Datasets:

Muse-research
/

Ifparse-v2.0

---
license: cc0-1.0
task_categories:
- text-generation
size_categories:
- 1K<n<10K
pretty_name: IFParse v2.0
configs:
- config_name: default
  data_files:
  - split: test
    path: test.parquet
tags:
- structured-output
- json
- observability
- telemetry
- log-parsing
- benchmark
---

# IFParse v2.0

**Hostile telemetry in. Exact JSON out.**

IFParse v2.0 is a benchmark for the part of observability that demos usually skip: turning real production record shapes into a strict, normalized JSON contract with no repairs, no guessing, and no partial credit.

Every task contains a raw record, a frozen JSON Schema contract, and a reference object. The model must emit exactly that object. A single wrong type, wrong null, wrong timestamp, extra key, code fence, or explanatory sentence fails the task.

## At a glance

| | |
|---|---|
| Tasks | **1,800** |
| Families | **9** × 200 |
| Difficulty | **360 hard**, **1,080 brutal**, **360 nightmare** |
| Trap types | **117** machine-declared transformations |
| Native formats | nginx, RFC 5424 syslog, AWS CloudTrail, Kubernetes audit, Zeek conn, ArcSight CEF, Windows Sysmon XML, PostgreSQL csvlog, raw HTTP/1.1 |
| Primary metric | **perfect** — binary exact semantic match |
| Diagnostics | valid JSON, no fences, no filler, schema match, exact match, field accuracy, per-trap attribution |

## Why it exists

Most telemetry-parsing evaluations stop at "did the model produce valid JSON?" That question is too easy. A parser can emit valid JSON with the wrong null semantics, an offset timestamp that was never normalized, a status/reason pair that cannot coexist, or a policy action attached to the wrong resource type.

IFParse v2.0 measures whether a model can produce the **one exact object** that a careful parser would produce from a hostile native record.

Three properties define the difficulty:

1. **Semantic exactness, not schema compliance.** A response can validate against the schema and still be wrong. The reference must be exactly equal, including null-vs-empty, integer types, array order, timestamp precision, and nested field semantics.
2. **Causal difficulty tiers.** Every task declares a `traps` list — the specific transformations it exercises, each with a weight. The tier is derived from the trap weight, not chosen independently: `hard` carries 3–5 weight points, `brutal` 6–9, `nightmare` 10–15.
3. **Real record grammar.** Records preserve native quoting, escaping, timezone offsets, NILVALUE, XML entities, CSV quoting, CEF escaping, chunked HTTP bodies, and JSON policy documents. The model has to decode each format exactly once.

## Task structure

Each row is a JSON object:

| Field | Type | Meaning |
|---|---|---|
| `task_id` | string | Stable id, e.g. `ifparse-v2.0-edge-0000` |
| `family` | string | One of the nine family names |
| `contract_id` | string | Contract version, e.g. `edge.v1` |
| `difficulty` | string | `hard`, `brutal`, or `nightmare` |
| `traps` | array | Machine ids of the transformations the task exercises |
| `raw_format` | string | Native format label |
| `system_prompt` | string | Frozen system message |
| `prompt` | string | Frozen user prompt containing the record and contract |
| `raw_record` | string | Native record |
| `schema` | string | JSON Schema serialized as a string |
| `reference` | string | Exact reference object serialized as a string |

`schema` and `reference` are strings containing JSON. The prompt is already frozen — no templating is required to evaluate.

## Task families

| Family | Native record | Contract | What makes it hostile |
|---|---|---|---|
| `edge` | nginx extended access log | `edge.v1` | offset timestamps, percent-decoded targets, absolute-form request lines with a decoy vhost, trusted-proxy X-Forwarded-For resolution, multi-hop upstream retries, NILVALUE cache |
| `syslog` | RFC 5424 | `syslog.v1` | PRI decomposition, NILVALUE, escaped structured data, multiple SD elements, variable timestamp precision, UTF-8 messages |
| `cloud` | AWS CloudTrail JSON | `cloud.v1` | nested identity, string booleans, double-encoded policy, ARN/resource coherence, decoy `additionalEventData`, `AssumeRole` role ARN |
| `k8s` | Kubernetes audit event | `k8s.v1` | RFC3339Nano latency floor, URI/apiVersion coherence, identity-linked RBAC reasons, stage-dependent semantics, service-account groups |
| `net` | Zeek `conn` TSV | `net.v1` | unset `-` vs `(empty)`, T/F booleans, sorted sets, epoch timestamps, interval-to-ms, failed-state packet semantics |
| `sec` | ArcSight CEF | `sec.v1` | header pipe escaping, extension escaping, full 0–10 severity bands, shuffled extensions, decoy fields, `cs1..csN` label pairs |
| `win` | Sysmon event XML | `win.v1` | four event types (1/3/11/22) in one contract, subtype-driven nullability, XML entities, hash order and case, 100 ns wire timestamps |
| `db` | PostgreSQL csvlog | `db.v1` | CSV quoting, embedded newlines and quotes, IPv6 clients, duration in milliseconds, SQLSTATE/message pairing |
| `http` | raw HTTP/1.1 request | `http.v1` | chunked bodies with byte-accurate sizes and chunk extensions, obs-fold continuations, duplicate and case-variant headers, header/body promotion rules |

