chironYu/latenarray-i2ce-data

latenarray-I2CE data cache

The precomputed tensors behind the paper Invariant Game Representation Learning from Steam Reviews (AICCC 2026): cached sentence embeddings of Steam reviews, store pages and wiki articles for 2,020 games, with the anchor packs, the evaluation queries and the splits.

• Code, appendices and supplement: Nice9Tian/I2CE-GameRepresention-by-SteamReviews
• The trained towers: chironYu/latenarray-i2ce-checkpoints

The w9 workers of the code repository (contrast_experiment/w9/Pod/w9_cv_worker.py, w9_a100_worker.py) read this folder as their --data-dir. Download it, rebuild the full pool, and pass the folder as --data-dir:

hf download chironYu/latenarray-i2ce-data --repo-type dataset --local-dir latenarray-i2ce-data --exclude "*_rawprefix.*"
cd latenarray-i2ce-data && python join_full_pool.py

The --exclude leaves out two files (10 GiB) that only repeating a run made before 2026-11-07 needs; see Changes.

full_pool_fp16.npy (150.1 GB) is stored as four byte parts under 50 GB each, full_pool_fp16.npy.part00 to part03. join_full_pool.py concatenates them, checks the result against SHA256SUMS, writes full_pool_READY (the marker the w9 notebooks wait for) and deletes the parts (--keep-parts keeps them).

The packaged entry points of the code release (steam_reviews_framework/run.py, contrast_experiment/run.py) need neither the full pool nor the two wscan_gal_rev_g*.npz galleries, about 21 GiB in all; they rebuild the anchor packs at their budget from wscan_pool_rev.npy and full_pool_meta.npz:

hf download chironYu/latenarray-i2ce-data --repo-type dataset --local-dir data/assets \
  --exclude "full_pool_fp16.npy.part*" "wscan_gal_rev_g*.npz" "*_rawprefix.*"

The corpus covers 2,020 Steam games released 2020 to 2024: 6,649,325 reviews and 73,289,517 sentences. Every sentence is embedded once with Qwen/Qwen3-Embedding-0.6B (1,024-d, last-token pooling, no instruction prefix, not normalized) and stored as float16. The repository holds no review text, only vectors, offsets and identifiers.

Sentence vectors are stored raw in the full pool, the document views and the wiki queries; the workers row-standardise them (mean 0, std 1 over the 1,024 dimensions) when they load them. The review pool, the anchor packs and the pseudo-queries are stored already row-standardised.

File	Contents
full_pool_fp16.npy	All 73,289,517 sentence vectors, [73289517, 1024] float16 (139.8 GiB). Training views are drawn from it (--full-pool); the 4,096-sentence evaluation draws read it as well
full_pool_meta.npz	game_names (2,020), game_review_offsets (2,021), review_offsets (6,649,326): game g owns reviews game_review_offsets[g]:[g+1], review r owns sentence rows review_offsets[r]:[r+1]
wscan_pool_rev.npy	The 2,048-sentence review pool per game, [2020, 2048, 1024] float16: the 3 longest reviews of the game first (when they fit), then whole reviews at random; anchor packs above 512 sentences are rebuilt from it
wscan_pool_rev_len.npy, wscan_pool_rev_rid.npy	Pool lengths and review ids
wscan_gal_rev.npz, wscan_gal_rev_g1024.npz, wscan_gal_rev_g4096.npz	Prebuilt anchor galleries at 512, 1,024 and 4,096 sentences (the 4,096 one is a single draw used by the fixed-split worker): store-page prefix (gal_doc_len rows), then whole reviews; gal_len rows used. See Changes for the 512 one
wscan_gal_rev_rawprefix.npz	The 512-sentence galleries as built before 2026-11-07, store-page prefix not row-standardised. Only needed to repeat a run of that time: the w9 workers read it with --raw-prefix
games.npz	Game names (names, the game index of every other file) and per-game assets
wiki_eval.npz, wiki_eval_split.json	The evaluation queries: LLM rewrites of the wiki articles of the 814-game evaluation universe, four variants each (neutral, noname, positive, negative), and the split (seed 20260711)
wiki_clean_views.npz, wiki_llm_views.npz, sp_raw_views.npz	Document views: cleaned Wikipedia, LLM rewrites, store pages
ss_queries_rev.npz, ss_queries_rev_S.npy	Pseudo-queries: four anchor-shaped packs per game (store-page prefix, then whole pool reviews to 512 sentences), flat-stored, query j = S[off[j]:off[j+1]]. Report-only diagnostics; checkpoint selection and every published number use wiki_eval.npz instead
ss_queries_rev_S_rawprefix.npy	The pseudo-query vectors before the prefix fix (see Changes)
tag_labels.npz, _tag_splitM.json	Tag labels (23 coarse classes) and the tag-probe split

Changes

2026-11-07: store-page prefix row-standardised in wscan_gal_rev.npz and ss_queries_rev_S.npy. Both files put a game's sp_raw store-page sentences in front of its review sentences. The review sentences were row-standardised; the store-page sentences were copied raw (row std about 2.9 against 1.0), and the tower has no input normalisation. Every other input already reached it standardised, including the store-page prefix of the 1,024- and 4,096-sentence galleries and of the galleries the workers build on the GPU at 1,024 and 2,048 sentences. The two files now carry the prefix row-standardised exactly as the workers standardise sp_raw_views.npz when they load it; every review row, every length and the order are unchanged, bit for bit. All other files are unchanged.

The paper's 512-sentence rows come from towers trained on the current files; their checkpoints carry _d512 in their names, the w9 workers name a run on the current packs that way, and they stop when the wscan_gal_rev.npz they are given still holds the prefix raw. The files as they were are kept as wscan_gal_rev_rawprefix.npz and ss_queries_rev_S_rawprefix.npy, for repeating a run made before the change: pass the worker --raw-prefix, which reads the two files and names the run without the tag.

File	SHA-256 before	SHA-256 now
wscan_gal_rev.npz	e4ee5da46c5d7240065f02a3948c51c78ca9fe22d3145613c752589b4b8f789e (now wscan_gal_rev_rawprefix.npz)	7a304e875717a3cb9abffb554f460d75f20e8fea9d41b4cb1020f7096cafbe5b
ss_queries_rev_S.npy	2762f528c70173f2613aad397d14f2f3abf3e1ca453d5441551ac911abf37dd4 (now ss_queries_rev_S_rawprefix.npy)	413ebc66c15568ede34ecf2d7f1886a3420252e5533dfbb27c2db13efe247f67

Citation

Zhilong Yu et al. Invariant Game Representation Learning from Steam Reviews. 2026 9th Artificial Intelligence and Cloud Computing Conference (AICCC 2026), December 18 to 21, 2026, Tokyo, Japan.

@inproceedings{yu2026invariant,
  author    = {Yu, Zhilong and others},
  title     = {Invariant Game Representation Learning from Steam Reviews},
  booktitle = {2026 9th Artificial Intelligence and Cloud Computing Conference (AICCC 2026)},
  address   = {Tokyo, Japan},
  year      = {2026}
}

The tensors are under the MIT license.