Simplify README paths and link experiment instructions to GitHub

Use short models, features, and data folders; remove server paths and internal configuration instructions. Preserve all checkpoint/encoder links, feature files, and dataset metadata.

FILES.json

@@ -38,8 +38,8 @@
     },
     {
       "path": "README.md",
-      "bytes": 65334,
-      "sha256": "cb29f8e132e3b272baf302dd233b727be35be603cd26b3566cf639e59564adab"
+      "bytes": 53731,
+      "sha256": "9936a9ce2b3f2044402b28451c1d887466e5506071ebcbdea4fd008a4ead01e5"
     },
     {
       "path": "encoders.json",
@@ -5026,7 +5026,7 @@
     }
   ],
   "total_files": 606,
-  "total_bytes": 717452503087,
+  "total_bytes": 717452491484,
   "source_lcs_manifest_sha256": "bc439048824c510b471d4c8cf34b143471a49a50260906f210cb8a1723e971ed",
   "published_lcs_manifest_sha256": "f934b05aadf9b94039b207812033e48b1e2aefe43e0fd084b5508e6fd103dbed",
   "validation": {
@@ -5049,5 +5049,5 @@
     "text_file_hashes_match_original_audits": true
   },
   "lcs10000_prepared_at": "2026-10-06T13:21:01.351852+00:00",
-  "documentation_updated_at_utc": "2026-10-07T01:35:07.450907+00:00"
+  "documentation_updated_at_utc": "2026-10-07T03:47:25.898933+00:00"
 }

README.md

@@ -60,111 +60,72 @@ The release contains LCS-558K image patch features for RAVEL and other paired im
 
 **Resources:** [Paper](https://arxiv.org/abs/2610.05413) · [Evaluation code](https://github.com/JuntaoTang/MLLM-VisionEncoder-Eval) · [MLLM Model Zoo and encoder weights](https://huggingface.co/336labs/VisionEncoder-to-MLLM-ModelZoo) · [Encoder catalog](encoders.json) · [File inventory](FILES.json)
 
-## Datasets Required by the Evaluation Pipelines
+<a id="datasets-required-by-the-evaluation-pipelines"></a>
 
-The [paper](https://arxiv.org/abs/2610.05413) evaluates trained MLLMs on 11 downstream benchmarks and compares their scores with vision-only and cross-modal encoder metrics. The tables below map those experiments to their data dependencies, using the server's current [experiment recipes](https://github.com/JuntaoTang/MLLM-VisionEncoder-Eval/tree/main/configs/experiments) and data loaders. **This Hub repository releases cached LCS and ImageNet200k features; the raw MLLM training/test sets and other baseline datasets must be obtained separately from the linked sources.**
+## Data requirements
 
-### Which data does each method use?
+This repository provides the three cached feature subsets listed below. Raw images, MLLM training data, evaluation benchmarks, and other baseline datasets are obtained separately.
 
