diff --git a/.gitattributes b/.gitattributes index bed0738c7eeb449bca98b5d2f33c89a1ee56349a..00cc615e8836a3c49dcca0770180d0c49269a399 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,60 +1,2 @@ -*.7z filter=lfs diff=lfs merge=lfs -text -*.arrow filter=lfs diff=lfs merge=lfs -text -*.avro filter=lfs diff=lfs merge=lfs -text -*.bin filter=lfs diff=lfs merge=lfs -text -*.bz2 filter=lfs diff=lfs merge=lfs -text -*.ckpt filter=lfs diff=lfs merge=lfs -text -*.ftz filter=lfs diff=lfs merge=lfs -text -*.gz filter=lfs diff=lfs merge=lfs -text -*.h5 filter=lfs diff=lfs merge=lfs -text -*.joblib filter=lfs diff=lfs merge=lfs -text -*.lfs.* filter=lfs diff=lfs merge=lfs -text -*.lz4 filter=lfs diff=lfs merge=lfs -text -*.mds filter=lfs diff=lfs merge=lfs -text -*.mlmodel filter=lfs diff=lfs merge=lfs -text -*.model filter=lfs diff=lfs merge=lfs -text -*.msgpack filter=lfs diff=lfs merge=lfs -text -*.npy filter=lfs diff=lfs merge=lfs -text -*.npz filter=lfs diff=lfs merge=lfs -text -*.onnx filter=lfs diff=lfs merge=lfs -text -*.ot filter=lfs diff=lfs merge=lfs -text *.parquet filter=lfs diff=lfs merge=lfs -text -*.pb filter=lfs diff=lfs merge=lfs -text -*.pickle filter=lfs diff=lfs merge=lfs -text -*.pkl filter=lfs diff=lfs merge=lfs -text -*.pt filter=lfs diff=lfs merge=lfs -text -*.pth filter=lfs diff=lfs merge=lfs -text -*.rar filter=lfs diff=lfs merge=lfs -text -*.safetensors filter=lfs diff=lfs merge=lfs -text -saved_model/**/* filter=lfs diff=lfs merge=lfs -text -*.tar.* filter=lfs diff=lfs merge=lfs -text -*.tar filter=lfs diff=lfs merge=lfs -text -*.tflite filter=lfs diff=lfs merge=lfs -text -*.tgz filter=lfs diff=lfs merge=lfs -text -*.wasm filter=lfs diff=lfs merge=lfs -text -*.xz filter=lfs diff=lfs merge=lfs -text -*.zip filter=lfs diff=lfs merge=lfs -text -*.zst filter=lfs diff=lfs merge=lfs -text -*tfevents* filter=lfs diff=lfs merge=lfs -text -# Audio files - uncompressed -*.pcm filter=lfs diff=lfs merge=lfs -text -*.sam filter=lfs diff=lfs merge=lfs -text -*.raw filter=lfs diff=lfs merge=lfs -text -# Audio files - compressed -*.aac filter=lfs diff=lfs merge=lfs -text -*.flac filter=lfs diff=lfs merge=lfs -text -*.mp3 filter=lfs diff=lfs merge=lfs -text -*.ogg filter=lfs diff=lfs merge=lfs -text -*.wav filter=lfs diff=lfs merge=lfs -text -# Image files - uncompressed -*.bmp filter=lfs diff=lfs merge=lfs -text -*.gif filter=lfs diff=lfs merge=lfs -text -*.png filter=lfs diff=lfs merge=lfs -text -*.tiff filter=lfs diff=lfs merge=lfs -text -# Image files - compressed -*.jpg filter=lfs diff=lfs merge=lfs -text -*.jpeg filter=lfs diff=lfs merge=lfs -text -*.webp filter=lfs diff=lfs merge=lfs -text -# Video files - compressed -*.mp4 filter=lfs diff=lfs merge=lfs -text -*.webm filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text diff --git a/ATTRIBUTION.md b/ATTRIBUTION.md new file mode 100644 index 0000000000000000000000000000000000000000..224f8fcf884d56f23e60266a858c8df10070044b --- /dev/null +++ b/ATTRIBUTION.md @@ -0,0 +1,53 @@ +# Upstream attribution and modifications + +License sources checked on **2026-09-28**. License texts and download URLs are +recorded in `licenses/`; pinned construction resources are recorded in +`protocol/source-resources.json`. + +**PlotQA: Reasoning over Scientific Plots** — Nitesh Methani, Pritha Ganguly, +Mitesh M. Khapra, and Pratyush Kumar, WACV 2020. +[Official dataset/code license statement](https://github.com/NiteshMethani/PlotQA#license) +and [pinned source instructions](https://github.com/NiteshMethani/PlotQA/blob/e0f5c34acbe92753f70798aafda99184e3e0cd8c/PlotQA_Dataset.md). +Dataset: [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). +The construction used the pinned +[Dodon mirror](https://huggingface.co/datasets/Dodon/plotqa-dataset/tree/6e97b674c6afcc28f79b90108e4d736b08bb2966) +as a transport source, retaining official upstream terms. Charts are re-rendered +from source tables; target values, visibility and referents are