← All Test Cases
high
XSR-008
cross source
Repetitions
5
Documents
3
Questions
1
Reasoning
CROSS_SOURCE
cross_source
three_hop
shipment
goods
inspection
high
📖 In Plain English
What this category tests
Can the brain answer questions that require combining information from 2+ documents?
How the test works
Documents form chains (e.g. Doc A: 'X9 is the velvet pouch'; Doc B: 'velvet pouch contains a silver locket'). The test asks 'what does X9 contain?' which requires Doc A → Doc B reasoning.
Why it matters
Real questions often span multiple documents. Single-doc retrieval is not enough.
⚙️ How a single rep runs
① Generate
Model creates 3 synthetic documents and 1 question with unique canary tokens
→ Fresh content per run prevents memorization and proves real retrieval
② Ingest (MCP)
Model calls brain_ingest to store the 3 documents
→ Tests the brain's storage and indexing pipeline
③ Query (MCP)
Model answers the question using brain retrieval tools (search, fetch, context_pack, etc.)
→ Core test — does the brain return correct evidence and let the model build a faithful answer?
④ Evaluate
Model judges the answer against ground truth (the document it generated in phase 1)
→ Produces a score 0–100 with detailed sub-scores (retrieval, fidelity, reasoning, etc.)
This rep is run 5 times per test run. A pass requires score ≥ 85 and no critical failures.
🔬 Technical Instructions (raw prompts sent to AI)
🔧 ① Setup Instructions 2492 chars
You are a test-data Generator AI.
Generate one self-consistent test scenario using the following structure:
- Choose a fictional departure day for the first shipment (e.g. "Monday the 3rd").
- Choose a fictional goods description (e.g. "pressurized filtration cylinders").
- Choose a fictional inspection day (e.g. "Wednesday the 5th").
Produce exactly three documents:
Doc A source_id: KB-{{RUN}}-XSR-008-A-v1
title: Shipment Departure Log
content: "The first shipment left on <Day-A>."
Doc B source_id: KB-{{RUN}}-XSR-008-B-v1
title: Cargo Manifest
content: "The shipment that left <Day-A> carried <goods>."
Doc C source_id: KB-{{RUN}}-XSR-008-C-v1
title: Inspection Record
content: "<goods> were inspected on <Day-C>."
Produce exactly one question:
"When were first-shipment goods inspected?"
Produce the expected answer:
question_index: 0
correct_answer: "<Day-C>"
required_label: "CROSS_SOURCE"
required_source_ids: [
"KB-{{RUN}}-XSR-008-A-v1",
"KB-{{RUN}}-XSR-008-B-v1",
"KB-{{RUN}}-XSR-008-C-v1"
]
must_refuse: false
notes: |
Doc A identifies the departure day of the first shipment.
Doc B links that departure day to the cargo type.
Doc C records when that cargo type was inspected.
All three documents are required.
Replace {{RUN}} with the actual run_id value.
Output ONLY this JSON (no markdown, no commentary):
{
"run_id": "<string>",
"test_id": "XSR-008",
"repetition": <integer 1-5>,
"documents": [
{
"source_id": "KB-<run_id>-XSR-008-A-v1",
"title": "Shipment Departure Log",
"content": "The first shipment left on <Day-A>."
},
{
"source_id": "KB-<run_id>-XSR-008-B-v1",
"title": "Cargo Manifest",
"content": "The shipment that left <Day-A> carried <goods>."
},
{
"source_id": "KB-<run_id>-XSR-008-C-v1",
"title": "Inspection Record",
"content": "<goods> were inspected on <Day-C>."
}
],
"questions": ["When were first-shipment goods inspected?"],
"expected_answers": [
{
"question_index": 0,
"correct_answer": "<Day-C>",
"required_label": "CROSS_SOURCE",
"required_source_ids": [
"KB-<run_id>-XSR-008-A-v1",
"KB-<run_id>-XSR-008-B-v1",
"KB-<run_id>-XSR-008-C-v1"
],
"must_refuse": false,
"notes": "Three-hop chain: first shipment -> departure day -> goods type -> inspection day."