-| Method / experiment | Dataset and required inputs | Relevant released cache / additional data |
-| --- | --- | --- |
-| **RAVEL** | Paired images and captions sampled from **LCS-558K**; patch tokens `[N,T,D]`, LLM text vectors `[N,D]`, and matching ordered sample IDs | [LCS-1000](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n1000_seed42) for the current mean-token text setting; [LCS-10000](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43) for the larger historical cache, with last-token text |
-| **RSA, CCA, GW, MutualNN** | The same **LCS image–text pairs**, with one global image vector and one text vector per sample | The released LCS samples/text can be reused. These workers require `[N,D]` visual inputs; separately exported global/CLS features are not included in this release. Match the chosen baseline's readout and sample order when preparing global vectors from an encoder or patch cache. |
-| **kNN classification** | **ImageNet-1K training subset**, 200 images/class, with pooled features, class labels, source indices, and a fixed support/query protocol | [ImageNet200k](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/imagenet1k/train200k). Its released protocol uses 195 support-pool images + 5 held-out queries/class; support sizes are 5/10/20/45/95/195. Queries come from the training subset, not the official validation split. |
-| **Linear probing** | Official **ImageNet-1K train + val** and the DINOv2 `extra/` metadata. The current paper worker uses 5 train images/class, support seed 0, and a separate 5,000-image validation sample, seed 42. | Obtain [ImageNet-1K](https://www.image-net.org/download.php). The 200k export can support a separately defined cached linear probe after checking indices/readout, but it lacks the independent validation features needed to reproduce this worker's protocol. |
-| **Alignment probing** | **COCO Karpathy captions** for the current COCO-2K recipe; **DreamLIP-recaptioned CC3M** for the supported SAIL-style cross-dataset/budget variants. Requires paired image/text embeddings and train/test membership. | COCO-2K: sample 2,000 images with seed 42, use 1,600 train / 400 test, retaining five captions/image. CC3M uses `raw_caption` and `longSV_captions`; cross-dataset evaluation can use COCO-2K. These data/caches are outside this release. |
-| **AC Policy / Law of Vision Representation** | A-score: **LCS-558K captions/images** and Stage-1 projector checkpoints. C-score: **SPair-71k** images, pair annotations, and keypoints. Policy fitting also uses downstream scores for its reference encoders. | Obtain [LLaVA-Pretrain](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain) and [SPair-71k](https://cvlab.postech.ac.kr/research/SPair-71k/index.html). The A-score loader takes the first 100 valid image/conversation records; this differs from the random LCS feature subsets. |
-| **Mid-training loss** | **LCS-558K** Stage-1 projector training under the specified FLOP budget, plus the resulting training-loss logs | Use the raw pretraining manifest/images and the MLLM training recipe. Cached image/text vectors alone do not provide the projector-training loss. |
-| **TokBench (T-ACC, T-NED, F-Sim)** | The official **TokBench image bundle**, text/face annotations, and reconstructions from each evaluated visual tokenizer/decoder | Obtain [Junfeng5/TokBench](https://huggingface.co/datasets/Junfeng5/TokBench). Use the image evaluation route and preserve the original folder/file identities in the reconstructed image tree; patch features alone do not supply reconstructions. |
-| **Downstream MLLM ground truth** | Two-stage MLLM training data plus the **11 test benchmarks** listed below; for correlation-only analysis, the already computed score table is sufficient | [MLLM checkpoints](https://huggingface.co/336labs/VisionEncoder-to-MLLM-ModelZoo) and the project's bundled [ground_truth.json](https://github.com/JuntaoTang/MLLM-VisionEncoder-Eval/blob/main/src/resources/ground_truth.json). Raw test images/questions are outside this feature release. |
+### Data used by each method
 
-ImageNet kNN features contain classification labels, not paired captions or LLM text embeddings, so they do not directly satisfy the alignment-probe input contract. An LCS alignment-probe experiment is possible with an explicit new train/test split and matching representations, but it would be a different protocol from the COCO/CC3M recipes above. Likewise, patch-mean pooling should not be assumed identical to a historical CLS/global readout.
+| Method | Data needed | Available here / source |
+|---|---|---|
+| RAVEL | Paired LCS-558K image patches and text features | `lcs558k/n1000_seed42/` and `lcs558k/n10000_seed42plus43/` |
+| RSA, CCA, GW, MutualNN | Paired LCS-558K global image vectors and text features | Shared samples and text are included; prepare the required global image vectors separately |
+| kNN | ImageNet training features, labels, and support/query split | `imagenet1k/train200k/` |
+| Linear probing | ImageNet train and validation images | [ImageNet](https://www.image-net.org/download.php); the released 200k cache contains training images only |
+| Alignment probing | COCO Karpathy captions; DreamLIP CC3M for the cross-dataset variants | [COCO / Karpathy splits](https://cs.stanford.edu/people/karpathy/deepimagesent/) and [DreamLIP captions](https://huggingface.co/datasets/qidouxiong619/dreamlip_long_captions) |
+| AC Policy / Law of Vision Representation | LCS-558K, Stage-1 projectors, and SPair-71k | [LLaVA-Pretrain](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain), [Model Zoo](https://huggingface.co/336labs/VisionEncoder-to-MLLM-ModelZoo), and [SPair-71k](https://cvlab.postech.ac.kr/research/SPair-71k/index.html) |
+| Mid-training loss | LCS-558K images/captions and projector-training logs | [LLaVA-Pretrain](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain); training instructions in the [code repository](https://github.com/JuntaoTang/MLLM-VisionEncoder-Eval#running) |
+| TokBench | Original images/annotations and matching tokenizer reconstructions | [TokBench](https://huggingface.co/datasets/Junfeng5/TokBench) |
+| MLLM training / evaluation | Training: LCS-558K and filtered mix665k; evaluation: the 11 benchmarks below | [Model Zoo](https://huggingface.co/336labs/VisionEncoder-to-MLLM-ModelZoo) and the linked dataset sources |
 
