Upload DejaCue dataset (part 2)

DATA_LICENSE.md

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+# Data and Feature Terms
+
+The `data/` directory combines metadata and reference results created for Déjà Cue with features derived from external research datasets and a frozen vision-language encoder. These materials do not share one blanket software or dataset license.
+
+## What Is Included
+
+The dataset contains frozen embeddings, stable evaluation IDs, fixed method settings, reference intervals, statistical results, and validation files. It does not contain native RGB or video, target-lineage masks, pretrained encoder weights, complete annotation files, resolved framewise consensus, or generated decoder checkpoints.
+
+A derived feature does not transfer ownership of, or grant new rights to, its source media or model. Users remain responsible for the terms of every upstream asset they access or redistribute.
+
+## Source Terms
+
+| Asset family | Upstream source | Files in this dataset | Terms to retain or verify |
+| --- | --- | --- | --- |
+| VOST histories | [VOST dataset](https://www.vostdataset.org) and [paper](https://openaccess.thecvf.com/content/CVPR2023/html/Tokmakov_Breaking_the_Object_in_Video_Object_Segmentation_CVPR_2023_paper.html) | Object-local embeddings, split lists, evaluation IDs, and reference intervals | VOST identifies [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/); also follow the underlying source-media terms identified by VOST |
+| HyperNeRF histories | [HyperNeRF](https://doi.org/10.1145/3478513.3480487) | Target and auxiliary-track embeddings plus evaluation information | Verify and retain the upstream project and dataset terms for the scenes used |
+| Neural 3D Video history | [Neural 3D Video](https://doi.org/10.1109/CVPR52688.2022.00544) | Coffee-martini target embeddings and evaluation information | Verify and retain the upstream project and dataset terms |
+| D-NeRF recurrence experiment | [D-NeRF](https://doi.org/10.1109/CVPR46437.2021.01018) | Predictions and reference intervals; no rendered RGB | Verify and retain the upstream project and dataset terms |
+| Feature encoder | [SigLIP 2](https://arxiv.org/abs/2502.14786) and its [model card](https://huggingface.co/google/siglip2-base-patch16-224) | Unit-normalized embeddings; no model files | Follow the model-card and weight terms for the specified revision |
+
+VOST documentation requests citation of VOST, Ego4D, and EPIC-KITCHENS when its sequences are used. Retain those attributions whenever the corresponding VOST-derived files are redistributed.
+
+Feature extraction uses `google/siglip2-base-patch16-224` at revision `75de2d55ec2d0b4efc50b3e9ad70dba96a7b2fa2`. The checksum list identifies the model files used for extraction, but those model files are not included.
+
+## Material Created for Déjà Cue
+
+The JSON metadata, file formats, protocol descriptions, and reference results created for Déjà Cue may be reused for research and method comparison with attribution to **Déjà Cue: Localizing States in Object Histories via Vocabulary-Relative Coordinates**. The Apache-2.0 license in the companion code repository applies to original software; it does not automatically cover external data, embeddings, or upstream media.
+
+## Redistribution
+
+When redistributing these files or a modified version:
+
+1. retain this file and the applicable upstream notices;
+2. cite Déjà Cue and the upstream sources whose assets were used;
+3. document every added, removed, or transformed file;
+4. preserve the stable evaluation IDs, or provide an explicit mapping when they change; and
+5. regenerate the checksum list for the redistributed files.
+
+Do not describe the package as granting rights to source media, model weights, or personal likenesses beyond those supplied by their owners.
+
+## Privacy and Sensitive Use
+
+The dataset omits direct identifiers and native media, but frozen embeddings are derived from external research content and should not be treated as anonymous by default. These files are intended for scientific retrieval and reproducibility research, not biometric identification, surveillance, or sensitive-attribute inference.
+
+These terms do not replace the upstream licenses or terms of use.

