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d3b8fff | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 | # RealSR Benchmark v2
## 1. Directory layout
```
hf_realsr_benchmark_v2/
├── harness/evaluation/ # shared, generic — one copy for all tasks
│ ├── eval_formula.py # execution engine: run a formula → raw metrics
│ └── evaluate.py # driver: `reference` / `score` modes
└── <primary>/<task_id>/
├── metadata.yaml # task definition (see §3)
├── prep_data.py # data_raw/ → data/
├── data_raw/ # raw data + download.sh + parse script
├── reference/ # paper PDFs + .txt + summary_*.md
├── data/ # Type I : train.csv + test.csv
│ # Type II: train.csv + test_fit.csv + test_test.csv
└── formulas/
├── <paper>.py # one reference-baseline formula per file
├── __init__.py # REGISTRY of the formula bank
└── reference_metrics.json # GENERATED by `evaluate.py reference`
```
A task directory holds **no evaluation code** — only inputs (data,
formulas, metadata). The harness is generic and takes a task directory
as an argument.
---
## 2. The `formula.py` contract
What a reference baseline — or an SR submission — exposes:
```python
USED_INPUTS = ["x1", "x2", ...] # input column names actually used
LAW_CONSTANTS = {"c_mu": 0.087, ...} # the scientific claim — paper-published,
# frozen. The only field scored by the
# LLM-judge constant channel. May be {}.
OTHER_CONSTANTS = {"e2": 1.44, ...} # every other constant the formula consumes
# (universal physics, unit factors, fixed
# operator coefficients). Declared so audits
# can count them; NOT scored. No bare `_FOO`
# module constants — declare everything here.
LOCAL_FITTABLE = {} # Type I: empty.
# Type II: {"r": {"init": ...}, ...}
# init = scalar (single start)
# | list (multi-start seeds)
# | None (closed-form fit)
def fit(X_fit, y_fit, **law_constants): # Type II only — omit for Type I
return {"r": ...} # keys MUST equal LOCAL_FITTABLE.keys()
def predict(X, **params): # params = LAW_CONSTANTS ∪ this cluster's LOCAL
# NO group_id argument — structurally prevents lookup-table cheating.
# OTHER_CONSTANTS are read from the module namespace, not passed as kwargs.
return y_pred
```
The harness performs **zero fitting on `train.csv`**. LAW_CONSTANTS are
frozen paper values (reference) or LLM-submitted values (submission).
LOCAL_FITTABLE parameters are fitted by the submission's own `fit()`,
once per Type II cluster.
---
## 3. `metadata.yaml` fields
```yaml
task_id / domain / license # identity
type: typeI | typeII # drives the evaluation path
has_group_id: bool
context: >- … # prompt slot — background knowledge only
# (no instructions, no columns, no priors)
target: {name, symbol, unit, description, range}
inputs: [{name, symbol, unit, description, range}, ...] # also the data_description source
data_files: {train, test} or {train, test_fit, test_test}
n_train / n_test / ...
priors: # prompt slot — candidate constants
- {name, value, unit, description, source, _role} # _role: candidate | distractor
references: # the reference-baseline bank
- {id, label, formula_file, reference_pdf,
n_law_constants, n_other_constants, n_local_params,
measured: {<metric>: value, ...}} # measured on test — hand-recorded
caps: {max_law_constants, max_local_params, # mirror the reference bank
max_init_size_per_param, fit_timeout_seconds}
metric: rmse | r2 | mdape | ... # scoring metric (task-declared)
best_baseline: <number> # metric value of the strongest reference
# — the 0.5 score anchor
```
**Not in metadata**: `task_description` (a global prompt, identical for
every task) and `data_description` (rendered from `target` + `inputs` at
prompt-assembly time). No `reference_baseline_id` / `naive_predictor` —
the harness anchors scoring on the empirically best reference.