-The code also retains **zero-shot ImageNet** evaluation (requires official ImageNet validation images and class-name prompts) and **CKA-X** recipes. CKA-X draws a frozen calibration pool from LMUData benchmark TSVs and [OCR-VQA book-cover data](https://ocr-vqa.github.io/), then requires per-encoder/text features, `cka_diverse.pt`, cross-modal statistics, and downstream labels. These are additional supported code paths; they are not additional methods in the paper's main comparison table. Their calibration artifacts are not part of this feature release.
+ImageNet kNN features have class labels but no paired captions, so they cannot supply an alignment probe's image–text inputs on their own. A full linear-probe benchmark also needs independent validation data.
 
-### MLLM training data and directory layout
+### Raw data folders
 
-| Stage | Source | Project files relative to `DATA_ROOT` |
-| --- | --- | --- |
-| Stage 1: projector pretraining | [LLaVA-Pretrain / LCS-558K](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain), including the annotation JSON and image archive | `instructions/pretrain/blip_laion_cc_sbu_558k.json`; `images/pretrain/` |
-| Stage 2: instruction finetuning | [LLaVA-v1.5 mix665k annotations](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/llava_v1_5_mix665k.json) and their referenced COCO/GQA/OCR-VQA/TextVQA/Visual Genome images, following the [LLaVA preparation recipe](https://github.com/haotian-liu/LLaVA#visual-instruction-tuning) | `instructions/finetune/llava_v1_5_mix665k_drop_ge8kchars.json`; `images/finetune/` |
-
-The current training configuration uses the filtered mix665k manifest: it removes 395 examples whose conversation text has at least 8,000 characters. Obtain the source images as well as the JSON, preserve each record's relative image path, and reproduce the configured filtering rather than silently substituting the unfiltered file.
+The following are example local folders. Keep the image subdirectories recorded in each dataset's annotations.
 
 ```text
-DATA_ROOT/
-  instructions/
-    pretrain/blip_laion_cc_sbu_558k.json
-    finetune/llava_v1_5_mix665k_drop_ge8kchars.json
-    test/<dataset_id>.tsv
-  images/
-    pretrain/<relative_image_path>
-    finetune/<relative_image_path>
-    test/<dataset_id>/<image_files>
-  .lmudata/
-    <dataset_id>.tsv
-    images/<dataset_id>/<image_files>
+data/
+  instructions/pretrain/blip_laion_cc_sbu_558k.json
+  instructions/finetune/llava_v1_5_mix665k_drop_ge8kchars.json
+  instructions/test/<dataset>.tsv
+  images/pretrain/
+  images/finetune/
+  images/test/
+  imagenet/{train,val}/
+  coco/
+  cc3m/
   SPair-71k/
-    JPEGImages/
-    ImageAnnotation/
-    PairAnnotation/test/
+  tokbench/
 ```
 
