Pith Number

pith:UX46GXRW

pith:2026:UX46GXRWIS4VKM7QMLCCIJZTMN

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Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse

Meng Li, Runsheng Wang, Wenxuan Zeng, Zizhuo Fu

Attention sinks in transformers naturally build a Mixture-of-Experts structure inside attention layers.

arxiv:2602.01203 v3 · 2026-02-01 · cs.CL · cs.LG

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Record completeness

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3 Author claim open · sign in to claim

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

the sink in Vanilla Attention and Sink Attention naturally construct a Mixture-of-Experts (MoE) mechanism within attention layers. This insight explains the head collapse phenomenon observed in prior work.

C2weakest assumption

That the attention sink directly and naturally constructs an MoE routing mechanism whose load imbalance is the primary driver of head collapse, as supported by the paper's theoretical and empirical analysis.

C3one line summary

Attention sinks forge native MoE mechanisms in attention layers that cause head collapse, addressed by sink-aware training with auxiliary load balancing.

Cited by

2 papers in Pith

Attention Sinks Induce Gradient Sinks: Massive Activations as Gradient Regulators in Transformers

Attention Sink in Transformers: A Survey on Utilization, Interpretation, and Mitigation

Receipt and verification

First computed	2026-05-28T01:04:36.041742Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

a5f9e35e3644b95533f062c424273363704a3ca9598782a6f3086ef0fed79c93

Aliases

arxiv: 2602.01203 · arxiv_version: 2602.01203v3 · doi: 10.48550/arxiv.2602.01203 · pith_short_12: UX46GXRWIS4V · pith_short_16: UX46GXRWIS4VKM7Q · pith_short_8: UX46GXRW

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/UX46GXRWIS4VKM7QMLCCIJZTMN \
  | 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: a5f9e35e3644b95533f062c424273363704a3ca9598782a6f3086ef0fed79c93

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "3993724ea628eb474a133aef2da3ce370a1e4f18fada5ac2582c42502195f881",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2026-02-01T12:45:39Z",
    "title_canon_sha256": "f0e4597edda6453bd6130d3f79b5166166002047f45d173e47af6ab320b2c958"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2602.01203",
    "kind": "arxiv",
    "version": 3
  }
}