--- license: cc-by-4.0 language: - en task_categories: - visual-question-answering - image-to-text tags: - vision-language-models - contextual-entrainment - context distraction - benchmark - probing - multimodal - vlm-evaluation pretty_name: "ENTRAP-VL: Entrainment Assessment Probe for Vision and Language" size_categories: - 1K ENTRAP-VL taxonomy overview

# ENTRAP-VL **Entr**ainment **A**ssessment **P**robe for **V**ision and **L**anguage — a taxonomically structured dataset for probing *dual contextual entrainment* in vision-language models. ENTRAP-VL is the instrument accompanying the paper *ENTRAP-VL: A Taxonomic Probe for Dual Contextual Entrainment in Vision-Language Models* (Goyal, Hossain, Das, Bhutani; 2026). It is designed to let the community investigate whether and how a VLM's output is pulled by auxiliary context in its input, separately for context arriving through the **textual channel** (*Textual Entrainment*) and the **visual channel** (*Visual Entrainment*). Each phenomenon is probed by a structured taxonomy of context conditions. **This dataset is a diagnostic instrument, not a leaderboard.** The taxonomy specifies conditions under which pull is anticipated; it is not a set of tasks a good model should always answer correctly. See *Intended use* below and the paper's Section 7 for the framing. --- ## Citing this dataset **Any use of this dataset in any form (research, publication, blog, benchmark tooling, presentation etc.) must cite our arXiv paper.** This is stated up front because it is the single most important obligation attached to the release. The required citation is: ```bibtex @misc{goyal2026entrapvltaxonomicprobedual, title={ENTRAP-VL: A Taxonomic Probe for Dual Contextual Entrainment in Vision-Language Models}, author={Karan Goyal and Afreen Hossain and Debojyoti Das and Vishal Bhutani}, year={2026}, eprint={2607.20092}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2607.20092}, } ``` *Paper available at .* A machine-readable `CITATION.cff` is provided at the repository root. --- ## At a glance | | Textual Entrainment | Visual Entrainment | | --- | --- | --- | | Fixed input | Image + textual query | Textual query (world-knowledge answer) | | Manipulated context | Textual (injected alongside the image-text query) | Visual (candidate images accompanying the query) | | Number of conditions | 8 | 3 | | Categories | 8 | 7 (`humans` excluded by design) | | Items per category | 100 | 100 | | Total items | 800 | 700 | | **Grand total** | | **1,500** | A single record on the **textual side** carries one image and a structured `contexts` object enumerating eight context conditions, each holding three statements. A single record on the **visual side** carries one textual query and three candidate images, one per visual condition. --- ## Repository layout ``` ENTRAP-VL/ ├── README.md (this file) ├── LICENSE (CC BY 4.0 for annotations; source licenses for images) ├── CITATION.cff (machine-readable citation metadata) ├── IMAGE_LICENSES.md (image provenance and per-source licensing) ├── data/ │ ├── textual//queries.jsonl │ ├── textual//images/ │ ├── visual//queries.jsonl │ ├── visual//images/{relatable_true,relatable_distractor,random_distractor}/ │ └── combined/ │ ├── textual_all.jsonl │ ├── visual_all.jsonl │ └── entrap_vl_all.jsonl └── docs/ ├── taxonomy.md (definitions, design, and worked examples) ├── schema.md (JSONL field specifications) └── data_statement.md (provenance, ethics, intended use) ``` Categories: `creatures`, `electronics`, `fruits`, `household_objects`, `humans`, `monuments`, `natural_landscapes`, `vehicles`. The `humans` category is intentionally excluded from the visual split; see the paper's Section 7 and `docs/data_statement.md`. --- ## Quick start ### Hugging Face `datasets` ```python from datasets import load_dataset textual = load_dataset( "goyalkaraniit/ENTRAP-VL", data_files="data/combined/textual_all.jsonl", split="train", ) visual = load_dataset( "goyalkaraniit/ENTRAP-VL", data_files="data/combined/visual_all.jsonl", split="train", ) print(len(textual), "textual items;", len(visual), "visual items") ``` ### Direct JSONL ```python import json from pathlib import Path root = Path("ENTRAP-VL/data") textual = [json.loads(l) for l in (root / "combined" / "textual_all.jsonl").read_text().splitlines() if l.strip()] visual = [json.loads(l) for l in (root / "combined" / "visual_all.jsonl").read_text().splitlines() if l.strip()] print(len(textual), "textual items;", len(visual), "visual items") ``` ### Loading a single textual example ```python import json from pathlib import Path from PIL import Image cat_dir = Path("ENTRAP-VL/data/textual/creatures") record = json.loads(cat_dir.joinpath("queries.jsonl").read_text().splitlines()[0]) image = Image.open(cat_dir / record["image_path"]) prompt = record["prompt"] for context_type, context_values in record["contexts"].items(): # context_values is a list of 3 statements for this condition for ctx in context_values: full_prompt = f"{ctx}\n{prompt}" # Feed (image, full_prompt) to your VLM; compare against ground_truth_short # to detect textual entrainment. ... ``` ### Loading a single visual example ```python import json from pathlib import Path from PIL import Image cat_dir = Path("ENTRAP-VL/data/visual/creatures") record = json.loads(cat_dir.joinpath("queries.jsonl").read_text().splitlines()[0]) prompt = record["prompt"] # The