-`DATA_ROOT` is a placeholder for your raw-data directory; the reference server uses `/cache/data`. Set `paths.datasets` in [configs/local.yaml](https://github.com/JuntaoTang/MLLM-VisionEncoder-Eval/blob/main/configs/local.example.yaml) and the corresponding `datasets_root` in [configs/mllm/data.yaml](https://github.com/JuntaoTang/MLLM-VisionEncoder-Eval/blob/main/configs/mllm/data.yaml). The project's loader builds `.lmudata/` as a VLMEvalKit view of `instructions/test/` and `images/test/`. It is a runtime view, separate from the independently copied `/cache/vision_encoder_eval_data` data archive.
-
-### The 11 downstream MLLM test datasets
-
-These are the exact dataset IDs in [configs/mllm/eval_datasets.yaml](https://github.com/JuntaoTang/MLLM-VisionEncoder-Eval/blob/main/configs/mllm/eval_datasets.yaml) and the continuous/discrete runtime recipes. Each requires its project/VLMEvalKit TSV plus the images referenced by that TSV.
-
-| Dataset ID | Source / download instructions | Required TSV under `DATA_ROOT/instructions/test/` |
-| --- | --- | --- |
-| `MMMU_TEST` | [MMMU official dataset](https://huggingface.co/datasets/MMMU/MMMU), **test** split | `MMMU_TEST.tsv` |
-| `MMBench_TEST_EN_V11` | [MMBench](https://github.com/open-compass/MMBench), **English test v1.1** | `MMBench_TEST_EN_V11.tsv` |
-| `VQAv2_VAL` | [VQA v2 downloads](https://visualqa.org/download.html), **validation** questions, annotations, and COCO images | `VQAv2_VAL.tsv` |
-| `ScienceQA_VAL` | [ScienceQA](https://github.com/lupantech/ScienceQA), **validation** split | `ScienceQA_VAL.tsv` |
-| `ChartQA_TEST` | [ChartQA](https://github.com/vis-nlp/ChartQA), **test** split | `ChartQA_TEST.tsv` |
-| `DocVQA_VAL` | [DocVQA](https://www.docvqa.org/datasets/docvqa), **validation** split | `DocVQA_VAL.tsv` |
-| `TextVQA_VAL` | [TextVQA](https://textvqa.org/), **validation** split | `TextVQA_VAL.tsv` |
-| `POPE` | [POPE](https://github.com/RUCAIBox/POPE), benchmark questions and corresponding images | `POPE.tsv` |
-| `GQA_TestDev_Balanced` | [GQA downloads](https://cs.stanford.edu/people/dorarad/gqa/download.html), **balanced test-dev** questions and images | `GQA_TestDev_Balanced.tsv` (the loader also accepts `GQA_TESTDEV_BALANCED.tsv`) |
-| `MSCOCO_KARPATHY_TEST` | [Karpathy caption annotations/splits](https://cs.stanford.edu/people/karpathy/deepimagesent/) and the matching [COCO images](https://cocodataset.org/#download), **Karpathy test** split | `MSCOCO_KARPATHY_TEST.tsv` |
-| `FLICKR30K_KARPATHY_TEST` | [Karpathy caption annotations/splits](https://cs.stanford.edu/people/karpathy/deepimagesent/) and [Flickr30k images](https://shannon.cs.illinois.edu/DenotationGraph/), **Karpathy test** split | `FLICKR30K_KARPATHY_TEST.tsv` |
-
-The [VLMEvalKit dataset loaders](https://github.com/open-compass/VLMEvalKit/tree/main/vlmeval/dataset) provide download mappings for supported benchmarks. Restore or prepare the **exact project TSVs**, including questions/options, reference answers/captions, image IDs, and any shared-image references; downloading an upstream image archive alone does not satisfy this layout. The Karpathy caption splits are distinct from ordinary COCO validation and Flickr30k training splits. Keep all reference captions for caption scoring and alignment-probe evaluation.
-
-The current runtime sets `max_samples: 1000` and `sample_seed: 42` for each benchmark, using seeded sampling rather than the TSV head. Preserve the source TSV order and subset settings when comparing runs. Full benchmarks are not reduced to 1,000 examples in this feature repository, because their raw test data are not included here.
-
-### Other baseline raw-data layouts
+**MLLM pretraining:** obtain the JSON and `images.zip` from [LLaVA-Pretrain](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain). Image names in the JSON are relative to `data/images/pretrain/`.
 
