| --- |
| license: apache-2.0 |
| task_categories: |
| - question-answering |
| - text-generation |
| tags: |
| - reasoning |
| - evaluation |
| - math |
| - benchmark-results |
| - model-outputs |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # Forecast-Generalization: raw evaluation outputs across 38 reasoning models |
|
|
| Complete generation-level outputs, per-seed scores and analysis artifacts from a |
| study of how well benchmark performance **forecasts generalization** to held-out |
| reasoning tasks. |
|
|
| Most released evaluations report only aggregate accuracy. This release keeps the |
| **raw per-problem, per-seed generations**, so item-level analyses can be redone |
| without re-running any inference. |
|
|
| ## What is here |
|
|
| **38 models** evaluated on **four competition-style reasoning benchmarks**, each |
| run with **five random seeds** (42, 123, 456, 789, 1024). |
|
|
| | benchmark | evaluation runs | |
| |---|---| |
| | HMMT | 36 | |
| | GPQA | 30 | |
| | AIME | 30 | |
| | IMO | 22 | |
|
|
| Model families covered include OLMo-3 (7B/32B, Think and Think-SFT variants), |
| DeepSeek-R1 distillations (Llama-8B/70B, Qwen-7B/14B/32B), Qwen3, Gemma-4, |
| Seed-OSS-36B, Kimi, MiniMax and others — spanning roughly 7B to 70B parameters |
| plus several API-served models. |
|
|
| ## Layout |
|
|
| | path | contents | |
| |---|---| |
| | `outputs/` | one directory per (benchmark, model, run): per-seed `*_predictions_seed-N.tsv` and `*_logs_seed-N.txt` | |
| | `expert_outputs/` | reference/expert model generations, grouped by `benchmark_model` | |
| | `scores/` | per-model accuracy CSVs: `name, seed_42 … seed_1024, mean_accuracy, std_error, num_seeds` | |
| | `data/` | benchmark inputs | |
| | `proxy_results/*.tar.gz` | proxy-metric evaluation results (next-token-prediction losses, sweeps) | |
| | `plots.tar.gz` | generated figures | |
| | `MANIFEST.tsv` | per-archive file counts, byte sizes and sha256 | |
|
|
| ## Why the tarballs |
|
|
| `proxy_results/` and `plots/` together hold about 129,000 small JSON and image |
| files. Uploaded raw they would exceed practical per-repository file counts, so |
| they are stored as gzipped tars split along their natural subset boundaries. |
|
|
| Every archive was verified with `tar -d` (a member-by-member diff against the |
| source filesystem) after creation, and `MANIFEST.tsv` records the source file |
| count, archive member count, size and sha256 for each. Extraction restores the |
| original tree exactly: |
|
|
| ```bash |
| tar -xzf proxy_results/ntp_results.tar.gz |
| ``` |
|
|
| ## Example: seed variance on AIME |
|
|
| ```python |
| import pandas as pd |
| from huggingface_hub import hf_hub_download |
| |
| p = hf_hub_download("dvader13/forecastgen-artifacts", |
| "scores/aime_scores_seeds.csv", repo_type="dataset") |
| df = pd.read_csv(p) |
| print(df.sort_values("mean_accuracy", ascending=False) |
| [["name", "mean_accuracy", "std_error"]].head()) |
| ``` |
|
|
| Each row is one model; the five `seed_*` columns give per-seed accuracy, so |
| seed-level variance is directly measurable rather than inferred. |
|
|
| ## Intended uses |
|
|
| - Item-level analysis (which problems a model solves, not just how many) |
| - Seed-variance and evaluation-reliability studies |
| - Correlating cheap proxy metrics against downstream benchmark accuracy |
| - Cross-model comparison on identical problems and identical seeds |
|
|
| ## Notes |
|
|
| - Generations are raw model output, unfiltered, and include incorrect answers — |
| that is deliberate, since error analysis is a main intended use. |
| - Benchmark problem statements belong to their original sources and retain the |
| licenses of those benchmarks; the license above applies to the outputs and |
| analysis artifacts collected here. |
|
|
