README.md

metadata

dataset_info:
  features:
    - name: query
      dtype: string
    - name: image
      dtype: 'null'
    - name: annot
      dtype: string
    - name: reasoning
      dtype: 'null'
    - name: rationale
      dtype: string
    - name: cate
      dtype: string
    - name: task
      dtype: string
    - name: metadata
      dtype: string
  splits:
    - name: train
      num_bytes: 210741
      num_examples: 76
  download_size: 54390
  dataset_size: 210741
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
extra_gated_fields:
  Name: text
  Affiliation: text
  Intended use: text
tags:
  - smart-manufacturing
  - sft
  - industrial
license: other
extra_gated_prompt: >-
  This dataset is released for **research use**. Access is reviewed and granted
  **manually** by the maintainers. Please state your name, affiliation, and
  intended use.
pretty_name: PF-D13

PF-D13

The agentic-scenario layer of PHMForge, reformatted into the unified SFT schema. Each scenario becomes a T-E3 (agentic tool-use) record; Cost-Benefit and Safety/Policy scenarios additionally become a T-D1 (decision) record.

The repository name is an internal code. See Provenance below for the underlying dataset.

Records

76 records. query = scenario question, annot = ground truth (answer + acceptance criteria, with the rationale removed). reasoning is null (these scenarios ship no chain-of-thought); the scenario's native templated rationale is preserved in a dedicated rationale field — a D13-specific column beyond the unified 7-field schema. 8 scenarios built on Week2-overlap datasets (CWRU / IMS / XJTU) are excluded (cross-paper de-dup) → 67 scenarios → 76 records.

Unified SFT schema (8 fields)

field	type	meaning
query	str	the question / query / instruction
image	Image | null	always null in this dataset
annot	str | list[str]	ground-truth answer + acceptance criteria (the scenario rationale is removed and kept separately in rationale)
reasoning	str | null	always null here — these scenarios carry no chain-of-thought / thinking trace
rationale	str | null	D13-specific field. The scenario's native answer-justification — a templated one-liner derived from the ground-truth labels (e.g. “Based on ground truth RUL values from RUL_FD001.txt …”), not an LLM/CoT reasoning trace; null when the source has none
cate	"A".."E"	one of the five SFT categories (this dataset: E, D)
task	"T-xx"	unified task id (this dataset: T-E3 + T-D1)
metadata	str (JSON)	all other info; carries a "split" key when the source has train/val/test

Load

from datasets import load_dataset
ds = load_dataset("AI4Manufacturing/PF-D13")

Gated — request access on the dataset page; access is granted manually by the maintainers.

Provenance & license

This dataset is a reformatted derivative (unified SFT schema) of:

PHMForge — Evaluating LLM Agents on Industrial Prognostics through MCP-Native, Algorithm-Grounded Tools (Columbia + IBM).

• Paper: https://arxiv.org/abs/2604.01532
• Code: https://github.com/DeveloperMindset123/PHMForge-A-Scenario-Driven-Agentic-Benchmark-for-Industrial-Asset-Lifecycle-Maintenance

Refer to the upstream source for the original licensing terms; this reformatted version is shared for research use. Please cite the upstream work.

Not yet included

Raw signal layer (T-C1 / T-C2) — not yet included. PHMForge's underlying sensor-signal datasets (13 PDMBench subsets + C-MAPSS + EngineMT-QA) map to time-series tasks whose input is signal → time-series image. That encoding step is owned by the team and not yet frozen, so the signal layer is intentionally not converted here yet; it will be added once the encoding is fixed.