-| Data root / configuration | Required contents | Source |
-| --- | --- | --- |
-| `paths.imagenet` | `train/`, `val/`, and DINOv2 `extra/` index/class metadata; a linear probe needs both training and independent validation inputs | [ImageNet downloads](https://www.image-net.org/download.php); [DINOv2 ImageNet metadata loader](https://github.com/facebookresearch/dinov2/blob/main/dinov2/data/datasets/image_net.py) |
-| `paths.cc3m` | `cc3m_3long_3short_1raw_captions_url.csv` and images at its `Image Path` values; preserve `raw_caption` and `longSV_captions` | [DreamLIP caption release](https://huggingface.co/datasets/qidouxiong619/dreamlip_long_captions/blob/main/cc3m_3long_3short_1raw_captions_url.csv); [SAIL data preparation](https://github.com/lezhang7/SAIL#data-preparation) |
-| `DATA_ROOT/SPair-71k` | `JPEGImages/<category>/`, `ImageAnnotation/<category>/`, `PairAnnotation/test/` for the current C-score loader | [SPair-71k official download](https://cvlab.postech.ac.kr/research/SPair-71k/index.html) |
-| TokBench image root | Official `images.zip` extracted as `images/`, `annotations/text_*.json`, and `annotations/face_meta.json`; a separate matching reconstruction tree | [TokBench files](https://huggingface.co/datasets/Junfeng5/TokBench/tree/main); [image evaluation instructions](https://github.com/wjf5203/TokBench#-evaluating-your-tokenizervae) |
+**MLLM finetuning:** obtain [mix665k](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/llava_v1_5_mix665k.json) and its COCO, GQA, OCR-VQA, TextVQA, and Visual Genome images using the [LLaVA data preparation instructions](https://github.com/haotian-liu/LLaVA#visual-instruction-tuning). The project uses `llava_v1_5_mix665k_drop_ge8kchars.json`, which excludes 395 examples with at least 8,000 conversation-text characters. Image names in this JSON are relative to `data/images/finetune/`.
 
-For the CC3M variants, the builder samples only available, decodable images with all requested captions; a partially downloaded URL pool changes the selected subset. Preserve its generated `cc3m2k.json` / selected-row CSV (and the corresponding larger-budget manifests) to reproduce the same experiment. The COCO control uses `alignment/data/coco2k.json` with five captions/image; derived probe caches live under `alignment/cache/{vision,text}/<dataset>/`. These manifests and caches have not been uploaded to this repository.
+### Downstream MLLM benchmarks
 
-### Reference server paths and availability
+Place the prepared benchmark TSVs in `data/instructions/test/`. Obtain the matching data from these sources:
 
-Checked on **2026-10-07 (Asia/Shanghai)**. Paths below describe the current server; replace them with your own local paths when reproducing experiments. “Required, missing” means the inspected configured path has no usable copy, not that the upstream dataset is unavailable.
+| Benchmark / required TSV | Source |
+|---|---|
+| `MMMU_TEST.tsv` | [MMMU](https://huggingface.co/datasets/MMMU/MMMU), test split |
+| `MMBench_TEST_EN_V11.tsv` | [MMBench](https://github.com/open-compass/MMBench), English test v1.1 |
+| `VQAv2_VAL.tsv` | [VQA v2](https://visualqa.org/download.html), validation split |
+| `ScienceQA_VAL.tsv` | [ScienceQA](https://github.com/lupantech/ScienceQA), validation split |
+| `ChartQA_TEST.tsv` | [ChartQA](https://github.com/vis-nlp/ChartQA), test split |
+| `DocVQA_VAL.tsv` | [DocVQA](https://www.docvqa.org/datasets/docvqa), validation split |
+| `TextVQA_VAL.tsv` | [TextVQA](https://textvqa.org/), validation split |
+| `POPE.tsv` | [POPE](https://github.com/RUCAIBox/POPE) |
+| `GQA_TestDev_Balanced.tsv` | [GQA](https://cs.stanford.edu/people/dorarad/gqa/download.html), balanced test-dev split |
+| `MSCOCO_KARPATHY_TEST.tsv` | [Karpathy splits](https://cs.stanford.edu/people/karpathy/deepimagesent/) and [COCO images](https://cocodataset.org/#download) |
+| `FLICKR30K_KARPATHY_TEST.tsv` | [Karpathy splits](https://cs.stanford.edu/people/karpathy/deepimagesent/) and [Flickr30k images](https://shannon.cs.illinois.edu/DenotationGraph/) |
 