DATA_MANIFEST.json

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+}

README.md

@@ -0,0 +1,206 @@
+---
+pretty_name: Déjà Cue Fixed-Feature Evaluation Assets
+language:
+  - en
+license: other
+license_name: deja-cue-data-and-feature-terms
+license_link: https://github.com/HaofanCao/DejaCue/blob/main/DATA_LICENSE.md
+annotations_creators:
+  - expert-generated
+language_creators:
+  - expert-generated
+multilinguality:
+  - monolingual
+source_datasets:
+  - extended
+size_categories:
+  - n<1K
+tags:
+  - video
+  - computer-vision
+  - temporal-localization
+  - moment-retrieval
+  - object-tracking
+  - reproducibility
+---
+
+<div align="center">
+
+<h1 align="center">Déjà Cue Fixed-Feature Evaluation Assets</h1>
+
+<p><strong>Frozen features, fixed evaluation settings, exact reference windows, and statistical results for Déjà Cue.</strong></p>
+
+<p align="center">Haofan Cao · Zhichao You · Yunkai Yang · Liang Guo · Jie Wang · Chongshou Li</p>
+
+<p align="center">
+  <a href="https://arxiv.org/abs/2608.02044"><img src="https://img.shields.io/badge/arXiv-2608.02044-b31b1b.svg" alt="arXiv"></a>
+  <a href="https://github.com/HaofanCao/DejaCue"><img src="https://img.shields.io/badge/Companion%20Code-GitHub-181717.svg?logo=github&logoColor=white" alt="Companion GitHub code"></a>
+  <a href="DATA_LICENSE.md"><img src="https://img.shields.io/badge/Data%20Terms-Read%20before%20use-7C3AED.svg" alt="Data terms"></a>
+</p>
+</div>
+
+<p align="center">
+  <img src="assets/method_overview.png" alt="Overview of the Déjà Cue retrieval framework" width="100%">
+</p>
+
+## 📄 Abstract
+
+Tracking links observations of the same object through visual change, yet cannot by itself determine when the object is empty or filled, intact or cut. Déjà Cue formulates identity-conditioned state-moment retrieval: given a tracked-object history and alternative state descriptions, it localizes an interval in which each described state holds. The method turns the alternatives into a vocabulary-relative coordinate system, subtracts their state-balanced centroid from each description, calibrates frame scores, and scans multiple durations within contiguous visible runs using a frozen encoder. On 78 VOST histories, changing only the query reference nearly doubles R@1 at tIoU 0.5 from 10.3% to 20.5% and raises Top-1 tIoU from 16.0% to 21.5%.
+
+## 🔥 News
+
+- **2026.08.05** · The official code and fixed-feature evaluation assets are now open source.
+
+## 📦 Dataset Summary
+
+This repository contains the same `data/` directory as the companion code release. It reproduces retrieval, interval selection, metrics, and statistical calculations without redistributing native video or rerunning the frozen encoder.
+
+| Group | Contents |
+| --- | --- |
+| VOST evaluation | 78 histories, 156 states, 312 descriptions, reference intervals, and frozen features |
+| Prompt variants | Raw, photo, definite, and normalized three-form text features |
+| Development set | 5 histories, 13 states, 26 descriptions, 46 positive episodes, and 86 training records |
+| Seven-history evaluation | 7 histories, 16 states, 32 descriptions, 59 reference episodes, and 8 auxiliary tracks |
+| Reference results | Exact selected windows and the rows used for the reported statistical calculations |
+| Evaluation settings | Fixed VOST split lists, final cohort information, method settings, and checksums |
+
+The dataset contains 288 files under `data/`. `DATA_MANIFEST.json` records the byte size and SHA-256 digest of every one.
+
+## ⬇️ Download and Verify
+
+With the current Hugging Face CLI, set the dataset ID and download the complete dataset:
+
+```bash
+export DEJA_CUE_DATASET_ID=your-namespace/deja-cue-data
+hf download "$DEJA_CUE_DATASET_ID" --type dataset --local-dir deja-cue-data
+cd deja-cue-data
+python verify_data.py
+```
+
+The script checks every file name, byte size, and SHA-256 value. A Python download is equivalent:
+
+```python
+from pathlib import Path
+
+from huggingface_hub import snapshot_download
+
+dataset_root = Path(
+    snapshot_download(
+        repo_id="your-namespace/deja-cue-data",
+        repo_type="dataset",
+        local_dir="deja-cue-data",
+    )
+)
+```
+
+## 🔎 Load One History
+
+```python
+import numpy as np
+
+visual_path = dataset_root / "data/features/siglip2/H001/visual_features.npz"
+with np.load(visual_path, allow_pickle=False) as arrays:
+    frame_indices = arrays["frame_indices"]
+    visual_features = arrays["visual_features"]
+    visibility_count = arrays["visibility_count"]
+
+print(frame_indices.shape, visual_features.shape, visibility_count.shape)
+```
+
+Primary visual archives contain:
+
+| Field | Meaning |
+| --- | --- |
+| `frame_indices` | Original temporal indices for observed target frames |
+| `visual_features` | Unit-normalized 768-dimensional SigLIP 2 features |
+| `visibility_count` | Number of target-lineage masks contributing at each frame |
+
+Primary text archives contain `state_ids`, `state_texts`, and unit-normalized `text_features`. Prompt-variant archives additionally contain `variant_names`. JSON manifests define sibling-state grouping, inclusive reference intervals, source-component aggregation, and relative feature paths.
+
+## 🔗 Use with the Code Release
+
+The companion GitHub repository already includes the same `data/` files, so a complete clone can reproduce the CPU results immediately:
+
+```bash
+git clone https://github.com/HaofanCao/DejaCue.git
+cd DejaCue
+python -m pip install -e ".[test]"
+python scripts/reproduce_main.py --device cpu
+```
+
