| --- |
| 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} |
| } |
| ``` |
|
|
