Pith Number

pith:T7HKF57J

pith:2026:T7HKF57JPFELS26FHRROLJJGPS

not attested not anchored not stored refs resolved

SemaTune: Semantic-Aware Online OS Tuning with Large Language Models

Georgios Liargkovas, Hubertus Franke, Kostis Kaffes, Mihir Nitin Joshi

SemaTune uses language models to reason over OS knob meanings and history, delivering 72.5 percent better stable performance than defaults across 13 workloads.

arxiv:2605.15026 v1 · 2026-05-14 · cs.OS · cs.AI · cs.PF

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\pithnumber{T7HKF57JPFELS26FHRROLJJGPS}

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Record completeness

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

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

Across the suite, SemaTune improves stable-phase performance by 72.5% over default settings and by 153.3% relative to the strongest non-LLM baseline.

C2weakest assumption

That the LLM-proposed changes, after passing typed validation, will reliably improve or maintain performance without entering persistent degraded regions, particularly when relying only on host-level metrics.

C3one line summary

SemaTune uses LLM guidance with semantic context to tune up to 41 Linux OS parameters, delivering 72.5% performance gains over defaults and 153.3% over non-LLM baselines on 13 workloads while avoiding degraded states.

References

93 extracted · 93 resolved · 4 Pith anchors

[1] PhD thesis, Inria Rennes-Bretagne Atlantique, 2019 2019

[2] Improving storage systems using machine learning.ACM Transactions on Storage, 19(1):1– 30, 2023 2023

[3] A machine learning framework to improve storage system performance 2021

[4] Cose: Configuring serverless functions using statistical learning 2020

[5] {CherryPick}: Adap- tively unearthing the best cloud configurations for big data analytics 2017

Receipt and verification

First computed	2026-05-17T23:38:54.635760Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

9fcea2f7e97948b96bc53c62e5a5267c97c12695d7838e5d93e3e94759de2f00

Aliases

arxiv: 2605.15026 · arxiv_version: 2605.15026v1 · doi: 10.48550/arxiv.2605.15026 · pith_short_12: T7HKF57JPFEL · pith_short_16: T7HKF57JPFELS26F · pith_short_8: T7HKF57J

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/T7HKF57JPFELS26FHRROLJJGPS \
  | 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: 9fcea2f7e97948b96bc53c62e5a5267c97c12695d7838e5d93e3e94759de2f00

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "a19a747b1a82d30c6cbbf5a0749fdd658c69e1a507532e845a4a4a0a3e29ea3d",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.PF"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.OS",
    "submitted_at": "2026-05-14T16:25:32Z",
    "title_canon_sha256": "197a71d8e71152f43bf5f65df13d6ec8f5c652c0febee1f4e7683d6a75f8156b"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15026",
    "kind": "arxiv",
    "version": 1
  }
}