pith:Q4O5LULE
Early Decisions Matter: Proximity Bias and Initial Trajectory Shaping in Non-Autoregressive Diffusion Language Models
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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\usepackage{pith}
\pithnumber{Q4O5LULEOMMCUSYUHYXYHNBUY6}
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Claims
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.
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.
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
· · · · ·Agent API
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
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