+When using a separate Hub download, place or link its `data/` directory at the code repository root without changing the relative paths. The code repository supplies the data loaders, retrieval implementation, reproduction commands, annotation and feature tools, and tests.
+
+## 🗂️ Repository Layout
+
+```text
+data/
+  benchmark.json              Primary benchmark manifest
+  features/                   VOST visual, text, and prompt features
+  learned/development/        Frozen learned-decoder development inputs
+  seven_history/              Seven-history features and auxiliary tracks
+  reference/                  Exact windows and statistical results
+  protocol/vost/              Fixed VOST split lists and protocol notes
+DATA_MANIFEST.json            File sizes and SHA-256 checksums
+DATA_LICENSE.md               Data and derived-feature terms
+verify_data.py                File verification script
+```
+
+Evaluation identifiers such as `H001` are stable IDs used to align features, states, descriptions, and references. All manifest paths are relative to the repository root.
+
+## 🌐 Data Sources
+
+| Asset family | Upstream research source | What this repository contains |
+| --- | --- | --- |
+| Primary histories | [VOST](https://openaccess.thecvf.com/content/CVPR2023/html/Tokmakov_Breaking_the_Object_in_Video_Object_Segmentation_CVPR_2023_paper.html) | Derived object-local features, evaluation IDs, fixed settings, and reference intervals |
+| Six seven-history sequences | [HyperNeRF](https://doi.org/10.1145/3478513.3480487) | Derived target and auxiliary-track features plus evaluation metadata |
+| Coffee-martini sequence | [Neural 3D Video](https://doi.org/10.1109/CVPR52688.2022.00544) | Derived target features and evaluation metadata |
+| Recurrence diagnostic | [D-NeRF](https://doi.org/10.1109/CVPR46437.2021.01018) | Predictions and reference intervals, not rendered RGB |
+| Feature encoder | [SigLIP 2](https://arxiv.org/abs/2502.14786) | Unit-normalized derived embeddings; no model weights |
+
+Feature extraction uses `google/siglip2-base-patch16-224` at revision `75de2d55ec2d0b4efc50b3e9ad70dba96a7b2fa2`. Upstream media and model terms continue to apply to derived assets; see [DATA_LICENSE.md](DATA_LICENSE.md).
+
+## 📝 Annotation and Included Files
+
+For VOST, two state descriptions per state are fixed before labeling. Two annotators independently label target-lineage frames as pre-state, transition, post-state, or unobserved while blind to method scores, and every disagreement is resolved to consensus. The 100-history selection retains 78 qualifying histories and excludes 22 with no qualifying event.
+
+The dataset includes the split lists, final cohort information, annotation validation scripts, and agreement and annotation-sensitivity summaries. It does not redistribute native RGB, target-lineage masks, pretrained encoder weights, generated decoder checkpoints, the two complete annotation files, or the resolved framewise consensus.
+
+## ✅ Intended Uses
+
+- Reproduction of the primary and extended VOST evaluations.
+- Reproduction of the seven-history coordinate study.
+- Training the documented eight learned decoders on the five-history development set and evaluating generated checkpoints.
+- Recalculation of the reported robustness, diagnostic, and statistical summaries.
+- Comparison of alternative retrieval methods under the same frozen representation and evaluation protocol.
+
+## ⚠️ Limitations
+
+The task assumes target-lineage masks, a closed sibling vocabulary, and one selected interval per description. It has no calibrated null output for an absent state. VOST uses binary event-adjacent states and scores one designated occurrence; the dataset also contains 13 additional pre-state and 20 additional post-state occurrences. The seven-history set adds multi-state, interior, recurrent, and progressive cases, but remains small and its three independently held-out histories show mixed coordinate effects.
+
+Fixed features isolate retrieval logic from encoder execution and do not establish behavior for other encoders. Tracking stresses use controlled feature mixing and identity switches rather than pixel-level mask drift. The release does not establish performance for automatically discovered identities, open vocabularies, broad multi-state collections, every natural tracking failure, or hardware-specific latency.
+
+## 🛡️ Privacy and Responsible Use
+
+No native RGB, video, masks, direct identifiers, or model weights are included. The embeddings remain derived from external research media and must be handled under the corresponding source terms. These assets are intended for scientific retrieval and reproducibility research, not biometric identification, surveillance, or inferring sensitive attributes.
+
+## ⚖️ Licensing
+
+This repository combines metadata created for Déjà Cue with embeddings derived from external research data, so its Hugging Face license field is `other`. Read [DATA_LICENSE.md](DATA_LICENSE.md) before reuse or redistribution, and retain the upstream attributions required for the source assets.
+
+## 📚 Citation
+
+If you use these assets, please cite the official paper record:
+
+```bibtex
+@misc{cao2026dejacue,
+  title         = {Déjà Cue: Localizing States in Object Histories via Vocabulary-Relative Coordinates},
+  author        = {Haofan Cao and Zhichao You and Yunkai Yang and Liang Guo and Jie Wang and Chongshou Li},
+  year          = {2026},
+  eprint        = {2608.02044},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.CV},
+  url           = {https://arxiv.org/abs/2608.02044}
+}
+```
+
+**Paper:** [**arXiv:2608.02044**](https://arxiv.org/abs/2608.02044)

