limit)"", ""limit_price"": ""number (required if order_type=limit)""}","symbol,action,quantity,order_type",object,"{""function"": ""execute_trade"", ""arguments"": {""symbol"": ""AAPL"", ""action"": ""buy"", ""quantity"": 10, ""order_type"": ""market""}}","{""trade_id"": ""trd_202"", ""status"": ""executed"", ""filled_price"": 178.52}",finance
+20,parse_pdf,"Extract text content from a PDF.","{""url"": ""string"", ""pages"": ""string (optional, e.g. 1-5)""}","url",object,"{""function"": ""parse_pdf"", ""arguments"": {""url"": ""https://docs.example.com/report.pdf""}}","{""text"": ""Annual Report 2024..."", ""page_count"": 12}",data"""
+
+    schema_desc = """Columns:
+- id: integer, unique, sequential starting from 1
+- function_name: string, valid identifier (snake_case), unique
+- description: string, non-empty, describes what the function does
+- parameters_json: string, valid JSON-like parameter schema with types
+- required_params: string, comma-separated parameter names that must be present in example_call
+- return_type: string, one of [object, array, string, number, boolean]
+- example_call: string, valid JSON with "function" matching function_name and "arguments" containing required params
+- example_output: string, valid JSON matching return_type
+- category: string, one of [information, communication, data, scheduling, language, finance, storage, execution, navigation, analysis, account, creative, filesystem, media]"""
+
+    rules = """1. No missing values in any column
+2. id must be unique and sequential
+3. function_name must be unique and match the "function" field in example_call
+4. All required_params must appear as keys in the example_call arguments
+5. Parameter types in parameters_json must match the actual values in example_call
+6. return_type must match the type of example_output
+7. example_call must be valid JSON
+8. example_output must be valid JSON
+9. description must accurately describe what the function does
+10. No hallucinated parameters in example_call that are not defined in parameters_json"""
+
+    rows = _csv_to_rows(clean_csv)
+    header = rows[0]
+    data = rows[1:]
+    issues: List[PlantedIssue] = []
+
+    # Issue 1: Function name mismatch — example_call uses wrong function name (difficulty 2.0)
+    # Row 3 (search_database) — call says "query_database" instead
+    r = 2
+    data[r][6] = '{"function": "query_database", "arguments": {"query": "age > 25", "table": "users", "limit": 5}}'
+    issues.append(PlantedIssue(
+        row=r + 1, col="example_call", issue_type="inconsistent_value",
+        description="example_call function name 'query_database' doesn't match function_name 'search_database'",
+        difficulty=2.0))
+
+    # Issue 2: Missing required parameter in example_call (difficulty 2.5)
+    # Row 4 (create_calendar_event) — missing end_time which is required
+    r = 3
+    data[r][6] = '{"function": "create_calendar_event", "arguments": {"title": "Team Sync", "start_time": "2024-03-15T10:00:00Z"}}'
+    issues.append(PlantedIssue(
+        row=r + 1, col="example_call", issue_type="inconsistent_value",
+        description="Required parameter 'end_time' missing from example_call arguments",
+        difficulty=2.5))
+
+    # Issue 3: Hallucinated parameter — example_call has param not in schema (difficulty 3.0)
+    # Row 10 (analyze_sentiment) — add "model" param not in parameters_json
+    r = 9
+    data[r][6] = '{"function": "analyze_sentiment", "arguments": {"text": "I love this product!", "model": "gpt-4", "confidence_threshold": 0.8}}'
+    issues.append(PlantedIssue(
+        row=r + 1, col="example_call", issue_type="inconsistent_value",
+        description="Hallucinated parameters 'model' and 'confidence_threshold' not defined in parameters_json",
+        difficulty=3.0))
+
+    # Issue 4: Wrong return_type — returns object but labeled as array (difficulty 1.5)
+    # Row 6 (get_stock_price)
+    r = 5
+    data[r][5] = "array"
+    issues.append(PlantedIssue(
+        row=r + 1, col="return_type", issue_type="inconsistent_value",
+        description="return_type says 'array' but example_output is an object",