---
## 4. The prompt — a 4-slot template
The prompt shown to an SR system is assembled from four slots, so any one
can be dropped for ablation experiments:
| Slot | Source | Content |
|---|---|---|
| `task_description` | global (not per-task) | contract instructions — what to submit |
| `context` | `metadata.context` | domain background knowledge — the science |
| `data_description` | rendered from `target` + `inputs` | what the data columns are |
| `priors` | `metadata.priors` | candidate constants, genuine + distractors |
`priors` lists genuinely useful constants mixed with distractors; both
candidates and distractors must be traceable to the reference materials.
The SR system must *select* which priors are relevant.
---
## 5. Train / test split
| Type | Split | Data files |
|---|---|---|
| Type I | feature-axis OOD (e.g. nuclear: proton number Z ≤ 82 vs ≥ 83) | `train.csv`, `test.csv` |
| Type II | cluster holdout — whole clusters held out — plus a within-cluster fit/test split | `train.csv`, `test_fit.csv`, `test_test.csv` |
For Type II, `test_fit.csv` / `test_test.csv` both carry `group_id`; the
harness groups by it. `submission.fit()` sees a cluster's `test_fit` rows;
`submission.predict()` is scored on the disjoint `test_test` rows.
All data must be real measurements (anti-fabrication: reference PDFs
physically present, every cited constant locatable in its paper, data
scripts actually run).
---
## 6. Evaluation — `evaluate.py`
```
# build the reference anchors (task setup, run once):
python harness/evaluation/evaluate.py reference <primary>/<task_id>
# score a submitted formula.py:
python harness/evaluation/evaluate.py score <primary>/<task_id> <submission.py>
# no submission → self-test: score each reference baseline as a submission
python harness/evaluation/evaluate.py score <primary>/<task_id>
```
`reference` mode runs the reference bank through `eval_formula`, writes
`formulas/reference_metrics.json` (raw metrics + derived caps).
`score` mode = contract check → `eval_formula` → reference-relative score.
**Scoring model** — anchored on the empirically best reference baseline
(no naive baseline):
```
lower-is-better metric: score = clip(1 - 0.5 * sub / best_ref, 0, 1)
higher-is-better (r2) : score = clip(0.5 + 0.5 * (sub - ref)/(1 - ref), 0, 1)
best reference baseline → 0.5
perfect prediction → 1.0
error = 2 × best ref → 0
```
Type II computes the score per cluster and takes the equal-weight mean.
The LLM-judge channels (constant recovery, form match) are not yet wired —
`total` currently equals the numeric score.
---
## 7. Anti-dump caps
A submission may be at most as complex as the most complex reference
formula. The harness **derives** the caps from the reference bank (no
hand-set values):
```
max_law_constants = max len(LAW_CONSTANTS) over the bank
max_local_params = max len(LOCAL_FITTABLE) over the bank
max_init_size_per_param = max init-array length (floor 1)
fit_timeout_seconds = slowest reference fit × safety factor (Type II);
None for Type I
```
Combined with the no-`group_id` `predict` signature, these block the
regression-dump failure mode (a fitted lookup table masquerading as a
formula).
---
## 8. Tasks present
| Task | Type | Size | Reference bank | Status |
|---|---|---|---|---|
| `astronomy/nuclear_binding_energy_ame2020__BE_per_A` | I | 2078 train / 470 test | weizsacker_1935, duflo_1995, moller_1995, wang_2014 | complete |
| `social_science/income_distribution_wid__pareto_alpha` | II | 47k rows / 40 test clusters | atkinson_2011, gabaix_2016, reed_2001 | metadata pending v2 convergence |
---
## 9. Known TODO
- The global `task_description` prompt text (§4 slot 1) is not yet written.
- `data_description` rendering from `target` + `inputs` is not yet
implemented in the harness (§4 slot 3).
- LLM-judge channels (constant recovery, form match) are stubbed in
`evaluate.py`; `total` currently equals the numeric score.
- This README is now the authoritative v2 spec — the design proposals it
superseded have been removed. `CLAUDE.md` and `task_formulation.html`
still reference the deleted v0.5 proposal and need reconciliation.
|