data/README.md

@@ -0,0 +1,14 @@
+# Data Layout
+
+This directory contains the fixed features and evaluation files used by the repository commands.
+
+| Path | Role |
+| --- | --- |
+| `benchmark.json` | Primary 78-history benchmark manifest and file references |
+| `features/` | VOST visual, text, and prompt-variant SigLIP 2 features |
+| `learned/development/` | Five-history inputs for native decoder training |
+| `seven_history/` | Seven-history evaluation features and auxiliary tracks |
+| `reference/` | Exact windows and the rows used for the reported calculations |
+| `protocol/vost/` | Fixed VOST split lists and protocol notes |
+
+Paths inside manifests are repository-relative. Loaders reject absolute paths, parent traversal, unexpected array fields, non-finite values, misaligned rows, and invalid feature norms. See the repository-level [`README.md`](../README.md) for the release overview and [`DATA_LICENSE.md`](../DATA_LICENSE.md) for terms.

data/protocol/vost/train.txt

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data/protocol/vost/validation.txt

@@ -0,0 +1,70 @@
+10625_knead_dough
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+4222_peel_wire
+5118_squeeze_bag

data/reference/robustness/annotation_agreement.json

@@ -0,0 +1,128 @@
+{
+  "coverage": {
+    "history_count": 3,
+    "matched_episode_count": 34,
+    "selected_frame_count": 1883,
+    "stable_valid_frame_count": 1700,
+    "state_history_pair_count": 8
+  },
+  "histories": [
+    {
+      "history_id": "A01",
+      "label": "slice_banana",
+      "selected_frames": {
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+        "exact_agreement_count": 254,
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+        "kappa": {
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+      }
+    },
+    {
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+      "label": "cross_hands",
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+  ],
+  "kind": "deja_cue_annotation_agreement_evidence",
+  "reported": {
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+      "all_disagreements_adjudicated": true,
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+    },
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+    "history_macro": {
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+      "stable_valid_frames": 0.7347306853629044
+    }
+  },
+  "schema_version": 1
+}

data/reference/robustness/annotation_sensitivity.json

@@ -0,0 +1,329 @@
+{
+  "kind": "deja_cue_annotation_sensitivity_evidence",
+  "reference_episode_counts": {
+    "adjudicated": {
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+        "mostly_unsliced": 1,
+        "partly_sliced": 1
+      },
+      "A02": {
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+        "hands_open_noninterlaced": 24
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+      "A03": {
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+        "nearly_full": 1,
+        "partly_filled": 1
+      }
+    },
+    "pass_A": {
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+        "mostly_unsliced": 1,
+        "partly_sliced": 1
+      },
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data/reference/robustness/development_controls.json