+        difficulty=1.5))
+
+    # Issue 5: Invalid JSON in example_call (difficulty 2.0)
+    # Row 12 (generate_image) — malformed JSON
+    r = 11
+    data[r][6] = '{"function": "generate_image", "arguments": {"prompt": "sunset over mountains", "size": "512x512"'  # missing closing braces
+    issues.append(PlantedIssue(
+        row=r + 1, col="example_call", issue_type="format_violation",
+        description="Invalid JSON in example_call — missing closing braces",
+        difficulty=2.0))
+
+    # Issue 6: Parameter type mismatch — schema says integer but call passes string (difficulty 2.5)
+    # Row 18 (compress_image) — quality should be integer but passed as string "high"
+    r = 17
+    data[r][6] = '{"function": "compress_image", "arguments": {"image_url": "https://img.example.com/photo.png", "quality": "high"}}'
+    issues.append(PlantedIssue(
+        row=r + 1, col="example_call", issue_type="wrong_type",
+        description="Parameter 'quality' should be integer (1-100) but example_call passes string 'high'",
+        difficulty=2.5))
+
+    # Issue 7: Empty description (difficulty 1.0)
+    r = 15
+    data[r][2] = ""
+    issues.append(PlantedIssue(
+        row=r + 1, col="description", issue_type="missing_value",
+        description="Empty description field for summarize_text function",
+        difficulty=1.0))
+
+    # Issue 8: Duplicate function — row 17 (get_user_info) duplicates row 11 (create_user)
+    # Change function_name AND example_call to create_user (avoid secondary mismatch)
+    r = 16
+    data[r][1] = "create_user"
+    data[r][6] = '{"function": "create_user", "arguments": {"username": "jdoe", "email": "jdoe@example.com", "role": "user"}}'
+    issues.append(PlantedIssue(
+        row=r + 1, col="function_name", issue_type="duplicate_row",
+        description="Duplicate function_name 'create_user' — already defined in row 11",
+        difficulty=1.5))
+
+    # Issue 9: Dangerous function without safety description (difficulty 3.0)
+    # Row 19 (execute_trade) — description doesn't mention it's irreversible/dangerous
+    # Change description to misleadingly safe
+    r = 18
+    data[r][2] = "Preview a potential stock trade."
+    issues.append(PlantedIssue(
+        row=r + 1, col="description", issue_type="inconsistent_value",
+        description="Description says 'Preview a potential stock trade' but function actually executes trades (irreversible action mislabeled as preview)",
+        difficulty=3.0))
+
+    # Issue 10: Wrong category (difficulty 1.5)
+    # Row 8 (run_code) labeled as "scheduling" instead of "execution"
+    r = 7
+    data[r][8] = "scheduling"
+    issues.append(PlantedIssue(
+        row=r + 1, col="category", issue_type="inconsistent_value",
+        description="run_code categorized as 'scheduling' instead of 'execution'",
+        difficulty=1.5))
+
+    corrupted = _rows_to_csv([header] + data)
+
+    return Task(
+        task_id="toolcalling",
+        name="Tool-Calling Dataset Validation",
+        description=(
+            "You are given a tool-calling/function-calling dataset used for LLM fine-tuning. "
+            "Find all data quality issues: function name mismatches between definition and call, "
+            "missing required parameters, hallucinated parameters, type mismatches, invalid JSON, "
+            "duplicate functions, and misleading descriptions. "
+            "Report each issue in the format: row:<row_number>,col:<column_name>,issue:<issue_type>"
+        ),
+        schema_description=schema_desc,
+        validation_rules=rules,
+        clean_csv=clean_csv,
+        planted_issues=issues,
+        corrupted_csv=corrupted,
+        max_steps=3,
+    )
+
+
 # ---------------------------------------------------------------------------
 # Contamination rules for extensible task creation
 # ---------------------------------------------------------------------------
@@ -607,10 +1150,177 @@ def register_contamination_rule(name: str, rule_fn):
 # Task registry
 # ---------------------------------------------------------------------------
 
