Datasets:
config string | results_root string | expected_cells int64 | validated_cells int64 | missing_cells int64 | predictions_per_complete_cell int64 | validated_predictions int64 | bootstrap_draws int64 | seed int64 |
|---|---|---|---|---|---|---|---|---|
configs/paper_runs_t128.json | results_128 | 281 | 281 | 0 | 12,800 | 3,596,800 | 10,000 | 20,260,728 |
Shared-emergence ICL replication at T=128
This dataset contains the complete raw result archive for the paper “Many Next-Token Predictors are In-Context Learners.”
The campaign evaluates a fixed suite of 100 program-synthesis tasks using 128 sampled prompts per task, for every clean and deranged shot cell described by the paper:
- 21 run keys;
- 281 experiment cells;
- 12,800 predictions per cell;
- 3,596,800 predictions in total.
The archive expands to a top-level results_128/ directory. It contains raw
per-task and per-trial JSON records, including predictions, exact-match
correctness, edit distances, and run configuration metadata. Summary JSON
files are present for the Evo2 runner but are not counted as experiment cells.
Use the code, complete run manifest, exact model revisions, strict validator,
analysis scripts, and per-file hashes in
N8python/shared-emergence-icl-modalities.
Integrity
Archive: icl_128_results_2026-07-28.tar.gz
SHA-256:
aa4c7b5331092582b14ce7cc1c025ef55f4a05a26696299cd69973e8da942931
Compressed size: 718,954,210 bytes.
The repository fetcher verifies both the byte size and SHA-256 before extracting, and the validator checks the complete 281-cell grid and all 3,596,800 trial records.
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