@@ -0,0 +1,315 @@
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+}

data/reference/robustness/dnerf_recurrence.json

@@ -0,0 +1,134 @@
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+      "query_id": "Q02",
+      "state_id": "limbs_extended"
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+    {
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+      "query_id": "Q03",
+      "state_id": "limbs_contracted"
+    },
+    {
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+}

data/reference/robustness/hard_negative_margins.json

@@ -0,0 +1,102 @@
+{
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data/reference/robustness/tracking_perturbations.json

@@ -0,0 +1,1313 @@
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+        "paired_delta_scene_cluster_bootstrap_95ci": {
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+          "state_macro_target_r1_tiou_0.5": [
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+      "support": {
+        "num_affected_histories": 4,
+        "num_affected_scenes": 4
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+  },
+  "kind": "deja_cue_tracking_perturbation_evidence",
+  "metrics": [
+    "state_macro_target_top1_tiou",
+    "state_macro_target_r1_tiou_0.5",
+    "query_macro_joint_identity_accuracy"
+  ],
+  "protocol": {
+    "contamination_semantics": "only exact-time co-visible distractor features are mixed; each mixed feature is L2-normalized",
+    "coordinate_system": "inclusive_absolute_frame_indices",
+    "eligibility": "metrics use only object histories with at least one actual feature or support modification; the paired clean baseline uses the identical subset",
+    "identity_switch_semantics": "one distractor track, chosen by maximum exact-time suffix coverage with track-id tie breaking, replaces the target suffix at exact shared frames",
+    "method": {
+      "features": "union",
+      "kernel_size": 3,
+      "query_centering": 1.0,
+      "visual_centering": 1.0
+    },
+    "missing_frame_semantics": "removed observations are deleted from FeatureTrack, so the scanner sees a true validity gap and cannot smooth or scan across it"
+  },
+  "schema_version": 1
+}

data/reference/robustness/vost_paraphrase.json

@@ -0,0 +1,803 @@
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data/reference/runtime.json

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data/reference/seven_history_learned.json

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data/reference/seven_history_summary.json

