Datasets:

jingyongai
/

executable-knowledge-lifecycles-benchmark

pretty_name: Executable Knowledge Lifecycles Benchmark
license: apache-2.0
language:
  - en
  - zh
task_categories:
  - text-classification
tags:
  - agent-systems
  - causal-reasoning
  - structured-generation
  - prompt-injection
  - reproducibility
configs:
  - config_name: default
    data_files:
      - split: test
        path: benchmark.jsonl

Executable Knowledge Lifecycles Benchmark

This dataset is the frozen semantic benchmark for Experiments 1A and 1B of Executable Knowledge Lifecycles as Dynamical Control in Agent Systems. It tests whether an untrusted natural-language parser can be placed behind a typed, model-external compilation boundary without acquiring validation authority.

Version: 0.2.6-exp.1-final
Version DOI: 10.5281/zenodo.21361204
Source release: GitHub v0.2.6-exp.1-final
Release collection: Executable Knowledge Lifecycles
Author: Yongjing Gao

Dataset structure

benchmark.jsonl contains one row per independently authored semantic case:

Field	Meaning
`case_id`	Stable case identifier
`domain`	Synthetic application-domain label
`cohort`	Preregistered core or cross-domain extension
`source_text`	Natural-language input treated as untrusted data
`gold_ir`	Frozen human-authored candidate causal IR
`critical_paths`	Executable fields used for severe-error scoring
`decode_replicates`	Decode seeds used for this case
`perturbation_context`	Long-context interference injected during evaluation

The repository also includes the causal-claim JSON Schema and the three frozen evaluation JSON files.

Counts and non-independence disclosure

Unique semantic cases: 18
Preregistered core cases: 3, with 30 decode replicates per case
Cross-domain extension cases: 15, with 20 decode replicates per case
Total decoding events: 390

The 390 decoding events are repeated stochastic decodes, not 390 independent semantic samples. The cross-domain cases are manually authored templates rather than samples from a naturally occurring business distribution.

Intended use

The benchmark supports:

causal-direction, lag, validity-window, and prompt-injection regression tests;
strict JSON and required-field evaluation;
SemanticHash and EnvelopeHash conformance checks;
adversarial additions that preserve the published schema contract.

Severe semantic error

A severe semantic error is an output that reverses causal direction, changes a required timing or intervention semantic, fabricates protected provenance, grants itself validation authority, or otherwise disagrees with the frozen Gold IR on a case's critical_paths.

Limitations and safety

Gold IR is a human annotation target; it is not proof that a causal claim is true.
Deterministic source anchors are a system capability, not a claim about the bare model.
The study uses synthetic, linear, small-scale environments.
Domain labels including clinical safety and credit risk do not make these examples suitable for medical, financial, industrial, or other safety-critical deployment.
In the frozen experiment, E_OOD and E_inv are operationally derived from the same intervention-style error measurement and are not independent mechanisms.

See the repository's LIMITATIONS.md before interpreting the metrics.

Citation

@software{gao_2026_executable_knowledge_lifecycles,
  author    = {Yongjing Gao},
  title     = {Executable Knowledge Lifecycles as Dynamical Control in Agent Systems},
  version   = {0.2.6-exp.1-final},
  year      = {2026},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.21361204},
  url       = {https://doi.org/10.5281/zenodo.21361204}
}