Field-level contracts and the full trap list with weights: [`TAXONOMY.md`](TAXONOMY.md).

## Difficulty tiers

| Tier | Per family | Global | Trap weight |
|---|---:|---:|---|
| `hard` | 40 | 360 | 3–5 |
| `brutal` | 120 | 1,080 | 6–9 |
| `nightmare` | 40 | 360 | 10–15 |

Tier is a property of the task, derived from its declared traps. The gold object is always exact.

## Scoring

`perfect` is binary:

```text
perfect = valid_json ∧ no_fences ∧ no_filler ∧ schema_match ∧ exact_match
```

Diagnostics are reported separately:

- `valid_json` — the raw response parses as JSON
- `no_fences` — no Markdown code fence
- `no_filler` — no prose before or after the JSON value
- `schema_match` — the parsed value validates against the task schema
- `exact_match` — strict semantic equality with the reference
- `field_accuracy` — path-level agreement, for error analysis only
- `by_trap` — per-trap perfect rate and field accuracy

There is no normalization of numeric strings, no coercion of `null` to `""`, and no array sorting at scoring time. The contract is the contract.

## Usage

```python
import json

rows = [json.loads(line) for line in open("test.jsonl", encoding="utf-8")]
row = rows[0]

print(row["task_id"], row["family"], row["difficulty"], row["traps"])
print(row["raw_record"])
print(json.loads(row["reference"]))
```

Predictions are JSONL records with `task_id` and `response`:

```json
{"task_id":"ifparse-v2.0-edge-0000","response":"{\"bytes\":3157}"}
```

## Splits

There is one `test` split (1,800 tasks). There is no train split.

## Data characteristics

All telemetry is synthetic.

- Domains use `example.com`, `example.net`, `example.org`, or `.invalid`.
- IPv4 and IPv6 addresses use documentation, private, loopback, or otherwise reserved ranges.
- Identities, accounts, ARNs, hostnames, process ids, session ids, and hashes are fabricated.
- No real customer data, credentials, access keys, or production telemetry are included.

The release verifier scans raw records and references for non-reserved emails and public IP addresses; the current release has zero findings.

## Limitations

- The references define one defensible normalization per task. Other production systems may choose different conventions for nulls, duplicate query keys, or array ordering.
- The benchmark covers nine common telemetry grammars. It does not cover every vendor or version of those grammars.
- The tasks are synthetic and seeded from a generator. They are not sampled from a production corpus.
- Exact match is intentionally unforgiving. Field accuracy and per-trap attribution are provided for diagnosis.
- Exact match is a hard ceiling: a model that answers 95% of individual fields correctly still fails most tasks. Report field accuracy alongside the primary metric.

## Contamination policy

The public test split is frozen. A held-out split with identical contracts, families, and tier
balance exists under a private seed and is never published; it exists so that results on the
public split can be checked against unseen rows of the same distribution. Note the standard
caveat: a held-out split prevents answer memorization, not style overfitting to the generator.

## Usage policy

- Do not train on the test split and report the result as held-out performance. If you use the data for training, disclose it.
- Do not use the benchmark as evidence that a model is safe for production ingestion.

## Citation

```bibtex
@dataset{Muse2026ifparsev2,
  title  = {IFParse v2.0: Hostile Telemetry Normalization with Exact JSON Contracts},
  author = {Muse-Research},
  year   = {2026},
  version = {2.0.0}
}
```

## License

Code is MIT. Data is CC0 1.0. See [`LICENSE`](https://huggingface.co/datasets/Muse-research/Ifparse-v2.0/blob/main/LICENSE).

	---
	license: cc0-1.0
	task_categories:
	- text-generation
	size_categories:
	- 1K<n<10K
	pretty_name: IFParse v2.0
	configs:
	- config_name: default
	data_files:
	- split: test
	path: test.parquet
	tags:
	- structured-output
	- json
	- observability
	- telemetry
	- log-parsing
	- benchmark
	---

	# IFParse v2.0

	Hostile telemetry in. Exact JSON out.