@@ -0,0 +1,1438 @@
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+      "history_id": "S05",
+      "object_group_id": "neu3d/coffee_martini/cam00/martini_glass_contents",
+      "scene": "coffee_martini",
+      "states": [
+        {
+          "descriptions": [
+            "nearly empty martini glass",
+            "martini glass containing only a little cocktail"
+          ],
+          "feature_state_id": "s_0dc6a1ebde35dc4dcfcdfde433e57511eb688ccf184ed1e65fd22e2dd9704ca8",
+          "references": [
+            [
+              0,
+              28
+            ]
+          ],
+          "state_id": "nearly_empty"
+        },
+        {
+          "descriptions": [
+            "partly filled martini glass",
+            "martini glass filled to an intermediate level"
+          ],
+          "feature_state_id": "s_9c3f6b9f22664c7ddef3da3b6d651873ced809109734c17aa0401bbf86131a41",
+          "references": [
+            [
+              31,
+              68
+            ]
+          ],
+          "state_id": "partly_filled"
+        },
+        {
+          "descriptions": [
+            "nearly full martini glass",
+            "martini glass containing cocktail near the top of its bowl"
+          ],
+          "feature_state_id": "s_32adb45c45524ed571c710620dbf59a63ae1001946e6de13b95715ccdf583003",
+          "references": [
+            [
+              71,
+              99
+            ]
+          ],
+          "state_id": "nearly_full"
+        }
+      ],
+      "stratum": "extension",
+      "target_visual_features": "data/seven_history/features/evaluation/neu3d__coffee_martini__cam00/visual_features.npz",
+      "text_features": "data/seven_history/features/evaluation/neu3d__coffee_martini__cam00/text_features.npz",
+      "text_state_field": "state_tokens"
+    },
+    {
+      "dataset": "hypernerf",
+      "distractors": [],
+      "history_id": "S06",
+      "object_group_id": "hypernerf/misc_cross-hands/cross-hands1/participants_two_hands_union",
+      "scene": "misc_cross-hands",
+      "states": [
+        {
+          "descriptions": [
+            "two open hands with fingers not interlaced",
+            "both hands open without interwoven fingers"
+          ],
+          "feature_state_id": "s_2f813244369f254efb490c00d580c6527aabdc87b40550582b872c6653160489",
+          "references": [
+            [
+              0,
+              15
+            ],
+            [
+              86,
+              138
+            ],
+            [
+              139,
+              181
+            ],
+            [
+              253,
+              279
+            ],
+            [
+              285,
+              296
+            ],
+            [
+              312,
+              325
+            ],
+            [
+              353,
+              393
+            ],
+            [
+              505,
+              575
+            ],
+            [
+              596,
+              629
+            ],
+            [
+              679,
+              693
+            ],
+            [
+              695,
+              709
+            ],
+            [
+              722,
+              732
+            ],
+            [
+              736,
+              754
+            ],
+            [
+              775,
+              791
+            ],
+            [
+              805,
+              822
+            ],
+            [
+              834,
+              843
+            ],
+            [
+              853,
+              866
+            ],
+            [
+              897,
+              907
+            ],
+            [
+              909,
+              979
+            ],
+            [
+              1113,
+              1171
+            ],
+            [
+              1173,
+              1187
+            ],
+            [
+              1230,
+              1256
+            ],
+            [
+              1341,
+              1375
+            ],
+            [
+              1430,
+              1445
+            ]
+          ],
+          "state_id": "hands_open_noninterlaced"
+        },
+        {
+          "descriptions": [
+            "two hands clasped with interlaced fingers",
+            "opposing hands with fingers visibly interwoven"
+          ],
+          "feature_state_id": "s_338072088c92362605e882c18121bdc89c7fb1cf2628e5e77d76f076f89e5e07",
+          "references": [
+            [
+              16,
+              32
+            ],
+            [
+              33,
+              85
+            ],
+            [
+              182,
+              225
+            ],
+            [
+              227,
+              252
+            ],
+            [
+              298,
+              311
+            ],
+            [
+              394,
+              434
+            ],
+            [
+              435,
+              504
+            ],
+            [
+              630,
+              663
+            ],
+            [
+              664,
+              678
+            ],
+            [
+              711,
+              721
+            ],
+            [
+              758,
+              774
+            ],
+            [
+              824,
+              833
+            ],
+            [
+              867,
+              880
+            ],
+            [
+              887,
+              896
+            ],
+            [
+              980,
+              1051
+            ],
+            [
+              1054,
+              1112
+            ],
+            [
+              1188,
+              1203
+            ],
+            [
+              1204,
+              1229
+            ],
+            [
+              1376,
+              1411
+            ],
+            [
+              1414,
+              1429
+            ]
+          ],
+          "state_id": "fingers_interlaced_clasped"
+        }
+      ],
+      "stratum": "extension",
+      "target_visual_features": "data/seven_history/features/evaluation/hypernerf__misc_cross-hands__cross-hands1/visual_features.npz",
+      "text_features": "data/seven_history/features/evaluation/hypernerf__misc_cross-hands__cross-hands1/text_features.npz",
+      "text_state_field": "state_tokens"
+    },
+    {
+      "dataset": "hypernerf",
+      "distractors": [],
+      "history_id": "S07",
+      "object_group_id": "hypernerf/interp_slice-banana/primary_attached_flesh",
+      "scene": "interp_slice-banana",
+      "states": [
+        {
+          "descriptions": [
+            "mostly unsliced peeled banana",
+            "peeled banana with most of its body still intact"
+          ],
+          "feature_state_id": "s_df9c6a044f693cddf5740d944d053c87e30d49a79fce71282da624b3bcef53f6",
+          "references": [
+            [
+              0,
+              139
+            ]
+          ],
+          "state_id": "mostly_unsliced"
+        },
+        {
+          "descriptions": [
+            "partly sliced banana",
+            "peeled banana with roughly half of its body remaining intact"
+          ],
+          "feature_state_id": "s_1e1db404cc2c647f11bdb17c91f3ec4bfc52e8cdeb0888de78f26fcc8e38fe87",
+          "references": [
+            [
+              141,
+              189
+            ]
+          ],
+          "state_id": "partly_sliced"
+        },
+        {
+          "descriptions": [
+            "mostly sliced banana",
+            "peeled banana with only a short intact section remaining"
+          ],
+          "feature_state_id": "s_5c83c9be22e679a281caf37dbcbdc3254d767b733b8bea7bb6729688e82a8696",
+          "references": [
+            [
+              254,
+              299
+            ],
+            [
+              301,
+              329
+            ]
+          ],
+          "state_id": "mostly_sliced"
+        }
+      ],
+      "stratum": "extension",
+      "target_visual_features": "data/seven_history/features/evaluation/hypernerf__interp_slice-banana/visual_features.npz",
+      "text_features": "data/seven_history/features/evaluation/hypernerf__interp_slice-banana/text_features.npz",
+      "text_state_field": "state_tokens"
+    }
+  ],
+  "interval_convention": "zero_based_inclusive",
+  "kind": "deja_cue_seven_history_feature_manifest",
+  "schema_version": 1
+}

