Pith Number

pith:OXRYN2TO

pith:2026:OXRYN2TOQOYECO5M3HGTWP3DQL

not attested not anchored not stored refs pending

Diet Your LLM: Dimension-wise Global Pruning of LLMs via Merging Task-specific Importance Score

Jaehyung Kim, Jimyung Hong

DIET prunes LLM dimensions by merging per-task activation scores into one global mask without any training.

arxiv:2603.23985 v3 · 2026-03-25 · cs.LG

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\usepackage{pith}
\pithnumber{OXRYN2TOQOYECO5M3HGTWP3DQL}

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Record completeness

1 Bitcoin timestamp

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

at 20% sparsity on Gemma-2 2B, DIET achieves near 10% average accuracy improvement, compared to previous state-of-the-art structured pruning methods.

C2weakest assumption

that activation magnitudes computed from only 100 samples per task are sufficient to produce a reliable global importance ranking that generalizes across unseen tasks and inputs.

C3one line summary

DIET prunes LLMs dimension-wise by merging task-specific activation importance scores via majority voting on 100 samples per task, yielding up to 10% accuracy gains over prior structured methods at 20% sparsity on Gemma-2 models.

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-27T01:05:53.464958Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

75e386ea6e83b0413bacd9cd3b3f6382e830d6c2bd36482b94a3b5f90829bd03

Aliases

arxiv: 2603.23985 · arxiv_version: 2603.23985v3 · doi: 10.48550/arxiv.2603.23985 · pith_short_12: OXRYN2TOQOYE · pith_short_16: OXRYN2TOQOYECO5M · pith_short_8: OXRYN2TO

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/OXRYN2TOQOYECO5M3HGTWP3DQL \
  | 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: 75e386ea6e83b0413bacd9cd3b3f6382e830d6c2bd36482b94a3b5f90829bd03

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "902a117a272fcd9238d3d408303513c06388bdfce6a2c2a3b59da6a271578778",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-03-25T06:28:58Z",
    "title_canon_sha256": "bb50e61da520dd9eb49619541867382880f6005196f6b1f634af04c91e99cee8"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2603.23985",
    "kind": "arxiv",
    "version": 3
  }
}