changed under +explicit programs while question text is preserved. The official PlotQA paper +describes source data collected from real-world organizations; this package +does not assert ownership of those underlying facts or additional third-party +rights. Numeric table provenance remains in the per-world metadata. + +**CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual +Reasoning** — Justin Johnson, Bharath Hariharan, Laurens van der Maaten, +Li Fei-Fei, C. Lawrence Zitnick, and Ross Girshick, CVPR 2017. +[Official data and license statement](https://cs.stanford.edu/people/jcjohns/clevr/). +Dataset: [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). +[Official generator](https://github.com/facebookresearch/clevr-dataset-gen/tree/f0ce2c81750bfae09b5bf94d009f42e055f2cb3a). +Scenes are freshly rendered with the official Blender 2.79b/Cycles assets, with +source-program attribute changes and object visibility interventions. New label +and edit evidence is supplied separately from the upstream data. + +**GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question +Answering** — Drew A. Hudson and Christopher D. Manning, CVPR 2019. +[Official source downloads](https://cs.stanford.edu/people/dorarad/gqa/download.html) +and [official question-generator repository](https://github.com/dorarad/gqa). +No explicit annotation redistribution grant was located on these pages or in +the downloaded `questions1.2.zip` and `sceneGraphs.zip` README files. The public +candidate uses source references and local reconstruction. The reconstruction +normalizes source JPEGs and applies constant gray patches at source object +boxes; symbolic world edits and source-program alternatives support its labels. + +**Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image +Annotations** — Ranjay Krishna and collaborators, IJCV 2017. +[Official data page](https://homes.cs.washington.edu/~ranjay/visualgenome/api.html) +links [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). +This is the source of GQA photographs and the basis of its scene annotations. +Visual Genome's notice is retained as provenance, not used to infer a license +for all GQA-authored annotations. No photographs are bundled in this candidate. + +The project software is the Explicit Learning project (2026), Apache 2.0 as +declared by its package metadata. The dataset release and documentation are +prepared by Sungguk Cha, Mintae Kim, Youngsub Han, Byoung-Ki Jeon, and Sangyeob +Lee, using the manuscript author list. No upstream author endorses these edits +or the reported model results by virtue of this attribution. diff --git a/CITATION.bib b/CITATION.bib new file mode 100644 index 0000000000000000000000000000000000000000..d2958af35cae7355ee93bf564f19411e1610ca16 --- /dev/null +++ b/CITATION.bib @@ -0,0 +1,32 @@ +@misc{cha2026visualanswerability, + title = {When Does an Image Determine the Answer? Benchmarking Visual Answerability across Charts and Scenes}, + author = {Cha, Sungguk and Kim, Mintae and Han, Youngsub and Jeon, Byoung-Ki and Lee, Sangyeob}, + year = {2026}, + url = {https://huggingface.co/datasets/sungguk/visual-answerability}, + version = {1.0.0}, + note = {Dataset release answerability-v1.0.0-20260928; Hub tag v1.0.0} +} + +@inproceedings{methani2020plotqa, + title = {PlotQA: Reasoning over Scientific Plots}, + author = {Methani, Nitesh and Ganguly, Pritha and Khapra, Mitesh M. and Kumar, Pratyush}, + booktitle = {WACV}, year = {2020} +} + +@inproceedings{johnson2017clevr, + title = {CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning}, + author = {Johnson, Justin and Hariharan, Bharath and van der Maaten, Laurens and Li, Fei-Fei and Zitnick, C. Lawrence and Girshick, Ross}, + booktitle = {CVPR}, year = {2017} +} + +@inproceedings{hudson2019gqa, + title = {GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering}, + author = {Hudson, Drew A. and Manning, Christopher D.