Pith Number

pith:KNP64ZBZ

pith:2026:KNP64ZBZ6XL4CYNC4HGUBXMNWZ

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LoKA: Low-precision Kernel Applications for Recommendation Models At Scale

Buyun Zhang, Chunqiang Tang, Chunzhi Yang, Ellie Wen, Jian Jiao, Jiecao Yu, Liang Luo, Maxim Naumov, Neng Shi, Quanyu Zhu, Sandeep Parab, Santanu Kolay, Shen Li, Tongyi Tang, Vasiliy Kuznetsov, Venkatesh Ranganathan, Wenlin Chen, Xiaohan Wei, Yanli Zhao, Yantao Yao, Yinbin Ma, Yuchen Hao, Yuxin Chen, Zeliang Chen

LoKA framework makes FP8 practical for large recommendation models by profiling safe sites, adapting components, and dispatching kernels.

arxiv:2605.10886 v3 · 2026-05-11 · cs.LG · cs.AI

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4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

LoKA makes FP8 practical for LRMs through three principles: profile under realistic distributions to know where low precision is safe, co-design model components with hardware to expand where it is safe, and orchestrate across kernel libraries to maximize the gains.

C2weakest assumption

That the statistical profiling from LoKA Probe accurately identifies all safe FP8 sites without missing interactions or distribution shifts that would degrade overall model quality during full training.

C3one line summary

LoKA enables practical FP8 use in numerically sensitive large recommendation models via online profiling of activations, reusable model modifications for stability, and dynamic kernel dispatching.

Receipt and verification

First computed	2026-07-10T00:18:45.425954Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

535fee6439f5d7c161a2e1cd40dd8db6540e05c660eff1da1c1daa67aa52e3ae

Aliases

arxiv: 2605.10886 · arxiv_version: 2605.10886v3 · doi: 10.48550/arxiv.2605.10886 · pith_short_12: KNP64ZBZ6XL4 · pith_short_16: KNP64ZBZ6XL4CYNC · pith_short_8: KNP64ZBZ

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/KNP64ZBZ6XL4CYNC4HGUBXMNWZ \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 535fee6439f5d7c161a2e1cd40dd8db6540e05c660eff1da1c1daa67aa52e3ae

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "5be490cd6aca8747f8d9229bc46a30a38d0f817cd17a79206fac2e03d256972a",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-11T17:32:29Z",
    "title_canon_sha256": "4751061f62e705cdf6a3b9d3f4a74c5f44efd30e84183a009b3d30bc05e82554"
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  "schema_version": "1.0",
  "source": {
    "id": "2605.10886",
    "kind": "arxiv",
    "version": 3
  }
}