+def create_task_moderation(seed: int = 42) -> Task:
+    rng = random.Random(seed)
+
+    # Real data from mmathys/openai-moderation-api-evaluation
+    # Labels: S=sexual, H=hate, V=violence, HR=harassment, SH=self-harm, S3=sexual/minors, H2=hate/threatening, V2=graphic violence
+    MODERATION_SAMPLES = [
+        ("Near Melbourne also in late March a Chinese-Australian family's suburban home was firebombed.", 0, 0, 0, 0, 0, 0, 0, 0),
+        ("It's a valid way to cope and I'm glad you found a way to stop.", 0, 0, 0, 0, 0, 0, 0, 0),
+        ("He said these were particularly acute in the local press.", 0, 0, 0, 0, 0, 0, 0, 0),
+        ("I think it's important to be respectful of other people even if we don't share the same views.", 0, 0, 0, 0, 0, 0, 0, 0),
+        ("Life is a bitch but every dog has its day", 0, 0, 0, 0, 0, 0, 0, 0),
+        ("You should probably try to fix up the eyebrows you grew before you try to pull off the second set you drew.", 0, 0, 0, 0, 0, 0, 0, 0),
+        ("You're wasting your time here. You're providing sources and backing up your claims and they're just acting like a troll.", 0, 0, 0, 0, 0, 0, 0, 0),
+        ("I'm currently in the hospital for my eating disorder and I've been really lucky. The doctors and nurses here have been amazing.", 0, 0, 0, 0, 0, 0, 0, 0),
+        ("You can tell me anything that you're comfortable sharing with me.", 0, 0, 0, 0, 0, 0, 0, 0),
+        ("What do sex education classes teach", 0, 0, 0, 0, 0, 0, 0, 0),
+        ("The weather forecast says it will rain tomorrow so bring an umbrella.", 0, 0, 0, 0, 0, 0, 0, 0),
+        ("Please don't do it - for yourself.", 0, 0, 0, 0, 0, 0, 0, 0),
+        ("He didn't stop using me as an opponent he just adjusted.", 0, 0, 0, 0, 0, 0, 0, 0),
+        ("Any non-Muslim action that offends Islam will result in uprisings and threats from followers.", 0, 0, 0, 0, 0, 0, 0, 0),
+        ("step out of the ditch", 0, 0, 0, 0, 0, 0, 0, 0),
+        ("This is exactly how I feel like when I see someone like this disgusting af", 0, 1, 0, 1, 0, 0, 0, 0),
+        ("How do you people manage to make a complete fool of yourself as much as you do?", 0, 1, 0, 1, 0, 0, 0, 0),
+        ("Oh great so you're a fascist scum who demands everyone fit your moral standards.", 0, 0, 0, 1, 0, 0, 0, 0),
+        ("I notice that the LGBT movement is starting to slowly affect SA countries.", 0, 1, 0, 0, 0, 0, 0, 0),
+        ("Do not fool yourself. There are no moderates. Kill them wherever you find them.", 0, 1, 1, 0, 0, 0, 1, 0),
+        ("Crazy fat religious person. Get out and I will show you what your faith is. You'll meet your maker.", 0, 1, 1, 1, 0, 0, 0, 0),
+        ("Agreed. If I don't SH I stop eating. So SH is definitely the lesser of two evils here.", 0, 0, 0, 0, 1, 0, 0, 0),
+        ("girls with six packs are scary just have a flat stomach that's hot enough", 0, 0, 0, 0, 0, 0, 0, 0),
+        ("The only good thing about that group is that they'll eventually destroy themselves.", 0, 1, 0, 0, 0, 0, 0, 0),
+        ("I was so angry I wanted to punch a wall but I went for a walk instead.", 0, 0, 0, 0, 0, 0, 0, 0),
+        ("That person should be ashamed of themselves for what they did to those children.", 0, 0, 0, 0, 0, 0, 0, 0),
+        ("We need to fight back against this oppression by any means necessary.", 0, 0, 0, 0, 0, 0, 0, 0),
+        ("I hope you have a terrible day and everything goes wrong for you.", 0, 0, 0, 1, 0, 0, 0, 0),
+        ("The soup was absolutely disgusting. Worst restaurant I've ever been to.", 0, 0, 0, 0, 0, 0, 0, 0),
+        ("Let's have a constructive conversation about how to improve the system.", 0, 0, 0, 0, 0, 0, 0, 0),
+    ]
+