}, + booktitle = {CVPR}, year = {2019} +} + +@article{krishna2017visualgenome, + title = {Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations}, + author = {Krishna, Ranjay and Zhu, Yuke and Groth, Oliver and Johnson, Justin and Hata, Kenji and Kravitz, Joshua and Chen, Stephanie and Kalantidis, Yannis and Li, Li-Jia and Shamma, David A. and Bernstein, Michael S. and Fei-Fei, Li}, + journal = {International Journal of Computer Vision}, year = {2017} +} diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 0000000000000000000000000000000000000000..2b477b3199728d4e4c13112a30026abbbd418907 --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,20 @@ +cff-version: 1.2.0 +message: "Cite this frozen dataset and the upstream datasets in ATTRIBUTION.md." +type: dataset +title: "Visual Answerability across Charts and Scenes" +version: "answerability-v1.0.0-20260928" +authors: + - family-names: Cha + given-names: Sungguk + - family-names: Kim + given-names: Mintae + - family-names: Han + given-names: Youngsub + - family-names: Jeon + given-names: Byoung-Ki + - family-names: Lee + given-names: Sangyeob +date-released: 2026-09-28 +url: "https://huggingface.co/datasets/sungguk/visual-answerability" +repository-code: "https://huggingface.co/datasets/sungguk/visual-answerability/tree/v1.0.0" +abstract: "Frozen evaluated cohorts, bounded chart witnesses, scene edit evidence, and scoring replay for visual answerability. Hub release v1.0.0." diff --git a/EVALUATION.md b/EVALUATION.md new file mode 100644 index 0000000000000000000000000000000000000000..f9391911fd9667eac6a66ec1acea81e7f3928739 --- /dev/null +++ b/EVALUATION.md @@ -0,0 +1,95 @@ +# Evaluate new predictions and replay the paper + +Install `requirements.txt` in a Python 3.12+ environment. The verified runtime +and exact package versions are in `protocol/verified-runtime.json`. All commands +below run on CPU, use local files, and make no model/API calls. Run from this +dataset folder, with outputs outside it so the frozen manifest stays valid. + +## New model predictions + +Use only `image` and `question` as model inputs. Read the exact prompt and schema +from `protocol/prompt.json`. Obtain GQA inputs using `RECONSTRUCTION.md` first. +Write one JSON object per input in `predictions.jsonl`: + +```json +{"item_id":"item-0001","answerable":false,"answer":null,"reason":"The needed value is not visible."} +``` + +That is a format example, not a prescribed answer for other inputs. Retain each +stable item ID. For a terminal malformed model output use: + +```json +{"item_id":"item-0001","status":"invalid_schema","terminal_invalid":true,"parsed":null} +``` + +One row is required for every selected input. Do not submit both examples for +the same ID. Missing/duplicate/unexpected IDs or incomplete state groups cause +the scorer to fail rather than silently shrink the denominator. Invalid final +outputs count as failures. A valid answerable decision with a null answer can +pass decision scoring but fails answer correctness. Unanswerable decisions +must have a null answer and all valid outputs need a nonempty reason. + +```bash +python scripts/score_hf_dataset.py --predictions ../predictions.jsonl \ + --sources plotqa clevr gqa --output ../scores.json +``` + +For a single subset, supply only its predictions and e.g. `--sources plotqa`. +Output includes exact counts, per-state failures, per-view decision accuracy, +all-decisions-correct groups, supported-answer accuracy, class-balanced failure, +and complete joint answer/abstention success. Chart answers use the paper's +conservative exact-rational parser (no numeric tolerance). Scene scoring first +checks exact rationals, then Unicode NFKC, case and whitespace normalization; +it does not infer synonyms. A percentage marker follows the historical parser +and is not automatically divided by 100. + +The GQA flag in the metadata defines the same 376-group location sensitivity +stratum as the paper. The supplied saved-response