BEV 150K Decision Mix

150,000 English-language decision rows drawn from existing datasets, public records, and text-based game environments. Each row presents source-grounded input state and one or more bounded decisions inspired by the JEV CHOICE, NOUL, and SCORE contracts. The mixture aims to train models that select an option, judge a proposition, or assign an ordered score without generating a long free-form answer.

There is no validation split. The split counts differ slightly from the four input batches because 169 rows were reassigned when packaging: 166 Upworthy rows moved from test to train and three rows moved from train to test to keep repeated input states and related source groups on one side. No rows were added or dropped. The original four batches remain unchanged. The release contains 140,007 distinct normalized input states; repeated states within a split can carry different decision questions.

Decision formats

Each decision row has a state string and a questions_json string containing a questions object keyed by meaningful decision names. Question types are:

The number of questions exceeds the number of rows because a state can have several decision targets. For CHOICE, criteria maps stable answer keys to option descriptions and label is one key. For NOUL, label is a boolean. For SCORE, criteria is an ordered list of anchored levels and label is its zero-based index. A SCORE label is an ordinal target; it is not a calibrated probability or an unconstrained numeric prediction.

Domains and skills

The table uses one primary domain per row. It describes the actual released mixture, not the sizes of the upstream datasets. Each link leads to an original source or source collection.

Construction and provenance

Four independently built batches contribute 50,000; 19,997; 50,000; and 30,003 rows. Most labels come from upstream annotations, recorded outcomes, test transitions, simulator replays, or declared deterministic policies. In one 20,000-row slice, GPT-6 Luna rewrote bounded decision questions and selected source quotes from existing records; source-backed labels were kept. Other smaller Luna steps named existing response options or produced date-checkable deadline questions. The model did not author new primary source passages for this release. Some upstream corpora are themselves synthetic, template-generated, or model-generated; this is not a collection of exclusively human-written prompts.

The published rows include a primary domain category. The domain table above links the upstream sources. Row-level source identifiers, raw source downloads, and generation scripts are not included in this release.

Loading the data

data/train.parquet and data/test.parquet are the Hub-compatible viewer files. Their five columns are state, questions_json, domain, question_types, and question_count. The JSON column contains the flexible JEV-like question object. Read the decision questions with:

import json
from datasets import load_dataset

ds = load_dataset("avbiswas/bev-decision-150K")
example = ds["train"][0]
state = example["state"]
questions = json.loads(example["questions_json"])

The release contains only the two Parquet data files. Parquet is used because the heterogeneous nested question objects cannot be reliably inferred as one Arrow JSON schema.

Intended use and limits

This mixture is for training and studying bounded decision models. Its test split is a held-out portion of the mixture, not an independent benchmark suite. It includes existing benchmark families, so evaluating on those same public benchmarks requires checking record and source-family overlap first. Some choice alternatives are sampled or constructed, and answer frequencies were sometimes rebalanced; neither the test split nor SCORE outputs imply real-world calibration.

Several labels have narrower meanings than their names may suggest: a bank subscription is an observed outcome, not a causal recommendation to contact that customer; CVSS base severity is not asset-specific risk; game scores describe immediate transitions, not long-run expected return; and Taskmaster service choices are derived from annotated dialogues, not logs of executed tools. Some source judgments and LLM-rewritten questions may be noisy. English filtering was automated and should not be read as a guarantee that every excerpt is perfect English.

Attribution and redistribution

No single blanket license is asserted for this mixed-source release. The Hub metadata uses license: unknown for that reason. Consult the linked upstream sources, their papers, and their current terms before public redistribution or commercial use, and attribute every source used. In particular, the ProofWriter mirror leaves upstream redistribution rights to the user to confirm; NVD descriptions may be authored by third-party CVE reporters; and the GoEmotions release includes Reddit text. The generated question wording does not erase those upstream obligations.

Please cite this dataset's Hub page and the relevant upstream datasets or papers linked in the domain table above when using a subset.