README.md

---
license: cc-by-4.0
language:
- en
task_categories:
- text-generation
tags:
- reasoning
- chain-of-thought
- self-correction
- llm
- math
- aime
pretty_name: Wrong-Reasoning Trace Bank (Completeness Cliff)
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: train
    path: data/*.jsonl
---

# Wrong-Reasoning Trace Bank (Completeness Cliff)

Model-generated reasoning traces used as the injection material for the pilot study
**"The completeness cliff: language models escape wrong reasoning until it's finished."**
Each row is one sampled baseline rollout, kept so its reasoning can be spliced back into a
fresh prompt to test whether a model can still recover the correct answer (pass@k).

- **Code and write-up:** https://github.com/AlvinZH04/Completeness-Cliff
- **Blog post:** https://alvinzh04.github.io/blog/completeness-cliff.html

## What is here

One JSONL file per baseline run (`data/base_<model>_<dataset>.jsonl`). The `correct == false`
rows are the **self_wrong** injection source (the model's own wrong reasoning); the
`correct == true` rows feed the **corrupted** source (a correct scaffold with numbers
perturbed near the cut) and serve as **irrelevant / cross-domain** donors for other questions.

### Schema

| field | type | meaning |
|---|---|---|
| `run` | string | baseline run this rollout came from |
| `qid` | string | question id (e.g. `aime25-3`) |
| `dataset` | string | `aime24_25`, `rg_maze`, `rg_mini_sudoku` |
| `rg_task` | string/null | reasoning-gym task name, if applicable |
| `gold` | string | correct answer |
| `sample_index` | int | which of the N=16 samples |
| `answer` | string | the model's extracted answer for this sample |
| `correct` | bool | whether `answer` matched `gold` |
| `truncated` | bool | hit the generation length cap |
| `n_tokens` | int | generated token count |
| `trace_text` | string | the reasoning channel (a reasoning model's `<think>` content; an instruct model's response). This is what gets injected. |

### Models and problems

| model | kind | AIME 24+25 | reasoning-gym |
|---|---|---|---|
| Qwen3-4B-Thinking-2507 | reasoning | yes | maze, mini-sudoku |
| Qwen3-4B-Instruct-2507 | instruct (matched sibling) | yes (+ a 32k-budget rerun) | maze |
| Gemma-4-E2B-it | hybrid (cross-family) | yes | maze |

N = 16 samples per question, official card-recommended sampling, vLLM. Per-run baseline
metrics (pass@k, adoption, truncation, token counts) are in `baseline_summaries.json`.

## Load

```python
from datasets import load_dataset
ds = load_dataset("AZH04/wrong-reasoning-traces", split="train")
wrong = ds.filter(lambda r: not r["correct"])   # the self_wrong injection traces
```

Or a single run directly:

```python
import json
rows = [json.loads(l) for l in open("data/base_qwen3-4b-thinking_aime24_25.jsonl")]
```

## Notes

- Only distilled fields are kept. The full raw rollouts (with the post-think answer text and
  token-level metadata, ~2.4 GB) are not published here; this bank preserves the injectable
  reasoning and is regenerable from the code repo.
- Question **text** is not redistributed; rows reference AIME problems by `qid` and include the
  gold answer only. AIME problems are the property of the Mathematical Association of America.
- Traces are outputs of Qwen3 (Apache-2.0) and Gemma (Gemma Terms of Use) models. The dataset
  card and structure are released CC-BY-4.0; model outputs are subject to the source models' terms.

## Citation

If you use this, please cite the project:

```bibtex
@misc{zhang2026completenesscliff,
  title        = {The completeness cliff: language models escape wrong reasoning until it's finished},
  author       = {Zhang, Alvin},
  year         = {2026},
  howpublished = {\url{https://alvinzh04.github.io/blog/completeness-cliff.html}},
  note         = {wrong\_reason notes}
}
```