replay reports both strata. + +## Reproduce reported statistics + +```bash +python scripts/replay_hf_statistics.py --output ../replay +``` + +This checks all 72,000 scoring projections, recomputes all 18 model/domain cells +and the paper's 10,000-draw paired group bootstrap intervals, compares the full +analysis JSON to the frozen expected result, regenerates tables and chart +diagnostics, verifies the 5,000 chart view-to-proof identity joins, and runs the +new-prediction scorer against all six saved configurations. It also checks the +finite-study counts and enumeration/SMT agreement from saved per-case records. +It does not regenerate the finite solver-study witnesses, raw generations or +historical serving traces. GQA question-ID tokens replace upstream question +text consistently in numerical replay; reconstruction checks the actual text. + +The supplementary shortcut and answer-error counts can also be checked directly: + +```bash +python scripts/verify_argument_evidence.py --inputs inputs \ + --finite-results evidence/finite/original-results.jsonl.gz \ + --solver-results evidence/finite/solver-results.jsonl.gz \ + --output ../supplementary-counts.json +``` + +```bash +python scripts/verify_hf_dataset.py --workers 8 --output ../verification.json +``` + +This verifies the frozen file manifest, loads all three configurations through +Hugging Face Datasets, decodes and hashes all 9,000 bundled images, checks every +state group and unique source-image identity, verifies all 1,000 complete chart +proofs against their evaluated PNGs, and replays chart/CLEVR source programs. +The GQA source-program and image check is run by the separate reconstruction +command. A small machine may reduce `--workers`. + +To run only the chart proof checker: + +```bash +python scripts/verify_bounded_witness_bundle.py \ + --proofs evidence/plotqa/bounded-witness-proofs.jsonl.gz \ + --workers 8 --output ../chart-proof-verification.json +``` + +The prepared package includes historical counts plus current validation reports. +Reports are evidence of the checks they describe, not a guarantee about every +upstream annotation or a substitute for independent scientific review. diff --git a/INFERENCE_ENVIRONMENTS.json b/INFERENCE_ENVIRONMENTS.json new file mode 100644 index 0000000000000000000000000000000000000000..24c8301efb30afec525508edbcf604dc74dd5ed7 --- /dev/null +++ b/INFERENCE_ENVIRONMENTS.json @@ -0,0 +1,85 @@ +{ + "scope": "Historical inference evidence; separate from the pinned current CPU reproduction environment", + "models": { + "qwen38-flash-next": { + "model": "Qwen/Qwen3.8-Flash-Next", + "temperature": 0, + "max_tokens": 512, + "serving_engine": "vLLM (historical manuscript record)", + "runtime_versions": null, + "checkpoint_revision": null, + "provenance_limit": "Per-run raw/native contracts are not in the portable numerical archive; unknown versions are not inferred.", + "chart_checkpoint_revision": "de4b8e4d43b917e7706784d8bb445c9af86a3540", + "chart_decoding": { + "temperature": 0, + "top_p": 1, + "max_tokens": 512, + "structured_output": "json_schema", + "thinking": false, + "seed": 20260914 + }, + "chart_runtime": { + "vllm": "0.1.dev20073+g8e685d198", + "transformers": "5.15.1", + "dtype": "bfloat16", + "batch": 32, + "tensor_parallel_size": 2, + "max_model_len": 16384, + "enforce_eager": false, + "gpu_memory_utilization": 0.9, + "ple_cpu_offload": true + }, + "chart_source_record": "results/visual_benchmark_20260915/qwen-next-full/summary.json" + }, + "gemma4-31b-it": { + "model": "google/gemma-4-31B-it", + "temperature": 0, + "max_tokens": 512, + "serving_engine": "vLLM (historical manuscript record)", + "runtime_versions": null, + "checkpoint_revision": null, + "provenance_limit": "Per-run raw/native contracts are not in the portable numerical archive; unknown versions are not inferred." + }, + "qwen": { + "model": "Qwen/Qwen3.8-27B-FP8", + "temperature": 0, + "max_tokens": 