---
license: unknown
pretty_name: IMD Oracle Outcomes
task_categories:
- text-classification
tags:
- imd
- oracle
- blockchain
- disagreement-prediction
- evaluation
size_categories:
- 1K<n<10K
configs:
- config_name: outcomes
  default: true
  data_files:
  - split: train
    path: data/train.jsonl
  - split: validation
    path: data/validation.jsonl
  - split: test
    path: data/test.jsonl
- config_name: outcomes_2026_10_13
  data_files:
  - split: train
    path: snapshots/2026-10-13/data/train.jsonl
  - split: validation
    path: snapshots/2026-10-13/data/validation.jsonl
  - split: test
    path: snapshots/2026-10-13/data/test.jsonl
---

# IMD Oracle Outcomes

Eval Engine's community dataset of 7,804 IMD oracle requests and 915,153 panel responses, collected from the [IMD API](https://api.imd.fun). Includes questions, recorded answers, outcomes and source provenance.

## Load

```python
from datasets import load_dataset
updated = load_dataset("evalengine/imd-oracle-outcomes", "outcomes_2026_10_13")
```

The current benchmark has 4,533 training, 1,349 validation and 381 test examples. Use `input_json` as model input; `label` is `0` for attested and `1` for disagreed. These labels describe protocol outcomes, not factual correctness.

The default `outcomes` configuration retains the original October 8 splits. Keep the two versions separate: their training and evaluation assignments differ.

## Files and answers

- [Full archive](resolve/main/raw/oracle-requests.jsonl.gz): all requests, panel responses and provenance. Available answers appear in `body.attestation.answer`, `body.agreement.answer` and `body.members[].answer`.
- [Split metadata](blob/main/snapshots/2026-10-13/metadata/dataset-card.json), [audit](blob/main/metadata/oracle-audit.json) and [checksums](blob/main/SHA256SUMS): counts, cutoffs, deduplication and integrity checks.

## Model benchmarks

Task: predict **attested or disagreed** from request fields only. These untuned baselines use the same frozen prompt and a 0.5 threshold. Precision and recall measure the disagreement class.

The current test has 381 examples, including 70 disagreements, from `outcomes_2026_10_13`.

| Model | Dataset used | Precision | Recall | Average precision |
| --- | --- | ---: | ---: | ---: |
| GLM 5.3 Flash | Current, test | 47.67% | 58.57% | 0.5253 |
| GLM 5.3 | Current, test | 42.22% | 81.43% | 0.4273 |
| Kimi K3 | Current, test | 47.83% | 31.43% | 0.4485 |
| GPT-6 Luna | Current, test | 41.00% | 58.57% | 0.3238 |
| GPT-6.1 Sol | Current, test | 60.44% | 78.57% | 0.6037 |

Sol completed the test. Validation was deferred after gateway errors, with saved but unscored predictions: 140/1349 validation.

The current runs requested low reasoning and a 4,096-token output limit. Sol and Luna ran through a local gateway; the GLMs and Kimi used OpenRouter. Providers and quantization differ, so this compares the recorded serving setups. The gateway did not report dollar costs. [Full results, usage and settings](blob/main/metadata/model-benchmarks-latest.json).

<details>
<summary>Current validation results</summary>

There are 1,349 validation examples but only six disagreements. One case changes recall by 16.7 percentage points. Keep validation and test scores separate: disagreement prevalence is 0.44% and 18.37%, respectively.

| Model | Dataset used | Precision | Recall | Average precision |
| --- | --- | ---: | ---: | ---: |
| GLM 5.3 Flash | Current, validation | 2.13% | 50.00% | 0.0750 |
| GLM 5.3 | Current, validation | 1.19% | 66.67% | 0.0172 |
| Kimi K3 | Current, validation | 1.36% | 50.00% | 0.0137 |
| GPT-6 Luna | Current, validation | 0.64% | 50.00% | 0.0073 |
| GPT-6.1 Sol | Current, validation; incomplete | Not scored | Not scored | Not scored |

</details>

<details>
<summary>Original dataset results</summary>

These results use the original 1,133 validation examples, including 18 disagreements. They are not directly comparable with the current splits. [Original results and settings](blob/main/metadata/model-benchmarks.json).

| Model | Dataset used | Precision | Recall | Average precision |
| --- | --- | ---: | ---: | ---: |
| Always attested | Original, Oct 8 | 0.00% | 0.00% | 0.0159 |
| Qwen3.8-27B FP8 | Original, Oct 8 | 33.33% | 5.56% | 0.0707 |
| GLM 5.3 Flash | Original, Oct 8 | 8.89% | 22.22% | 0.0587 |
| GLM 5.3 | Original, Oct 8 | 11.11% | 33.33% | 0.0720 |

</details>

The current validation and test sets have both been evaluated; no fine-tuning or outcome-driven prompt or threshold changes were used. The original test set remains unevaluated. Splits use creation time and retain one example per normalized question template. Recorded answers have not been independently verified; exclude them from outcome-prediction inputs.

License: unknown. Collected from the publicly accessible IMD API.