Pith Number

pith:TFJX65WE

pith:2025:TFJX65WEILIMHMJLN7ZGHBRONJ

not attested not anchored not stored refs pending

Resting Neurons, Active Insights: Robustifying Activation Sparsity in LLMs via Spontaneity

Haotian Xu, Jiannan Yang, Tengfei Ma, Tian Gao, Tsui-Wei Weng

Spontaneous neurons restore accuracy in activation-sparse large language models by anchoring hidden states to the dense model's distribution.

arxiv:2512.12744 v4 · 2025-12-14 · cs.LG

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\usepackage{pith}
\pithnumber{TFJX65WEILIMHMJLN7ZGHBRONJ}

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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

We address this issue by reframing activation sparsity as a representational alignment problem and introducing Spontaneous Neurons (SPON), a lightweight mechanism... SPON consistently restores performance, stabilizes latent representations, and preserves generalization.

C2weakest assumption

That a small set of input-independent learnable vectors trained only via distribution matching to the dense model will reliably counteract the distribution shifts induced by activation sparsity without introducing new instabilities or harming generalization on downstream tasks.

C3one line summary

SPON adds learnable persistent activation anchors trained via distribution matching to restore LLM accuracy under high activation sparsity by preventing representational distribution shifts.

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-22T01:03:52.236672Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

99537f76c442d0c3b12b6ff263862e6a6f5452d15ed86b1107df035c64a92d18

Aliases

arxiv: 2512.12744 · arxiv_version: 2512.12744v4 · doi: 10.48550/arxiv.2512.12744 · pith_short_12: TFJX65WEILIM · pith_short_16: TFJX65WEILIMHMJL · pith_short_8: TFJX65WE

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/TFJX65WEILIMHMJLN7ZGHBRONJ \
  | 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: 99537f76c442d0c3b12b6ff263862e6a6f5452d15ed86b1107df035c64a92d18

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "d8dec47c6276a44f7b1f062884957537b198d25b37af4e65f5bc3c7cc06ad776",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2025-12-14T15:47:40Z",
    "title_canon_sha256": "8f95beb995634cbf53f41f19afbac3831510c7245126bf97add8c16fba9bd337"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2512.12744",
    "kind": "arxiv",
    "version": 4
  }
}