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pith:Q4O5LULE

pith:2026:Q4O5LULEOMMCUSYUHYXYHNBUY6
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Early Decisions Matter: Proximity Bias and Initial Trajectory Shaping in Non-Autoregressive Diffusion Language Models

Jiyeon Kim, Minjoon Seo, Moontae Lee, Sungik Choi, Yongrae Jo

Proximity bias in non-autoregressive diffusion language models makes the full generation trajectory depend on the position of the first unmasked token.

arxiv:2604.10567 v2 · 2026-04-12 · cs.CL · cs.AI

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\pithnumber{Q4O5LULEOMMCUSYUHYXYHNBUY6}

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4 Citations open
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Claims

C1strongest claim

we uncover an inherent failure mode in confidence-based non-autoregressive generation stemming from a strong proximity bias-the tendency for the denoising order to concentrate on spatially adjacent tokens. This local dependency leads to spatial error propagation, rendering the entire trajectory critically contingent on the initial unmasking position.

C2weakest assumption

That the observed proximity bias is the dominant cause of poor non-autoregressive performance and that a lightweight planner plus temperature annealing will reliably correct it across tasks without introducing new failure modes or significant overhead.

C3one line summary

Non-autoregressive diffusion language models have an inherent proximity bias in token unmasking that causes spatial error propagation, which a minimal planner and annealing strategy can mitigate for better reasoning performance.

Receipt and verification
First computed 2026-05-28T01:04:39.941034Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

871dd5d16473182a4b143e2f83b434c7b4e3f6a1b433ab029c79fc2fcd7ec660

Aliases

arxiv: 2604.10567 · arxiv_version: 2604.10567v2 · doi: 10.48550/arxiv.2604.10567 · pith_short_12: Q4O5LULEOMMC · pith_short_16: Q4O5LULEOMMCUSYU · pith_short_8: Q4O5LULE
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/Q4O5LULEOMMCUSYUHYXYHNBUY6 \
  | 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: 871dd5d16473182a4b143e2f83b434c7b4e3f6a1b433ab029c79fc2fcd7ec660
Canonical record JSON
{
  "metadata": {
    "abstract_canon_sha256": "4eb935cda19702d74549a85de843af8a867fe59d846e7091674c1394005bbbb7",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2026-04-12T10:26:41Z",
    "title_canon_sha256": "3126823eead0555446e3cf9cb37fef1b3fcbefe7600e35a8e924695cd8f1619b"
  },
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  "source": {
    "id": "2604.10567",
    "kind": "arxiv",
    "version": 2
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}