512, + "serving_engine": "vLLM (historical manuscript record)", + "runtime_versions": null, + "checkpoint_revision": null, + "provenance_limit": "Per-run raw/native contracts are not in the portable numerical archive; unknown versions are not inferred.", + "scene_native_checkpoint_revision": "017b9c7af6b5689d5dd426a76e0bc077eb5ca20a", + "bridge_checkpoint_revision": null, + "scene_native_settings": "inputs/crossdomain/qwen-backend-provenance.json" + }, + "gemma4-26b-a4b-it": { + "model": "google/gemma-4-26B-A4B-it", + "temperature": 0, + "max_tokens": 512, + "serving_engine": "vLLM (historical manuscript record)", + "runtime_versions": null, + "checkpoint_revision": null, + "provenance_limit": "Per-run raw/native contracts are not in the portable numerical archive; unknown versions are not inferred." + }, + "glm": { + "model": "GLM-5.3-Flash", + "temperature": 0, + "max_tokens": 512, + "serving_engine": "vLLM (historical manuscript record)", + "runtime_versions": null, + "checkpoint_revision": null, + "provenance_limit": "Per-run raw/native contracts are not in the portable numerical archive; unknown versions are not inferred." + }, + "molmo2-8b": { + "model": "allenai/Molmo2-8B", + "temperature": 0, + "max_tokens": 512, + "serving_engine": "vLLM (historical manuscript record)", + "runtime_versions": null, + "checkpoint_revision": null, + "provenance_limit": "Per-run raw/native contracts are not in the portable numerical archive; unknown versions are not inferred." + } + }, + "raw_token_archives_bundled": false, + "new_inference_performed": false +} diff --git a/LICENSE.md b/LICENSE.md new file mode 100644 index 0000000000000000000000000000000000000000..631a8c0737ae48ddc17d96ee089a808c5a2dfa9f --- /dev/null +++ b/LICENSE.md @@ -0,0 +1,41 @@ +# Component licenses + +This is an aggregation with distinct component terms, not a blanket relicensing +of upstream content. The card's `other` license points here. + +- **PlotQA-derived images, questions, tables and evidence:** Creative Commons + Attribution 4.0 International. The official PlotQA README explicitly applies + CC BY 4.0 to its datasets; its MIT license applies to code/models. Preserve + the PlotQA attribution and the description of our re-rendering and edits. +- **CLEVR-derived images, questions, scenes, programs and evidence:** Creative + Commons Attribution 4.0 International, as stated on the official CLEVR page. + Preserve the CLEVR attribution and identify these as modified/re-rendered + evaluation views. CLEVR generator code/assets have their own BSD license; + the upstream generator/assets are obtained separately, not bundled here. +- **Release-authored labels, edit recipes, documentation, benchmark metadata + and scoring projections:** CC BY 4.0, to the extent the project holds rights. + This does not grant additional rights over source datasets or model weights. +- **Project software under `src/` and `scripts/`:** Apache License 2.0, + following the existing project's `pyproject.toml` declaration. Upstream + attribution must remain intact. External dependencies retain their licenses + and are installed separately; their binaries and model weights are absent. +- **GQA upstream questions, full programs, scene graphs and photographs:** + not redistributed. The official GQA site and downloaded archive READMEs + checked on 2026-09-28 do not state an explicit annotation redistribution + license. A local configuration's `cc-by-4.0` declaration and third-party + mirror cards are insufficient evidence to settle that grant. Users obtain + these inputs directly from the upstream source under its applicable terms. + The GQA index contains our labels, factual source IDs and our edit deltas. +- **Manuscripts:** not bundled. Their publication/license selection is separate + from this dataset preparation. + +Full license texts are in `licenses/CC-BY-4.0.txt` and +`licenses/Apache-2.0.txt`. The upstream PlotQA MIT and CLEVR BSD notices are +also retained for clarity; they do not replace the dataset CC BY terms. + +CC BY users must credit the creators, link the license, identify modifications, +retain applicable notices, and not imply upstream endorsement. No extra legal +or technical restriction is placed on the rights granted by that license. +The official Visual Genome site links CC BY 4.0, but this does not resolve the +separate GQA annotation grant. Original image-owner and other third-party rights +are not overridden. 