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README.md
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BeliefTrack is a closed-world benchmark for **Contextual Belief Management (CBM)** in multi-turn language model interactions. Each example asks a model to maintain the set of hypotheses that remain consistent with formal evidence.
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## Configurations
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| Config | Task | Model Setting | Splits |
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|---|---|---|---|
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| `task_a_7b` | Rule Discovery | 7B experiment setting | train/test |
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| `task_a_9b` | Rule Discovery | 9B thinking experiment setting | train/test |
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| `task_b_7b` | Circuit Diagnosis | 7B experiment setting | train/test |
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| `task_b_9b` | Circuit Diagnosis | 9B thinking experiment setting | train/test |
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The `7B` and `9B` names identify the experimental data-generation/evaluation setting used in the project. They do not restrict the dataset to those model sizes.
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## Failure Modes
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- `failed_stay`: redundant evidence should not change the belief state.
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- `failed_update`: a correction should replace earlier formal evidence and trigger belief recomputation.
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- `failed_isolation`: task-irrelevant noise should not affect the formal-evidence belief state.
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## Loading
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```python
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from datasets import load_dataset
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ds = load_dataset("YOUR_ORG/BeliefTrack", "task_a_9b")
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print(ds["train"][0])
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```
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You can also load directly from local files:
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```python
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from datasets import load_dataset
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ds = load_dataset(
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"json",
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data_files={
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"train": "task_a_9b/train.json",
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"test": "task_a_9b/test.json",
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},
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)
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```
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## Schema
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Each row contains:
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- `case_id`: unique example id.
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- `task`: `task_a` or `task_b`.
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- `task_name`: human-readable task name.
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- `model_setting`: `7B` or `9B`.
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- `split`: `train` or `test`.
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- `cbm_challenge_type`: `failed_stay`, `failed_update`, or `failed_isolation`.
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- `system_prompt`: system instruction shown to the model.
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- `turns_json`: JSON-encoded multi-turn formal-evidence trajectory with per-turn gold belief states when available.
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- `messages_json`: JSON-encoded training-format messages when available.
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- `target_set_json`, `oracle`, `gt_survivors_json`: symbolic verifier targets and labels when available.
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## Dataset Summary
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```json
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{
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"configs": {
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"task_a_7b": {
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"train": {
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"source": "data/belief_training_task_dataset/Task_A/7B/train/train_cases_7B.json",
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"num_rows": 1000,
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"counts_by_cbm_challenge_type": {
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"failed_stay": 500,
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"failed_update": 500
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}
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},
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"test": {
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"source": "data/belief_training_task_dataset/Task_A/7B/test/test_cases_7B.json",
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"num_rows": 200,
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"counts_by_cbm_challenge_type": {
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"failed_stay": 100,
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"failed_update": 100
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}
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}
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},
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"task_a_9b": {
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"train": {
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"source": "data/belief_training_task_dataset/Task_A/9B/train/train_cases_9B_thinking.json",
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"num_rows": 1260,
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"counts_by_cbm_challenge_type": {
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"failed_stay": 760,
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"failed_update": 500
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}
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},
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"test": {
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"source": "data/belief_training_task_dataset/Task_A/9B/test/test_cases_9B_thinking.json",
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"num_rows": 200,
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"counts_by_cbm_challenge_type": {
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"failed_stay": 100,
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"failed_update": 100
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}
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}
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},
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"task_b_7b": {
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"train": {
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"source": "data/belief_training_task_dataset/Task_B/7B/train/train_cases_7B.json",
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"num_rows": 750,
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"counts_by_cbm_challenge_type": {
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"failed_stay": 200,
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"failed_update": 550
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}
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},
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"test": {
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"source": "data/belief_training_task_dataset/Task_B/7B/test/test_cases_7B.json",
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"num_rows": 199,
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"counts_by_cbm_challenge_type": {
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"failed_stay": 100,
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"failed_update": 99
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}
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}
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},
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"task_b_9b": {
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"train": {
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"source": "data/belief_training_task_dataset/Task_B/9B/train/train_cases_9B_thinking.json",
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"num_rows": 1247,
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"counts_by_cbm_challenge_type": {
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"failed_stay": 598,
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"failed_update": 649
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}
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},
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"test": {
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"source": "data/belief_training_task_dataset/Task_B/9B/test/test_cases_9B_thinking.json",
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"num_rows": 326,
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"counts_by_cbm_challenge_type": {
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"failed_stay": 200,
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"failed_update": 126
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}
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}
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}
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}
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}
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```
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## Citation
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```bibtex
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BeliefTrack is a closed-world benchmark for **Contextual Belief Management (CBM)** in multi-turn language model interactions. Each example asks a model to maintain the set of hypotheses that remain consistent with formal evidence.
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## Citation
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```bibtex
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