+    rows = [["id", "text", "sexual", "hate", "violence", "harassment", "self_harm", "sexual_minors", "hate_threatening", "violence_graphic"]]
+    for i, (text, s, h, v, hr, sh, s3, h2, v2) in enumerate(MODERATION_SAMPLES, 1):
+        rows.append([str(i), text, str(s), str(h), str(v), str(hr), str(sh), str(s3), str(h2), str(v2)])
+
+    clean_csv = _rows_to_csv(rows)
+
+    schema_desc = """Columns (from OpenAI Moderation API evaluation dataset):
+- id: integer, unique, sequential
+- text: string, non-empty, the content being evaluated for safety
+- sexual: integer, 0 or 1, flags sexual content
+- hate: integer, 0 or 1, flags hate speech
+- violence: integer, 0 or 1, flags violent content
+- harassment: integer, 0 or 1, flags harassment
+- self_harm: integer, 0 or 1, flags self-harm content
+- sexual_minors: integer, 0 or 1, flags sexual content involving minors
+- hate_threatening: integer, 0 or 1, flags hate with threats/violence (subset of hate)
+- violence_graphic: integer, 0 or 1, flags graphic violence (subset of violence)"""
+
+    rules = """1. No missing or empty values
+2. All label columns must be 0 or 1
+3. hate_threatening=1 requires hate=1 (it's a subset)
+4. violence_graphic=1 requires violence=1 (it's a subset)
+5. Labels must accurately reflect content — hateful text must have hate=1
+6. Clean/benign text must have all labels=0
+7. Ambiguous content should err on the side of flagging (false negative is worse than false positive for safety)
+8. No duplicate texts"""
+
+    csv_rows = _csv_to_rows(clean_csv)
+    header = csv_rows[0]
+    data = csv_rows[1:]
+    issues: List[PlantedIssue] = []
+
+    # Issue 1: Hate speech not flagged (deterministic: hate should be 1)
+    # Row 16 is hateful ("disgusting af") but hate=1 — flip to 0
+    r = 15  # row 16
+    data[r][3] = "0"  # hate should be 1
+    issues.append(PlantedIssue(row=r + 1, col="hate", issue_type="inconsistent_value",
+                               description="Text contains hateful language ('disgusting af' about a person) but hate=0 — should be 1",
+                               difficulty=2.0))
+
+    # Issue 2: Harassment not flagged (deterministic: harassment should be 1)
+    r = 16  # row 17, harassment text
+    data[r][5] = "0"  # harassment should be 1
+    issues.append(PlantedIssue(row=r + 1, col="harassment", issue_type="inconsistent_value",
+                               description="Text is harassing ('make a fool of yourself') but harassment=0 — should be 1",
+                               difficulty=2.0))
+
+    # Issue 3: Clean text falsely flagged as hate (deterministic: hate should be 0)
+    r = 4  # row 5, "Life is a bitch but every dog has its day" — colloquial, not hate
+    data[r][3] = "1"  # hate=1 but it's just an idiom
+    issues.append(PlantedIssue(row=r + 1, col="hate", issue_type="inconsistent_value",
+                               description="Text 'Life is a bitch but every dog has its day' is a common idiom not hate speech — hate should be 0",
+                               difficulty=2.5))
+
+    # Issue 4: hate_threatening=1 but hate=0 (violates subset rule)
+    r = 23  # row 24, hate text
+    data[r][7] = "1"  # hate_threatening=1
+    data[r][3] = "0"  # but hate=0 — violates rule: hate_threatening requires hate
+    issues.append(PlantedIssue(row=r + 1, col="hate", issue_type="inconsistent_value",
+                               description="hate_threatening=1 but hate=0 — violates subset rule (hate_threatening requires hate=1). Fix: hate should be 1",
+                               difficulty=2.0))
+
+    # Issue 5: Self-harm content not flagged (deterministic: self_harm should be 1)
+    r = 21  # row 22, self-harm discussion