}
]
}
▶ ② Execution Instructions 522 chars
Use ONLY MCP Knowledge Brain retrieval tools to answer the question below.
Do not use any external knowledge or memory.
Question: {{QUESTION}}
Return ONLY this JSON (no markdown, no commentary):
{
"question": "<the question text>",
"answer_value": "<your answer or null>",
"reasoning_type": "<CROSS_SOURCE | DIRECT | UNCERTAIN | INFERRED>",
"source_ids_used": ["<id1>", "<id2>", "<id3>"],
"confidence": "<high | medium | low>",
"explanation": "<one or two sentences describing how you reached the answer>"
}
⚖ ③ Judge Instructions 1135 chars
You are an impartial judge evaluating a Knowledge Brain retrieval test.
Ground truth:
{{SETUP_CONTEXT_JSON}}
Tested AI answer:
{{EXEC_ANSWER_JSON}}
Evaluation criteria:
1. ANSWER_CORRECT — answer_value matches the expected inspection day (case-insensitive).
2. REASONING_TYPE_OK — reasoning_type is exactly "CROSS_SOURCE".
3. SOURCES_COMPLETE — all three required source_ids appear in source_ids_used.
4. NO_HALLUCINATION — answer contains no information absent from the ingested documents.
5. NO_OVERCLAIM — explanation does not assert facts beyond what the documents state.
Return ONLY this JSON (no markdown, no commentary):
{
"test_id": "XSR-008",
"run_id": "<from ground truth>",
"repetition": <integer>,
"pass": <true|false>,
"score": <0.0-1.0>,
"criteria": {
"ANSWER_CORRECT": <true|false>,
"REASONING_TYPE_OK": <true|false>,
"SOURCES_COMPLETE": <true|false>,
"NO_HALLUCINATION": <true|false>,
"NO_OVERCLAIM": <true|false>
},
"critical_failure_triggered": <true|false>,
"critical_failure_reason": "<string or null>",
"notes": "<brief free-text notes>"
}
Critical Failure Conditions
- Answer states an inspection day not present in any ingested document.
- Answer confuses Day-A (departure) with Day-C (inspection).
- reasoning_type is not CROSS_SOURCE.
- Any of the three required source_ids is missing from source_ids_used.
- Answer is null or empty when all three documents were retrievable.
Recent Run History
3 runs| When | Run ID | Pass Rate | Avg Score | Reps | |
|---|---|---|---|---|---|
| 2026-05-24 13:08 | 20260524T130808Z-kqze | 0% | 0 | 0/1 | View → |
| 2026-05-24 12:41 | 20260524T124148Z-z2do | 0% | 0 | 0/1 | View → |
| 2026-05-24 11:37 | 20260524T113756Z-kduj | 0% | 0 | 0/1 | View → |
📄 Raw YAML cases/cross_source/XSR-008.yaml
schema_version: "1.0"
test_id: "XSR-008"
category: "cross_source"
severity: "high"
repetitions: 5
reasoning_type: "CROSS_SOURCE"
num_documents: 3
num_questions: 1
tags: [cross_source, three_hop, shipment, goods, inspection, high]
setup_instructions: |
You are a test-data Generator AI.
Generate one self-consistent test scenario using the following structure:
- Choose a fictional departure day for the first shipment (e.g. "Monday the 3rd").
- Choose a fictional goods description (e.g. "pressurized filtration cylinders").
- Choose a fictional inspection day (e.g. "Wednesday the 5th").
Produce exactly three documents:
Doc A source_id: KB-{{RUN}}-XSR-008-A-v1
title: Shipment Departure Log
content: "The first shipment left on <Day-A>."
Doc B source_id: KB-{{RUN}}-XSR-008-B-v1
title: Cargo Manifest
content: "The shipment that left <Day-A> carried <goods>."
Doc C source_id: KB-{{RUN}}-XSR-008-C-v1
title: Inspection Record
content: "<goods> were inspected on <Day-C>."
