---
license: apache-2.0
language:
  - en
pretty_name: DriftBench v1 Scenarios
size_categories:
  - n<1K
task_categories:
  - other
tags:
  - benchmark
  - belief-tracking
  - llm-evaluation
  - faithfulness
  - long-context
  - deterministic
configs:
  - config_name: default
    data_files:
      - split: test
        path: scenarios.jsonl
---

# DriftBench v1 — Scenarios

Official v1 evaluation scenarios for **DriftBench**, a fully deterministic benchmark
for whether a system faithfully tracks **belief drift, internal conflict, and identity
transition** across a multi-turn conversation — scored with no LLM judge.

- Code, validator, and full spec: **https://github.com/simon9679/driftbench** (Apache-2.0)
- Reliability research behind the benchmark: **https://github.com/simon9679/tbg-postmortem** — a
  negative-result study on a belief-memory engine, and the falsification protocol DriftBench is
  built to satisfy.
- This dataset contains only the *inputs and ground truth*. Scoring lives in the repo.

## Scope and honesty about stage

This is an early-stage benchmark. A few things stated plainly so nobody is surprised:

- The v1 ontology is **8 concepts in a single domain** (career / founder transition).
  This is intentional and narrow, not comprehensive. Cross-domain expansion is planned for v1.1.
- `mapping_confidence` in submissions is **self-reported by the adapter and not verified**
  against source text in v1 (semantic grounding is a v2 concern).
- The **GCS** (Graph Causal Score) metric is a proxy with known fragility around zero-baseline
  edges; see `SPEC.md` in the repo for the exact thresholds and limitations. This is no longer only
  an assumption — on repeated runs GCS was the most unstable of the five metrics (see
  "Measured spread" below).
- **Known NRS defect (v1.1 fix candidate):** an empty belief graph — e.g. from a parse failure —
  currently scores **NRS = 1.00**, the maximum. A system that emits nothing is credited with
  perfect noise resistance. Found only by repeated runs; carried to v1.1 (v1 is frozen, so v1
  behaviour is unchanged).

## What's here

`scenarios.jsonl` — 7 scenarios, one per row:

| field | type | notes |
|-------|------|-------|
| `id` | string | e.g. `01_burnout_to_founder` |
| `title` | string | human-readable |
| `spec_version` | string | `1.0.0` |
| `num_turns` | int | 12–14 |
| `messages` | list | `{turn, user, assistant}` |
| `ground_truth` | struct | `belief_changes`, `conflicts`, `identity_shift`, `noise_turns` |

`ground_truth`:
- `belief_changes`: `[{core_id, direction}]` where direction ∈ `up` / `down` / `up_then_down`
- `conflicts`: list of `[source_core_id, target_core_id]` pairs
- `identity_shift`: `{from_id, to_id}`
- `noise_turns`: turn indices that should NOT move any belief (empty for most scenarios)

`ontology.json` — the frozen 8-concept v1 ontology (`ID_FOUNDER`, `ID_EMPLOYEE`,
`V_FIN_SECURITY`, `V_GROWTH`, `F_FAILURE`, `F_STAGNATION`, `G_MVP_LAUNCH`, `G_PROMOTION`).
All `core_id` values in ground truth reference these keys.

## Scenarios

| id | title | note |
|----|-------|------|
| 01 | From Burnout to Founder | |
| 02 | Promotion Track vs Founder Path | |
| 03 | From Scarcity Mindset to Financial Agency | |
| 04 | Failure Shock to Launch Commitment | |
| 05 | Launch Momentum vs Corporate Advancement | |
| 10 | The Burnout Trap — Hustle to Balance | delayed contradiction |
| 11 | Signal Through Noise | has `noise_turns` for NRS |

## Load

```python
from datasets import load_dataset

ds = load_dataset("simon9679/driftbench-v1", split="test")
print(ds[0]["id"], ds[0]["num_turns"])
print(ds[0]["ground_truth"]["conflicts"])
```

To actually *score* a system, use the validator and scorer in the GitHub repo — the metrics
(CER, GCS, BDA, ISS, NRS) are deterministic and require the zero-trust validator, which is not
part of this dataset.

## Metrics (computed in the repo, not here)

| metric | measures |
|--------|----------|
| CER | Conflict Edge Recovery (F1 vs ground-truth conflict pairs) |
| GCS | Graph Causal Score — do conflict edges precede target suppression? |
| BDA | Belief Drift Accuracy — right beliefs move the right direction |
| ISS | Identity Shift Score — target identity overtakes source |
| NRS | Noise Resistance Score — irrelevant turns don't move beliefs |

## Measured spread (repeated runs)

The scenarios are fixed, but the *systems* scored on them are not. The same model, on the same
scenarios, at `temperature=0`, with **nothing changed between runs**, still produces different
scores on repeat. Three runs of `gpt-oss-120b` gave this aggregate (whole-benchmark mean) spread:

| metric | aggregate Δ (range over 3 runs) |
|--------|---------------------------------|
| BDA | Δ0.03 |
| ISS | Δ0.07 |
| CER | Δ0.09 |
| GCS | Δ0.17 |

On **individual scenarios** the swing reaches **Δ0.50** (GCS). **Differences smaller than these
figures are indistinguishable from run-to-run noise** — do not rank systems on a single run. GCS is
the most unstable metric here, which turns the zero-baseline fragility noted above from an
assumption into a measured fact.

Three runs is a small sample and **underestimates** the true spread rather than bounding it; treat
these deltas as a floor, not a ceiling. Full per-scenario tables live in `baselines/BASELINES.md`
in the repo.

## Citation

```
@software{driftbench2026,
  title  = {DriftBench: A Deterministic Benchmark for Belief Drift and Identity Tracking},
  author = {DriftBench},
  year   = {2026},
  url    = {https://github.com/simon9679/driftbench},
  license= {Apache-2.0}
}
```