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pretty_name: SMILE artifacts
language:
- en
tags:
- in-context-learning
- vision-language-models
- hard-negatives
task_categories:
- visual-question-answering
- image-classification
viewer: false
---
# SMILE: CLIP embeddings, mined hard-negative pools and predictions
Artifacts for **The Value of Being Wrong: Self-Mined Visual In-Context Learning from Errors** (NeurIPS 2026 Spotlight).
Paper: [arXiv:2610.16977](https://arxiv.org/abs/2610.16977).
Code: [github.com/Tree-Shu-Zhao/SMILE](https://github.com/Tree-Shu-Zhao/SMILE).
## Contents
```
clip_embeddings/
<data>_<split>_image.pt # CLIP ViT-g-14 (laion2b_s12b_b42k) image embeddings, L2-normalized
<data>_<split>_ids.pt # the sample id of each row
hard_negatives/<data>/<org_model>/
hard_negatives.jsonl # the pool: training samples the model gets wrong, with its voted prediction
hard_negatives_predictions.jsonl # the voted prediction for every training sample
hard_negatives_stats.json
hard_negatives_params.json
predictions/<data>/<org_model>/
vicl_<k>_<negative_k>.jsonl # test predictions, with retrieved_ids and contrastive_ids
vicl_<k>_<negative_k>_params.json
```
- `<data>`: `cub`, `flowers`, `okvqa`, `vizwiz`. `<split>`: `train`, `test` (for VizWiz, `test` is its val split).
- `<org_model>`: `Qwen_Qwen2.5-VL-7B-Instruct`, `Qwen_Qwen2.5-VL-3B-Instruct`, `google_gemma-3-12b-it`,
`google_gemma-3-4b-it`.
- Pool records: `id`, `question`, `imgpath`, `gt_answer`, `voted_pred`, `vote_count`, `total_votes`, `token_f1`,
`is_correct`.
- Prediction records: `id`, `question`, `imgpath`, `pred_answer`, `gt_answer`, `k`, `retrieved_ids`,
`contrastive_ids`. The VizWiz records hold `id`, `pred_answer`, `query_prediction`, `retrieved_ids` and
`contrastive_ids`.
- The predictions are one inference run per model and dataset. VizWiz predictions are stored before the
answerability vote, which the scorer applies.
- `imgpath` is relative to the root of the code repository (`data/<dataset>/...`).
- The records contain questions and answers from CUB-200-2011, Oxford 102 Flowers, OK-VQA and VizWiz, which remain
under the terms of those datasets. No images are included.
## Usage
From the root of the code repository:
```bash
uvx --from "huggingface_hub>=1.0" hf download TreezzZ/SMILE-data --repo-type dataset --local-dir . \
--include "clip_embeddings/*" --include "hard_negatives/*" --include "predictions/*"
```
The files land where the code reads them. To score the released predictions:
```bash
bash scripts/cub/evaluate.sh predictions/cub/Qwen_Qwen2.5-VL-7B-Instruct/vicl_8_4.jsonl
bash scripts/vizwiz/evaluate.sh predictions/vizwiz/Qwen_Qwen2.5-VL-7B-Instruct/vicl_12_8.jsonl
```
## Citation
```bibtex
@misc{zhao2026smile,
title={The Value of Being Wrong: Self-Mined Visual In-Context Learning from Errors},
author={Shu Zhao and Tan Yu},
year={2026},
eprint={2610.16977},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2610.16977},
}
```
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