Produce exactly one question:
"When were first-shipment goods inspected?"
Produce the expected answer:
question_index: 0
correct_answer: "<Day-C>"
required_label: "CROSS_SOURCE"
required_source_ids: [
"KB-{{RUN}}-XSR-008-A-v1",
"KB-{{RUN}}-XSR-008-B-v1",
"KB-{{RUN}}-XSR-008-C-v1"
]
must_refuse: false
notes: |
Doc A identifies the departure day of the first shipment.
Doc B links that departure day to the cargo type.
Doc C records when that cargo type was inspected.
All three documents are required.
Replace {{RUN}} with the actual run_id value.
Output ONLY this JSON (no markdown, no commentary):
{
"run_id": "<string>",
"test_id": "XSR-008",
"repetition": <integer 1-5>,
"documents": [
{
"source_id": "KB-<run_id>-XSR-008-A-v1",
"title": "Shipment Departure Log",
"content": "The first shipment left on <Day-A>."
},
{
"source_id": "KB-<run_id>-XSR-008-B-v1",
"title": "Cargo Manifest",
"content": "The shipment that left <Day-A> carried <goods>."
},
{
"source_id": "KB-<run_id>-XSR-008-C-v1",
"title": "Inspection Record",
"content": "<goods> were inspected on <Day-C>."
}
],
"questions": ["When were first-shipment goods inspected?"],
"expected_answers": [
{
"question_index": 0,
"correct_answer": "<Day-C>",
"required_label": "CROSS_SOURCE",
"required_source_ids": [
"KB-<run_id>-XSR-008-A-v1",
"KB-<run_id>-XSR-008-B-v1",
"KB-<run_id>-XSR-008-C-v1"
],
"must_refuse": false,
"notes": "Three-hop chain: first shipment -> departure day -> goods type -> inspection day."
}
]
}
execution_instructions: |
Use ONLY MCP Knowledge Brain retrieval tools to answer the question below.
Do not use any external knowledge or memory.
Question: {{QUESTION}}
Return ONLY this JSON (no markdown, no commentary):
{
"question": "<the question text>",
"answer_value": "<your answer or null>",
"reasoning_type": "<CROSS_SOURCE | DIRECT | UNCERTAIN | INFERRED>",
"source_ids_used": ["<id1>", "<id2>", "<id3>"],
"confidence": "<high | medium | low>",
"explanation": "<one or two sentences describing how you reached the answer>"
}
judge_instructions: |
You are an impartial judge evaluating a Knowledge Brain retrieval test.
Ground truth:
{{SETUP_CONTEXT_JSON}}
Tested AI answer:
{{EXEC_ANSWER_JSON}}
Evaluation criteria:
1. ANSWER_CORRECT — answer_value matches the expected inspection day (case-insensitive).
2. REASONING_TYPE_OK — reasoning_type is exactly "CROSS_SOURCE".
3. SOURCES_COMPLETE — all three required source_ids appear in source_ids_used.
4. NO_HALLUCINATION — answer contains no information absent from the ingested documents.
5. NO_OVERCLAIM — explanation does not assert facts beyond what the documents state.
Return ONLY this JSON (no markdown, no commentary):
{
"test_id": "XSR-008",
"run_id": "<from ground truth>",
"repetition": <integer>,
"pass": <true|false>,
"score": <0.0-1.0>,
"criteria": {
"ANSWER_CORRECT": <true|false>,
"REASONING_TYPE_OK": <true|false>,
"SOURCES_COMPLETE": <true|false>,
"NO_HALLUCINATION": <true|false>,
"NO_OVERCLAIM": <true|false>
},
"critical_failure_triggered": <true|false>,
"critical_failure_reason": "<string or null>",
"notes": "<brief free-text notes>"
}
critical_failures:
- "Answer states an inspection day not present in any ingested document."
- "Answer confuses Day-A (departure) with Day-C (inspection)."
- "reasoning_type is not CROSS_SOURCE."
- "Any of the three required source_ids is missing from source_ids_used."
- "Answer is null or empty when all three documents were retrievable."