-| Asset | Actual or required server path | Availability |
-| --- | --- | --- |
-| LCS raw annotations | `/cache/vision_encoder_eval_data/datasets/lcs558k/annotations/blip_laion_cc_sbu_558k.json` | Present; the active MLLM recipe also retains `/cache/data/instructions/pretrain/blip_laion_cc_sbu_558k.json` |
-| LCS raw images | `/cache/vision_encoder_eval_data/datasets/lcs558k/images/` | Present; active recipe copy: `/cache/data/images/pretrain/` |
-| LCS-1000 visual patches | `/cache/vision_encoder_eval_data/features/lcs558k/n1000_seed42/ravel_recovered_patches/` | Present and published as `lcs558k/n1000_seed42/vision/` |
-| LCS-1000 baseline global vectors | `/cache/vision_encoder_eval_data/features/lcs558k/n1000_seed42/gw/outputs/gw_reproduction/features/vision/` | Present locally; not a separate published global-vector subset |
-| Current LCS-1000 mean-token text | `/home/ma-user/MLLM-VisionEncoder-Eval/runs/ravel_paper_features/b8608d4b92a2/` | Present and published as `lcs558k/n1000_seed42/text/` |
-| Complete published LCS-10000 block layout | `/cache/hf_visionencoder_features_n10000_20261006/payload/lcs558k/n10000_seed42plus43/` | Present; prepared upload copy of the original organized caches, with `vision/`, `text/`, and `samples/` matching the Hub layout |
-| ImageNet200k pooled features / labels / source indices | `/cache/vision_encoder_eval_data/features/imagenet1k/train200k/` | Present and published as `imagenet1k/train200k/` |
-| ImageNet kNN split protocol | `/cache/vision_encoder_eval_data/protocols/knn/imagenet_train_195pool_5query_seed42.json` | Present; released counterpart: `imagenet1k/train200k/protocols/knn_seed42.json` |
-| MLLM finetune data | `/cache/data/instructions/finetune/llava_v1_5_mix665k_drop_ge8kchars.json` and `/cache/data/images/finetune/` | Required, missing |
-| All 11 MLLM test sets | `/cache/data/instructions/test/`, `/cache/data/images/test/`, runtime view `/cache/data/.lmudata/` | Required, missing; `/home/ma-user/LMUData/` exists but is empty |
-| Raw ImageNet train/val and validation feature cache | Configure `paths.imagenet` to a populated root with `train/`, `val/`, `extra/` | Not configured to a usable data root; the available 200k training export does not supply validation data |
-| COCO alignment data, DreamLIP CC3M, SPair-71k, TokBench | COCO TSV: `/cache/data/instructions/test/MSCOCO_KARPATHY_TEST.tsv`; CC3M: configure `paths.cc3m`; SPair: `/cache/data/SPair-71k`; TokBench: configure its image/annotation root | Required raw data not found in the inspected project inventory; `paths.cc3m` / `paths.tokbench` remain placeholders |
+Prepare images referenced by the TSVs under `data/images/test/`. Folder names follow the benchmark loader: for example, MMBench v1.1 uses `MMBench_V11/`. TSVs with embedded images can be decoded by the loader. GQA also accepts the filename `GQA_TESTDEV_BALANCED.tsv`.
 
-Paths in `FILES.json`, `encoders.json`, and the download examples below are **relative to the downloaded Hub snapshot**, not relative to the server's `features/` directory. The generic experiment templates also contain example cache paths: point each run to the selected array and ordered manifest explicitly. Keep the LCS-1000 mean-token and LCS-10000 historical last-token text settings distinct.
+Use the [VLMEvalKit dataset loaders](https://github.com/open-compass/VLMEvalKit/tree/main/vlmeval/dataset) for benchmark preparation. Original dataset downloads must be converted to the required TSV format; changing a filename alone is insufficient. Configuration and experiment commands are in the code repository's [Setup](https://github.com/JuntaoTang/MLLM-VisionEncoder-Eval#setup) and [Running](https://github.com/JuntaoTang/MLLM-VisionEncoder-Eval#running) sections.
 