world-knowledge answer to `prompt` is record["ground_truth_short"]; # it is known independently of the image. Pull is measured relative to the # model's no-image answer, not to relatable_true (which is NOT a baseline — # see docs/taxonomy.md). for context_type, image_rel in record["images"].items(): image = Image.open(cat_dir / image_rel) # Feed (prompt, image) to your VLM; compare across all three context_types # to detect visual entrainment. ... ``` --- ## Record schema (summary) Full schema in [`docs/schema.md`](docs/schema.md). **Textual records** (8 fields): ```json { "category": "creatures", "item_id": 1, "image_path": "images/001.jpg", "prompt": "What is the dog doing?", "ground_truth_long": "The dog is running through the grass.", "ground_truth_short": "running", "contexts": { "relatable_true": ["...", "...", "..."], "relatable_contradictory": ["...", "...", "..."], "relatable_distractor": ["...", "...", "..."], "random_distractor": ["...", "...", "..."], "relatable_distractor_short": ["...", "...", "..."], "random_distractor_short": ["...", "...", "..."], "relatable_counterfactual": ["...", "...", "..."], "random_counterfactual": ["...", "...", "..."] }, "entrainment_type": "textual" } ``` **Visual records** (6 fields): ```json { "category": "creatures", "query_id": 1, "prompt": "A large gray animal with a trunk and tusks is an", "ground_truth_short": "elephant", "entrainment_type": "visual", "images": { "relatable_true": "images/relatable_true/001.jpg", "relatable_distractor": "images/relatable_distractor/001.jpg", "random_distractor": "images/random_distractor/001.jpg" } } ``` `item_id` (textual) and `query_id` (visual) are 1-indexed identifiers unique within each category. --- ## Taxonomy at a glance The taxonomy is the conceptual core of ENTRAP-VL. It is organized around two descriptive axes, both defined relative to the **item at hand** — the depicted image in the textual stream, and the textual query in the visual stream: - **Association**: *relatable* (associated with the item) vs. *random* (unassociated). - **Veracity**: *true* / *contradictory* (false of the depicted scene but possible in the world) / *counterfactual* (false in the world). The **textual stream** instantiates both axes and yields eight conditions. The **visual stream** instantiates only the association axis (an image is a perceptual referent, not a proposition, so veracity does not apply the same way) and yields three conditions. All eight textual conditions and the three visual conditions are defined and worked out with examples in [`docs/taxonomy.md`](docs/taxonomy.md). --- ## Intended use ENTRAP-VL is a diagnostic instrument for probing dual contextual entrainment in VLMs. Reduced pull under distractor conditions indicates a model behaving well by the criterion the instrument measures; large pull indicates the phenomenon the instrument is designed to detect. Neither is a general endorsement or condemnation of the model. **Results should be reported by condition rather than aggregated**, alongside the no-context (or no-image) reference measurements, so that entrainment magnitudes are interpretable. ENTRAP-VL is **not** intended for: - Training or fine-tuning models. It is a diagnostic set; training on it would both contaminate it as an evaluation instrument and misrepresent its purpose. - Measuring overall VLM quality. It measures one specific failure mode. - Any use involving the people depicted in the `humans` category beyond the evaluation of model behavior (see `docs/data_statement.md`). --- ## Ethics summary The `humans` category contains images of real people from royalty-free repositories. Every query about a person is answered by something directly observable (an action, a visible physical state, or an occupation indicated by visible cues). No query asks the model to identify *who* a person is, and no query infers protected or non-visible attributes. Where a prompt uses affect-adjacent wording, the answer is the observable action (e.g., "screaming"), not an inferred emotional state. The `humans` category is excluded entirely from the visual split, since constructing visual distractors for human subjects would require pairing additional images of identifiable people — a consent and identification concern the dataset does not incur. Full ethical framing, including scale and locale caveats, is in [`docs/data_statement.md`](docs/data_statement.md). --- ## License ENTRAP-VL uses a **split license**: - **Annotations and documentation** — all JSONL content (prompts, ground truths, context statements, identifiers, taxonomy structure) and all documentation under `docs/` and at the repository root — are released under **CC BY 4.0**. Attribution to the authors is required; the required attribution form is the arXiv citation above. - **Images** are **not** relicensed. They were sourced from royalty-free repositories (Unsplash, Pexels, Pixabay) and remain subject to the terms of their respective source licenses, which permit free use and redistribution. ENTRAP-VL does not assert copyright over the images. See [`IMAGE_LICENSES.md`](IMAGE_LICENSES.md). All images were manually verified to be free of watermarks prior to release. See [`LICENSE`](LICENSE) for the full text. --- ## Contact Karan Goyal · IIIT Delhi, India · `karang@iiitd.ac.in` Issues and corrections: please open an issue on this HuggingFace repository.