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/dev/null +++ b/PROTOCOL.md @@ -0,0 +1,50 @@ +# Frozen model protocol and environment + +`protocol/prompt.json` holds the exact user prompt template and requested JSON +schema. Present one image and the unchanged source question independently; +group states only after inference. All six reported configurations used +temperature 0 and a 512-token output cap through vLLM-based serving. The saved +analysis treats them as serving configurations; it does not equate API and +local checkpoint revisions that lack matching provenance. + +| Scoring key | Paper name | Model identifier | +|---|---|---| +| `qwen38-flash-next` | Qwen 3.8 Flash Next | `Qwen/Qwen3.8-Flash-Next` | +| `gemma4-31b-it` | Gemma 4 31B | `google/gemma-4-31B-it` | +| `qwen` | Qwen 3.8 27B | `Qwen/Qwen3.8-27B-FP8` | +| `gemma4-26b-a4b-it` | Gemma 4 26B A4B | `google/gemma-4-26B-A4B-it` | +| `glm` | GLM 5.3 Flash | `GLM-5.3-Flash` | +| `molmo2-8b` | Molmo2-8B | `allenai/Molmo2-8B` | + +In `inputs/crossdomain/`, `qwen` is named `qwen3.8-27b-fp8` and `glm` is named +`GLM-5.3-Flash`. Each configuration contributes 5,000 chart and 7,000 scene +responses. The 11 terminal invalids comprise eight chart outputs from Molmo2 +and one CLEVR output each from Qwen Next, Qwen 27B and Gemma 26B. All GQA final +responses are schema-valid. + +Qwen 27B scene outputs combine 2,425 preserved bridge responses and 4,575 local +FP8 fallback responses. The local fallback's model revision and serving settings +are in `inputs/crossdomain/qwen-backend-provenance.json`. The bridge checkpoint +revision is unknown. Mixed-backend question groups are retained and reported; +do not interpret a backend difference as a causal model/domain effect. + +The verified **CPU reproduction** environment is pinned in `requirements.txt` +and `protocol/verified-runtime.json`. It reproduces statistics and validates +the packaged evidence; it is not claimed to be every model's inference +environment. Available historical inference revisions/versions and decoding +settings are recorded in `INFERENCE_ENVIRONMENTS.json`; unknown +values stay unknown. No model weights, API credentials or generation traces +are included, and preparing this release ran no new inference. + +For future GPU replication, start with a small preflight and record GPU memory, +utilization and throughput. The original local runners used vLLM where supported, +`gpu_memory_utilization=0.90`, greedy decoding, and model-native image processing. +Batching/parallelism and backend-specific compatibility affect the runtime; +the frozen results can be reproduced from the supplied scoring records without +access to any original endpoint. + +Strict scene answers normalize rational numbers, Unicode NFKC, case and spaces +only. Invalid outputs fail full-denominator scores and all-sibling group success. +All post-hoc strata, class-balanced metrics and bootstrap settings retain their +descriptive status. The finite-program construction records are independent of +the six-configuration model response denominator. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..5f5382b25a27b5fe044794c10fe89a5978839209 --- /dev/null +++ b/README.md @@ -0,0 +1,194 @@ +--- +pretty_name: Visual Answerability across Charts and Scenes +language: +- en +license: other +license_name: component-specific-see-license +license_link: LICENSE.md +task_categories: +- visual-question-answering +size_categories: +- 10K