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license: apache-2.0
task_categories:
- text2text-generation
language:
- en
tags:
- json-repair
- schema-validation
- structured-output
- tool-calling
- agent-workflows
- constraint-dsl
size_categories:
- 100K<n<1M
---
# StructFix-Bench
A benchmark for **schema-aware structured output recovery** — repairing broken outputs from agents, tool calls, and LLM workflows.
## What it tests
Unlike JSON syntax repair benchmarks, StructFix-Bench focuses on **semantic and schema-level recovery**:
- Replacing invalid enum values with valid ones
- Adding missing required fields
- Correcting type mismatches
- Recovering tool call arguments from Python syntax
- Reconstructing outputs from truncated agent chains
- Extracting JSON from markdown/text wrappers
## Dataset splits
| Split | Examples | Field names | Enums |
|-------|---------|-------------|-------|
| `train` | 200,000 | Mixed (semantic + shuffled) | Mixed (realistic + synthetic) |
| `validation` | 20,000 | Semantic | Realistic |
| `test_seen` | 10,000 | Semantic | Realistic |
| `test_unseen` | 10,000 | Semantic | Realistic |
| `test_random_fields` | 10,000 | **Random hex** (`f_8f31a7`) | Synthetic |
## Corruption types (28 total)
### Syntactic (10)
`single_quotes`, `unquoted_keys`, `trailing_comma`, `markdown_fences`, `extra_text_before`, `extra_text_after`, `truncated_object`, `truncated_array`, `missing_bracket`, `missing_brace`
### Semantic (9)
`wrong_type`, `invalid_enum`, `missing_required`, `null_required`, `extra_field`, `boolean_as_string`, `number_with_unit`, `synonym_enum`, `date_format_error`
### LLM-like (3)
`markdown_explanation`, `partial_structure`, `thought_process`
### Tool/Agent (6)
`tool_call_bad_format`, `tool_call_text_mix`, `tool_call_python_syntax`, `tool_call_partial_args`, `tool_call_wrong_param`, `agent_chain`
## Input format
Each example uses **ConstraintDSL** to represent the schema:
```
TASK repair_structured_output
SPEC
FIELD priority TYPE string VALUES low|medium|high REQUIRED yes
FIELD description TYPE string REQUIRED yes
BROKEN_OUTPUT
{"priority":"urgent"}
```
Target: `{"priority":"high","description":""}`
## Key results
| Method | Schema Success (unseen) | Schema Success (random fields) |
|--------|:----------------------:|:----------------------------:|
| json-repair | 65.2% | 64.0% |
| CodeT5+ + JSON Schema | 55.0% | — |
| **CodeT5+ + ConstraintDSL** | **96.3%** | **91.9%** |
## Schema ablation study
Full ablation results are included in `results/all_results.json`:
| DSL variant | Schema Success |
|------------|:--------------:|
| Full ConstraintDSL | 96.3% |
| No DSL at all | 78.4% |
| Dummy DSL | 75.0% |
| Conflicting DSL | 88.3% |
| Shuffled field names | 73.4% |
| Shuffled enum values | 89.8% |
| Shuffled required flags | 93.2% |
| Minimal DSL | 82.5% |
| Field names only | 72.2% |
## Known limitations
The benchmark uses synthetically generated schemas and corruptions. A showcase validation with 25 hand-crafted real-world examples revealed that model performance degrades when field names overlap with training vocabulary — the model may substitute field names (e.g., `action` → `active`, `contract_id` → `consign_id`). See the model card for details.
## Files
- `train.jsonl` / `validation.jsonl` / `test_seen.jsonl` / `test_unseen.jsonl` / `test_random_fields.jsonl`
- `results/all_results.json` — all experiment results including ablations
## Each example contains
```json
{
"input": "TASK repair_structured_output\n\nSPEC\n...",
"target": "{\"priority\":\"high\",\"description\":\"\"}",
"corruption_type": "invalid_enum",
"schema": {"type": "object", "properties": {...}, "required": [...]},
"invalid_json": "{\"priority\":\"urgent\"}",
"error": "Field 'priority' has invalid enum value 'urgent'",
"target_json": "{\"priority\":\"high\",\"description\":\"\"}"
}
```
## Citation
```bibtex
@software{structfix_bench,
title = {StructFix-Bench: A Benchmark for Schema-Aware Structured Output Recovery},
author = {Ottema},
year = {2026},
url = {https://huggingface.co/datasets/ottema/structfix-bench}
}
```
## License
Apache-2.0
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