datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
nmr-belief-cascade
BeliefCascade Branch Grid
Each row is one complete sequential belief-revision episode. The benchmark
uses a 432-condition grid: nodes per level {2, 3, 4, 5}, level counts
{3, 4, 5}, out-/in-degree complexity bands {20, 50, 80}, and revision
types {monotonic, nmr_retraction, nmr_newinfo, nmr_mixed}. There are 10
train and 50 test episodes for every condition (4,320 train / 21,600 test).
Columns
text: atoms, static dependencies, and inference policy.
belief:… See the full description on the dataset page: https://huggingface.co/datasets/leo-bjpark/nmr-belief-cascade.sci-agent-verification-cascade
Scientific Agent Verification Cascade
Public evaluation fixtures and verified aggregate results for testing whether
scientific claims keep their source, meaning, uncertainty, and verification
requirements as they move between AI agents.
This dataset accompanies the
Scientific Agent Verification Cascade
codebase. Version 0.2.0
contains synthetic evaluation data and aggregate-only results. It contains no
raw hosted-model response, private holdout identifier,
source-record… See the full description on the dataset page: https://huggingface.co/datasets/jang1563/sci-agent-verification-cascade.reasoning-sft-Nemotron-Cascade-SFT-SWE-210K
reasoning-sft-Nemotron-Cascade-SFT-SWE-210K
Converted version of nvidia/Nemotron-Cascade-SFT-SWE, filtered to thinking=True rows with exactly one valid <think>...</think> block.
Format
Each row has three columns:
input — list of dicts (conversation turns with role and content, system messages dropped, last assistant message removed)
response — assistant response string including <think> reasoning block
domain — {category}_{source} (e.g. SWE Repair_SWE-Fixer-Train)… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/reasoning-sft-Nemotron-Cascade-SFT-SWE-210K.