verify_data.py

@@ -0,0 +1,97 @@
+"""Build or verify the SHA-256 manifest for the Deja Cue dataset."""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+from pathlib import Path
+from typing import Any
+
+
+ROOT = Path(__file__).resolve().parent
+DATA_ROOT = ROOT / "data"
+MANIFEST = ROOT / "DATA_MANIFEST.json"
+
+
+def sha256_file(path: Path) -> str:
+    """Return a streaming SHA-256 digest for one file."""
+
+    digest = hashlib.sha256()
+    with path.open("rb") as handle:
+        for block in iter(lambda: handle.read(1024 * 1024), b""):
+            digest.update(block)
+    return digest.hexdigest()
+
+
+def build_manifest() -> dict[str, Any]:
+    """Describe every regular file below data/ in stable path order."""
+
+    files = sorted(
+        (path for path in DATA_ROOT.rglob("*") if path.is_file()),
+        key=lambda path: path.relative_to(ROOT).as_posix(),
+    )
+    entries = [
+        {
+            "path": path.relative_to(ROOT).as_posix(),
+            "bytes": path.stat().st_size,
+            "sha256": sha256_file(path),
+        }
+        for path in files
+    ]
+    return {
+        "schema_version": 1,
+        "kind": "deja_cue_data_manifest",
+        "algorithm": "sha256",
+        "file_count": len(entries),
+        "total_bytes": sum(entry["bytes"] for entry in entries),
+        "files": entries,
+    }
+
+
+def validate_manifest(observed: dict[str, Any], expected: dict[str, Any]) -> None:
+    """Raise when schema, paths, sizes, or hashes differ."""
+
+    for key in ("schema_version", "kind", "algorithm", "file_count", "total_bytes"):
+        if expected.get(key) != observed.get(key):
+            raise ValueError(f"Manifest field differs: {key}")
+    expected_files = expected.get("files")
+    observed_files = observed.get("files")
+    if not isinstance(expected_files, list) or expected_files != observed_files:
+        raise ValueError("Manifest file list differs")
+
+
+def main() -> None:
+    """Write a new manifest or validate the committed one."""
+
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--write",
+        action="store_true",
+        help="replace DATA_MANIFEST.json with the current data file list",
+    )
+    args = parser.parse_args()
+    observed = build_manifest()
+    if args.write:
+        MANIFEST.write_text(
+            json.dumps(observed, indent=2, ensure_ascii=True) + "\n",
+            encoding="ascii",
+            newline="\n",
+        )
+    else:
+        expected = json.loads(MANIFEST.read_text(encoding="ascii"))
+        validate_manifest(observed, expected)
+    print(
+        json.dumps(
+            {
+                "passed": True,
+                "file_count": observed["file_count"],
+                "total_bytes": observed["total_bytes"],
+            },
+            sort_keys=True,
+        )
+    )
+
+
+if __name__ == "__main__":
+    main()