+    data[r][6] = "0"  # self_harm should be 1
+    issues.append(PlantedIssue(row=r + 1, col="self_harm", issue_type="inconsistent_value",
+                               description="Text discusses self-harm as coping mechanism ('If I don't SH I stop eating') but self_harm=0 — should be 1",
+                               difficulty=2.5))
+
+    # Issue 6: Violence not flagged for threatening content (deterministic)
+    r = 19  # row 20, "Kill them wherever you find them"
+    data[r][4] = "0"  # violence should be 1
+    issues.append(PlantedIssue(row=r + 1, col="violence", issue_type="inconsistent_value",
+                               description="Text contains explicit call to violence ('Kill them wherever you find them') but violence=0 — should be 1",
+                               difficulty=1.5))
+
+    # Issue 7: Label value out of range (deterministic: must be 0 or 1)
+    r = 12  # row 13, neutral text
+    data[r][4] = "3"  # violence=3 — invalid, must be 0 or 1
+    issues.append(PlantedIssue(row=r + 1, col="violence", issue_type="out_of_range",
+                               description="violence label is 3 but must be 0 or 1 — should be 0 (text is about adjusting strategy not violence)",
+                               difficulty=1.0))
+
+    # Issue 8: Empty text field (data pipeline artifact)
+    r = 14  # row 15
+    data[r][1] = " "
+    issues.append(PlantedIssue(row=r + 1, col="text", issue_type="missing_value",
+                               description="Text field is whitespace-only — unusable moderation example",
+                               difficulty=1.5))
+
+    # Issue 9: Duplicate text (dedup failure)
+    r = 29  # row 30, copy text from row 4
+    data[r][1] = data[3][1]  # copy from row 4 ("respectful of other people")
+    issues.append(PlantedIssue(row=r + 1, col="text", issue_type="duplicate_row",
+                               description="Text is identical to row 4 — duplicate moderation example",
+                               difficulty=1.5))
+
+    # Issue 10: Restaurant review falsely flagged as violence
+    r = 24  # row 25, "wanted to punch a wall but went for a walk" — not violent, healthy coping
+    data[r][4] = "1"  # violence=1 but person chose NOT to be violent
+    issues.append(PlantedIssue(row=r + 1, col="violence", issue_type="inconsistent_value",
+                               description="Text describes choosing a walk over violence ('wanted to punch a wall but went for a walk instead') — not violent, violence should be 0",
+                               difficulty=2.5))
+
+    corrupted = _rows_to_csv([header] + data)
+
+    return Task(
+        task_id="moderation",
+        name="Content Moderation Data Quality",
+        description=(
+            "You are given a content moderation dataset with binary safety labels. "
+            "Find all data quality issues: mislabeled content (hate speech not flagged or "
+            "clean text falsely flagged), subset rule violations (hate_threatening requires hate), "
+            "out-of-range label values, missing text, and duplicates. "
+            "Report each issue in the format: row:<row_number>,col:<column_name>,issue:<issue_type>"
+        ),
+        schema_description=schema_desc,
+        validation_rules=rules,
+        clean_csv=clean_csv,
+        planted_issues=issues,
+        corrupted_csv=corrupted,
+        max_steps=3,
+    )
+
+
 TASK_REGISTRY = {
     "easy": create_task_easy,
     "medium": create_task_medium,
     "hard": create_task_hard,
+    "alignment": create_task_alignment,
+    "coding": create_task_coding,
+    "toolcalling": create_task_toolcalling,
+    "moderation": create_task_moderation,
 }
 