	IFParse v2.0 is a benchmark for the part of observability that demos usually skip: turning real production record shapes into a strict, normalized JSON contract with no repairs, no guessing, and no partial credit.

	Every task contains a raw record, a frozen JSON Schema contract, and a reference object. The model must emit exactly that object. A single wrong type, wrong null, wrong timestamp, extra key, code fence, or explanatory sentence fails the task.

	## At a glance

	\| \| \|
	\|---\|---\|
	\| Tasks \| 1,800 \|
	\| Families \| 9 × 200 \|
	\| Difficulty \| 360 hard, 1,080 brutal, 360 nightmare \|
	\| Trap types \| 117 machine-declared transformations \|
	\| Native formats \| nginx, RFC 5424 syslog, AWS CloudTrail, Kubernetes audit, Zeek conn, ArcSight CEF, Windows Sysmon XML, PostgreSQL csvlog, raw HTTP/1.1 \|
	\| Primary metric \| perfect — binary exact semantic match \|
	\| Diagnostics \| valid JSON, no fences, no filler, schema match, exact match, field accuracy, per-trap attribution \|

	## Why it exists

	Most telemetry-parsing evaluations stop at "did the model produce valid JSON?" That question is too easy. A parser can emit valid JSON with the wrong null semantics, an offset timestamp that was never normalized, a status/reason pair that cannot coexist, or a policy action attached to the wrong resource type.

	IFParse v2.0 measures whether a model can produce the one exact object that a careful parser would produce from a hostile native record.

	Three properties define the difficulty:

	1. Semantic exactness, not schema compliance. A response can validate against the schema and still be wrong. The reference must be exactly equal, including null-vs-empty, integer types, array order, timestamp precision, and nested field semantics.
	2. Causal difficulty tiers. Every task declares a `traps` list — the specific transformations it exercises, each with a weight. The tier is derived from the trap weight, not chosen independently: `hard` carries 3–5 weight points, `brutal` 6–9, `nightmare` 10–15.
	3. Real record grammar. Records preserve native quoting, escaping, timezone offsets, NILVALUE, XML entities, CSV quoting, CEF escaping, chunked HTTP bodies, and JSON policy documents. The model has to decode each format exactly once.

	## Task structure

	Each row is a JSON object:

	\| Field \| Type \| Meaning \|
	\|---\|---\|---\|
	\| `task_id` \| string \| Stable id, e.g. `ifparse-v2.0-edge-0000` \|
	\| `family` \| string \| One of the nine family names \|
	\| `contract_id` \| string \| Contract version, e.g. `edge.v1` \|
	\| `difficulty` \| string \| `hard`, `brutal`, or `nightmare` \|
	\| `traps` \| array \| Machine ids of the transformations the task exercises \|
	\| `raw_format` \| string \| Native format label \|
	\| `system_prompt` \| string \| Frozen system message \|
	\| `prompt` \| string \| Frozen user prompt containing the record and contract \|
	\| `raw_record` \| string \| Native record \|
	\| `schema` \| string \| JSON Schema serialized as a string \|
	\| `reference` \| string \| Exact reference object serialized as a string \|

	`schema` and `reference` are strings containing JSON. The prompt is already frozen — no templating is required to evaluate.

	## Task families

	\| Family \| Native record \| Contract \| What makes it hostile \|
	\|---\|---\|---\|---\|
	\| `edge` \| nginx extended access log \| `edge.v1` \| offset timestamps, percent-decoded targets, absolute-form request lines with a decoy vhost, trusted-proxy X-Forwarded-For resolution, multi-hop upstream retries, NILVALUE cache \|
	\| `syslog` \| RFC 5424 \| `syslog.v1` \| PRI decomposition, NILVALUE, escaped structured data, multiple SD elements, variable timestamp precision, UTF-8 messages \|
	\| `cloud` \| AWS CloudTrail JSON \| `cloud.v1` \| nested identity, string booleans, double-encoded policy, ARN/resource coherence, decoy `additionalEventData`, `AssumeRole` role ARN \|
	\| `k8s` \| Kubernetes audit event \| `k8s.v1` \| RFC3339Nano latency floor, URI/apiVersion coherence, identity-linked RBAC reasons, stage-dependent semantics, service-account groups \|
	\| `net` \| Zeek `conn` TSV \| `net.v1` \| unset `-` vs `(empty)`, T/F booleans, sorted sets, epoch timestamps, interval-to-ms, failed-state packet semantics \|
	\| `sec` \| ArcSight CEF \| `sec.v1` \| header pipe escaping, extension escaping, full 0–10 severity bands, shuffled extensions, decoy fields, `cs1..csN` label pairs \|
	\| `win` \| Sysmon event XML \| `win.v1` \| four event types (1/3/11/22) in one contract, subtype-driven nullability, XML entities, hash order and case, 100 ns wire timestamps \|
	\| `db` \| PostgreSQL csvlog \| `db.v1` \| CSV quoting, embedded newlines and quotes, IPv6 clients, duration in milliseconds, SQLSTATE/message pairing \|
	\| `http` \| raw HTTP/1.1 request \| `http.v1` \| chunked bodies with byte-accurate sizes and chunk extensions, obs-fold continuations, duplicate and case-variant headers, header/body promotion rules \|