 ## Dataset Structure
 
@@ -179,42 +140,20 @@ Paths in `FILES.json`, `encoders.json`, and the download examples below are **re
 Total prepared contents, including shared metadata and protocols: approximately **668.18 GiB**. NumPy sizes include their headers; no extra precision conversion was applied.
 
 ```text
-README.md
-LICENSES.md
-encoders.json
-FILES.json
-checksums.sha256
-lcs558k/n1000_seed42/
-  samples/
-    samples.jsonl
-    manifest.json
-    images_manifest.json
+features/
+  lcs558k/
+    n1000_seed42/{vision,text,samples}/
+    n10000_seed42plus43/{vision,text,samples}/
+  imagenet1k/train200k/
+    features/
+    metadata/
+    samples/
+    protocols/
+    labels.npy
     source_indices.npy
-  vision/
-    <tokenizer_id>_patch_n1000_seed42.npy
-    <tokenizer_id>_patch_n1000_seed42.json
-  text/
-    <llm_id>_penultimate_mean_n1000_seed42.npy
-    <llm_id>_penultimate_mean_n1000_seed42.audit.json
-lcs558k/n10000_seed42plus43/
-  feature_manifest.json
-  samples/{samples.jsonl,manifest.json,source_indices.npy}
-  samples/part0_seed42/{samples.jsonl,manifest.json,source_indices.npy}
-  samples/part1_seed43_disjoint/{samples.jsonl,manifest.json,source_indices.npy}
-  samples/{disjoint_sample_audit.json,index_semantics.json}
-  vision/<tokenizer_id>/{part0_seed42,part1_seed43_disjoint}.npy
-  vision/<tokenizer_id>/{part0_seed42,part1_seed43_disjoint}.json
-  text/<llm_id>/{part0_seed42,part1_seed43_disjoint}_penultimate_lasttok.npy
-  text/<llm_id>/{part0_seed42,part1_seed43_disjoint}_penultimate_lasttok.audit.json
-imagenet1k/train200k/
-  samples/{samples.jsonl,manifest.json}
-  features/<rank>_<tokenizer_id>.npy
-  metadata/<rank>_<tokenizer_id>.json
-  labels.npy
-  source_indices.npy
-  export_manifest.json
-  manifest.tsv
-  protocols/{knn_seed42.json,generation_receipt.json}
+  encoders.json
+  FILES.json
+  checksums.sha256
 ```
 
 ### LCS-558K: 1,000 paired examples, seed 42
@@ -268,7 +207,7 @@ The sample table contains `row_index`, `source_index`, and `label`. Each array's
 
 ## Download and Use
 
-Install `huggingface_hub` and `numpy`; install `datasets` to load the sample tables.
+Download into `features/` and keep the repository subfolders. File paths in `encoders.json` and `FILES.json` are relative to this folder. Install `huggingface_hub` and `numpy`; install `datasets` to load the sample tables.
 
 ### Download one encoder with aligned text features
 
@@ -287,7 +226,7 @@ root = Path(snapshot_download(
         "lcs558k/n1000_seed42/vision/clip_openai__l14*",
         "lcs558k/n1000_seed42/text/qwen3*",
     ],
-    local_dir="VisionEncoder-Features",
+    local_dir="features",
 ))
 base = root / "lcs558k/n1000_seed42"
 vision = np.load(base / "vision/clip_openai__l14_patch_n1000_seed42.npy",
@@ -316,7 +255,7 @@ root = Path(snapshot_download(
         "lcs558k/n10000_seed42plus43/vision/clip_openai__l14/**",
         "lcs558k/n10000_seed42plus43/text/qwen3/**",
     ],
-    local_dir="VisionEncoder-Features",
+    local_dir="features",
 ))
 base = root / "lcs558k/n10000_seed42plus43"
 blocks = ["part0_seed42", "part1_seed43_disjoint"]
@@ -352,7 +291,7 @@ root = Path(snapshot_download(
         "imagenet1k/train200k/samples/**",
         "imagenet1k/train200k/protocols/**",
     ],
-    local_dir="VisionEncoder-Features",
+    local_dir="features",
 ))
 base = root / "imagenet1k/train200k"
 features = np.load(base / "features/001_clip_openai__l14.npy",

checksums.sha256

@@ -1,6 +1,6 @@
 84ad34bd38f46da0d08198fa7ce7315065ab8ed0e40c0f945d4b3ad001a2adac  .gitattributes
 da50ab668f491868ce523b2d62fdd65de12e047163150a11f2bd5ba05857ed38  LICENSES.md
-cb29f8e132e3b272baf302dd233b727be35be603cd26b3566cf639e59564adab  README.md
+9936a9ce2b3f2044402b28451c1d887466e5506071ebcbdea4fd008a4ead01e5  README.md
 cb69aaf14da9dc22644c914a1b40892d1e025cc25a4d494a4a6f2ba71bbbf41f  encoders.json
 54b5e86fc0a55f5dd4e00d973dd47c626e1c7d9163ae389a90eb513d6f748633  imagenet1k/train200k/export_manifest.json
 c0f64e28098e460ee5cd5716fefd2b1577c54755c5c3ea80dda9a843182ec9fd  imagenet1k/train200k/features/001_clip_openai__l14.npy