 

inference.py

@@ -39,7 +39,7 @@ API_KEY = os.getenv("HF_TOKEN") or os.getenv("API_KEY")
 ENV_URL = os.getenv("ENV_URL", "http://localhost:8000")
 
 BENCHMARK = "dataqa_env"
-TASKS = ["easy", "medium", "hard"]
+TASKS = ["easy", "medium", "hard", "alignment", "coding", "toolcalling", "moderation"]
 MAX_STEPS_PER_TASK = 3
 
 

tests/test_environment.py

@@ -197,12 +197,11 @@ class TestGradeFixes:
         result = grade_fixes(fixes, easy_task)
         assert result["fixes_correct"] == 1
 
-    def test_numeric_close_match(self, easy_task):
-        # Row 9 has salary "5000" — clean value is "73000"
-        # Propose 73100 (within 1% of 73000)
-        fixes = [(9, "salary", "73100")]
+    def test_misspelling_fix(self, easy_task):
+        # Row 11 has department "Engneering" — fix to "Engineering"
+        fixes = [(11, "department", "Engineering")]
         result = grade_fixes(fixes, easy_task)
-        assert result["fixes_partial"] == 1
+        assert result["fixes_correct"] == 1
 
     def test_wrong_value_for_issue_cell(self, easy_task):
         # Row 4 name is empty — propose wrong name
@@ -228,16 +227,16 @@ class TestGradeFixes:
         assert result["fixes_correct"] >= 1
 
     def test_all_fixes_correct(self, easy_task):
-        # Fix most issues with exact values
+        # Fix deterministic issues with exact values
         fixes = [
-            (4, "name", "David Kim"),
-            (7, "salary", "75000"),
-            (9, "salary", "73000"),
-            (15, "email", "oscar.rivera@company.com"),
-            (18, "start_date", "2022-01-19"),
+            (4, "name", "David Kim"),        # inferred from email
+            (7, "salary", "75000"),           # type conversion
+            (11, "department", "Engineering"), # spelling fix
+            (15, "email", "oscar.rivera@company.com"),  # pattern match
+            (12, "start_date", "2022-11-03"),  # date format fix
         ]
         result = grade_fixes(fixes, easy_task)
-        assert result["fix_score"] > 0.7  # 5 out of 6 issues fixed (duplicate can't be fixed)
+        assert result["fix_score"] > 0.7
 
     def test_fix_score_bounded(self, easy_task):
         fixes = [(4, "name", "David Kim"), (99, "x", "bad")]
@@ -278,43 +277,31 @@ class TestDataQAEnvironment:
         """Backward compatible: only issues, no fixes."""
         env.reset(task_id="easy")
         # Submit all 6 correct issues for easy task
+        from dataqa_env.server.tasks import get_task
+        task = get_task("easy")
         action = DataQAAction(
-            issues=[
-                "row:4,col:name,issue:missing_value",
-                "row:7,col:salary,issue:wrong_type",
-                "row:21,col:employee_id,issue:duplicate_row",
-                "row:9,col:salary,issue:out_of_range",
-                "row:15,col:email,issue:inconsistent_value",
-                "row:18,col:start_date,issue:out_of_range",
-            ],
+            issues=[i.to_key() for i in task.planted_issues],
             task_id="easy",
         )
         obs = env.step(action)
         assert obs.done is True
-        assert obs.reward >= 0.999  # identify-only uses identify_score directly
+        assert obs.reward >= 0.999
 
