| --- |
| pretty_name: "ViRel-Budget: Metadata and Derived Evaluation Artifacts" |
| language: |
| - en |
| license: apache-2.0 |
| task_categories: |
| - visual-question-answering |
| tags: |
| - vision-language-models |
| - visual-token-pruning |
| - green-ai |
| - energy-efficiency |
| - reliability |
| configs: |
| - config_name: query_manifests |
| data_files: |
| - split: prospective |
| path: data/query_manifests/prospective.jsonl |
| - split: development |
| path: data/query_manifests/development.jsonl |
| - config_name: safety_labels |
| data_files: |
| - split: development |
| path: data/safety_labels/development.jsonl |
| - split: prospective |
| path: data/safety_labels/prospective.jsonl |
| - config_name: prospective_decisions |
| data_files: |
| - split: prospective |
| path: data/controller_decisions/prospective.jsonl |
| - config_name: replication_decisions |
| data_files: |
| - split: replication |
| path: data/controller_decisions/replication.jsonl |
| - config_name: replication_membership |
| data_files: |
| - split: replication |
| path: data/replication/membership.jsonl |
| --- |
| |
| # ViRel-Budget metadata and derived evaluation artifacts |
|
|
| This release accompanies **“ViRel-Budget: Reliability-Constrained Visual-Token Budgeting for Green Vision-Language Inference,”** accepted for an oral presentation at the ACM Multimedia 2026 GreenMM workshop. |
|
|
| Authors: Sean Wan, Shilin Ou, and Luyao Zhang. |
|
|
| ## Release boundary |
|
|
| The package contains the frozen query-selection manifests, group identifiers, budget-specific behavioral-fidelity labels, prospective controller decisions, and three-draw replication membership/results used by the paper. |
|
|
| It does **not** contain source images, questions, answers, or answer options. Those materials remain with MMStar, POPE, and Visual CounterFact and must be obtained from their official providers. The query manifests provide source repository, configuration, split, and row identifiers for reconstruction. |
|
|
| ## Configurations |
|
|
| - `query_manifests`: 1,200 development and 900 group-isolated prospective selections. |
| - `safety_labels`: derived labels for two LLaVA-1.5 scales, FastV/SCOPE/Random, and four pruned budgets. |
| - `prospective_decisions`: one-call decisions and execution outcomes for the six frozen prospective controllers. |
| - `replication_membership`: three fixed, group-disjoint draws of 210 unique queries. |
| - `replication_decisions`: matched one-call decisions and measured energy/latency outcomes for the replication population. |
|
|
| ## Loading |
|
|
| ```python |
| from datasets import load_dataset |
| |
| queries = load_dataset("SeanWan05/ViRel-Budget", "query_manifests") |
| labels = load_dataset("SeanWan05/ViRel-Budget", "safety_labels") |
| prospective = load_dataset("SeanWan05/ViRel-Budget", "prospective_decisions") |
| replications = load_dataset("SeanWan05/ViRel-Budget", "replication_decisions") |
| ``` |
|
|
| GitHub repository: https://github.com/SeanWan514/ViRel-Budget |
| HuggingFace repository: https://huggingface.co/datasets/SeanWan05/ViRel-Budget |
|
|
| ## Safety-label interpretation |
|
|
| `reference_safe` means that a pruned action preserved the dense model's original answer and its responses under all eligible interventions. It is an operational behavioral-fidelity label, not proof of semantic grounding or correct causal evidence use. Task correctness is reported separately where applicable. |
|
|
| The 1,200-query development labels were used for controller fitting/calibration. The 900-query prospective labels were hidden until all controller decisions had been recorded. |
|
|
| ## Energy and carbon boundary |
|
|
| GPU energy is measured within the declared call window. Carbon values in the associated paper are estimates derived from measured energy plus disclosed grid-intensity and PUE assumptions; they are not direct emissions measurements or lifecycle assessments. |
|
|
| ## Limitations |
|
|
| The controller evidence covers LLaVA-1.5 7B and 13B only. The selected tasks come from three benchmark families. Intervention-defined fidelity may reflect uncertainty, distribution shift, or intervention artifacts, and it inherits errors from the dense reference. Results do not establish transfer to unrelated VLM backbones, hardware, or modalities. |
|
|
| ## Source datasets and licensing |
|
|
| The original ViRel-Budget metadata and derived labels/results in this repository are released under the Apache License 2.0; see [`LICENSE`](LICENSE). See [`LICENSES.md`](LICENSES.md) for the third-party boundary. Users must retrieve source content from the official upstream repositories and comply with their current terms. |
|
|
| ## Integrity |
|
|
| - `hf_source_inventory.json` records source paths, checksums, and the code-repository commit used for packaging. |
| - `validation_report.json` records structural and leakage checks. |
| - `release_manifest.json` records the checksum and size of every upload candidate file. |
| - `dataset_schema.json` documents the configurations and primary keys. |
|
|
| ## Citation |
|
|
| Final ACM citation metadata will be added after the DOI and proceedings metadata are issued. |
|
|
| ## Acknowledgments |
|
|
| Sean Wan and Shilin Ou gratefully acknowledge support from the Summer Research Scholars Program at Duke Kunshan University, under the supervision of Prof. Luyao Zhang, and from the Duke Kunshan University Library grant supporting the Open Data Contest project. |
|
|