	Field-level contracts and the full trap list with weights: [`TAXONOMY.md`](TAXONOMY.md).

	## Difficulty tiers

	\| Tier \| Per family \| Global \| Trap weight \|
	\|---\|---:\|---:\|---\|
	\| `hard` \| 40 \| 360 \| 3–5 \|
	\| `brutal` \| 120 \| 1,080 \| 6–9 \|
	\| `nightmare` \| 40 \| 360 \| 10–15 \|

	Tier is a property of the task, derived from its declared traps. The gold object is always exact.

	## Scoring

	`perfect` is binary:

	```text
	perfect = valid_json ∧ no_fences ∧ no_filler ∧ schema_match ∧ exact_match
	```

	Diagnostics are reported separately:

	- `valid_json` — the raw response parses as JSON
	- `no_fences` — no Markdown code fence
	- `no_filler` — no prose before or after the JSON value
	- `schema_match` — the parsed value validates against the task schema
	- `exact_match` — strict semantic equality with the reference
	- `field_accuracy` — path-level agreement, for error analysis only
	- `by_trap` — per-trap perfect rate and field accuracy

	There is no normalization of numeric strings, no coercion of `null` to `""`, and no array sorting at scoring time. The contract is the contract.

	## Usage

	```python
	import json

	rows = [json.loads(line) for line in open("test.jsonl", encoding="utf-8")]
	row = rows[0]

	print(row["task_id"], row["family"], row["difficulty"], row["traps"])
	print(row["raw_record"])
	print(json.loads(row["reference"]))
	```

	Predictions are JSONL records with `task_id` and `response`:

	```json
	{"task_id":"ifparse-v2.0-edge-0000","response":"{\"bytes\":3157}"}
	```

	## Splits

	There is one `test` split (1,800 tasks). There is no train split.

	## Data characteristics

	All telemetry is synthetic.

	- Domains use `example.com`, `example.net`, `example.org`, or `.invalid`.
	- IPv4 and IPv6 addresses use documentation, private, loopback, or otherwise reserved ranges.
	- Identities, accounts, ARNs, hostnames, process ids, session ids, and hashes are fabricated.
	- No real customer data, credentials, access keys, or production telemetry are included.

	The release verifier scans raw records and references for non-reserved emails and public IP addresses; the current release has zero findings.

	## Limitations

	- The references define one defensible normalization per task. Other production systems may choose different conventions for nulls, duplicate query keys, or array ordering.
	- The benchmark covers nine common telemetry grammars. It does not cover every vendor or version of those grammars.
	- The tasks are synthetic and seeded from a generator. They are not sampled from a production corpus.
	- Exact match is intentionally unforgiving. Field accuracy and per-trap attribution are provided for diagnosis.
	- Exact match is a hard ceiling: a model that answers 95% of individual fields correctly still fails most tasks. Report field accuracy alongside the primary metric.

	## Contamination policy

	The public test split is frozen. A held-out split with identical contracts, families, and tier
	balance exists under a private seed and is never published; it exists so that results on the
	public split can be checked against unseen rows of the same distribution. Note the standard
	caveat: a held-out split prevents answer memorization, not style overfitting to the generator.

	## Usage policy

	- Do not train on the test split and report the result as held-out performance. If you use the data for training, disclose it.
	- Do not use the benchmark as evidence that a model is safe for production ingestion.

	## Citation

	```bibtex
	@dataset{Muse2026ifparsev2,
	title = {IFParse v2.0: Hostile Telemetry Normalization with Exact JSON Contracts},
	author = {Muse-Research},
	year = {2026},
	version = {2.0.0}
	}
	```

	## License

	Code is MIT. Data is CC0 1.0. See [`LICENSE`](https://huggingface.co/datasets/Muse-research/Ifparse-v2.0/blob/main/LICENSE).