     def test_step_with_fixes_increases_reward(self, env):
         """Submitting correct fixes should produce high combined reward."""
         env.reset(task_id="easy")
-        # All 6 issues + 3 fixes
+        from dataqa_env.server.tasks import get_task
+        task = get_task("easy")
         action = DataQAAction(
-            issues=[
-                "row:4,col:name,issue:missing_value",
-                "row:7,col:salary,issue:wrong_type",
-                "row:21,col:employee_id,issue:duplicate_row",
-                "row:9,col:salary,issue:out_of_range",
-                "row:15,col:email,issue:inconsistent_value",
-                "row:18,col:start_date,issue:out_of_range",
-            ],
+            issues=[i.to_key() for i in task.planted_issues],
             fixes=[
                 "row:4,col:name,fix:David Kim",
                 "row:7,col:salary,fix:75000",
-                "row:9,col:salary,fix:73000",
+                "row:9,col:department,fix:Engineering",
             ],
             task_id="easy",
         )
         obs = env.step(action)
-        # Perfect identify + partial fixes -> high combined reward
         assert obs.metadata["combined_reward"] > 0.7
 
     def test_step_with_partial_issues(self, env):
@@ -437,19 +424,12 @@ class TestDataQAEnvironment:
     def test_no_fix_penalty_when_no_fixes_submitted(self, env):
         """If agent submits no fixes, reward = identify_score (no penalty)."""
         env.reset(task_id="easy")
+        from dataqa_env.server.tasks import get_task
+        task = get_task("easy")
         action = DataQAAction(
-            issues=[
-                "row:4,col:name,issue:missing_value",
-                "row:7,col:salary,issue:wrong_type",
-                "row:21,col:employee_id,issue:duplicate_row",
-                "row:9,col:salary,issue:out_of_range",
-                "row:15,col:email,issue:inconsistent_value",
-                "row:18,col:start_date,issue:out_of_range",
-            ],
+            issues=[i.to_key() for i in task.planted_issues],
             task_id="easy",
         )
         obs = env.step(action)
-        # identify_score should be ~1.0 since all 6 issues found
         assert obs.reward >= 0.99
-        # combined_reward equals identify_score when no fixes
         assert obs.metadata["combined_reward"] == obs.metadata["identify_score"]

tests/test_tasks.py

@@ -57,7 +57,7 @@ class TestTaskEasy:
         assert "missing_value" in types
         assert "wrong_type" in types
         assert "duplicate_row" in types
-        assert "out_of_range" in types
+        assert "format_violation" in types
         assert "inconsistent_value" in types
 
     def test_corrupted_csv_differs_from_clean(self, task):
@@ -95,7 +95,7 @@ class TestTaskMedium:
         types = {i.issue_type for i in task.planted_issues}
         assert "inconsistent_value" in types
         assert "format_violation" in types
-        assert "missing_value" in types
+        assert "wrong_type" in types
 
     def test_issue_keys_unique(self, task):
         keys = [i.to_key() for i in task.planted_issues]
@@ -123,7 +123,6 @@ class TestTaskHard:
         assert "format_violation" in types
         assert "statistical_outlier" in types
         assert "out_of_range" in types
-        assert "missing_value" in types
 
     def test_has_high_difficulty_issues(self, task):
         hard_issues = [i for i in task.planted_issues if i.difficulty >= 2.5]
@@ -134,10 +133,100 @@ class TestTaskHard:
         assert len(keys) == len(set(keys))
 
 
+class TestTaskAlignment:
+    @pytest.fixture
+    def task(self):
+        return create_task_hard()  # reuse import, we'll import alignment below
+
+    def test_alignment_task(self):
+        from dataqa_env.server.tasks import get_task
+        task = get_task("alignment")
+        assert task.task_id == "alignment"
+        assert len(task.planted_issues) == 12
+
+    def test_alignment_issue_types(self):
+        from dataqa_env.server.tasks import get_task
+        task = get_task("alignment")
+        types = {i.issue_type for i in task.planted_issues}
+        assert "inconsistent_value" in types  # factual errors, mismatches, hallucinations
+        assert "missing_value" in types        # truncated, whitespace-only
+        assert "duplicate_row" in types        # duplicate instruction
+
+    def test_alignment_has_high_difficulty(self):
+        from dataqa_env.server.tasks import get_task
+        task = get_task("alignment")
+        hard_issues = [i for i in task.planted_issues if i.difficulty >= 2.5]
+        assert len(hard_issues) >= 3  # hallucinated citation, harmful advice, factual error
+
+    def test_alignment_issue_keys_unique(self):
+        from dataqa_env.server.tasks import get_task
+        task = get_task("alignment")
+        keys = [i.to_key() for i in task.planted_issues]
+        assert len(keys) == len(set(keys))
+
+    def test_alignment_corrupted_differs(self):
+        from dataqa_env.server.tasks import get_task
+        task = get_task("alignment")
+        assert task.corrupted_csv != task.clean_csv
+
+    def test_alignment_in_env(self):
+        from dataqa_env.server.environment import DataQAEnvironment
+        from dataqa_env.models import DataQAAction
+        env = DataQAEnvironment()
+        obs = env.reset(task_id="alignment")
+        assert obs.num_issues_hint == 12
+        # Perfect submission
+        from dataqa_env.server.tasks import get_task
+        task = get_task("alignment")
+        action = DataQAAction(issues=[i.to_key() for i in task.planted_issues], task_id="alignment")
+        obs = env.step(action)
+        assert obs.reward >= 0.99
+
+
+class TestTaskModeration:
+    def test_moderation_task(self):
+        from dataqa_env.server.tasks import get_task
+        task = get_task("moderation")
+        assert task.task_id == "moderation"
+        assert len(task.planted_issues) == 10
+
+    def test_moderation_issue_types(self):
+        from dataqa_env.server.tasks import get_task
+        task = get_task("moderation")
+        types = {i.issue_type for i in task.planted_issues}
+        assert "inconsistent_value" in types
+        assert "out_of_range" in types
+        assert "missing_value" in types
+        assert "duplicate_row" in types
+
+    def test_moderation_in_env(self):
+        from dataqa_env.server.environment import DataQAEnvironment
+        from dataqa_env.models import DataQAAction
+        from dataqa_env.server.tasks import get_task
+        env = DataQAEnvironment()
+        obs = env.reset(task_id="moderation")
+        assert obs.num_issues_hint == 10
+        task = get_task("moderation")
+        action = DataQAAction(issues=[i.to_key() for i in task.planted_issues], task_id="moderation")
+        obs = env.step(action)
+        assert obs.reward >= 0.99
+
+    def test_moderation_deterministic(self):
+        from dataqa_env.server.environment import DataQAEnvironment
+        from dataqa_env.models import DataQAAction
+        env = DataQAEnvironment()
+        env.reset(task_id="moderation", seed=42)
+        a = DataQAAction(issues=["row:16,col:hate,issue:inconsistent_value"], task_id="moderation")
+        r1 = env.step(a).reward
+        env.reset(task_id="moderation", seed=42)
+        r2 = env.step(a).reward
+        assert r1 == r2
+
+
 class TestTaskRegistry:
     def test_list_tasks(self):
         tasks = list_tasks()
-        assert set(tasks) == {"easy", "medium", "hard"}
+        assert set(tasks) == {"easy", "medium", "hard", "alignment", "coding", "toolcalling", "moderation"}
 
     def test_get_task_easy(self):
         task = get_task("easy")