Paper Citation Record · LEDGER

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

As of 10 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 36 inbound Pith citation observations for arXiv:2502.06768.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.06768 v3

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T14:30:26.749910Z

measured 83 of 83 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 36 of 36 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T04:14:52.223396Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

47 of 47 outbound references displayed

verified exact0
verified fuzzy15
unresolved32
parse uncertain0
malformed identifier0
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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation a99380a0-57d6-40b8-93f1-32d8822d3733 · outbound

This paper cites Hardness of sampling solutions from the Symmetric Binary Perceptron.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Hardness of sampling solutions from the Symmetric Binary Perceptron

Reference 1

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Observation 0537716a-70b0-4789-bd12-d079e69bda9e · outbound

This paper cites The masking problem in this case amounts to an instance of SLPN with input dimension N and sample size in [Ω(N log N ), O(N 0.49k)].

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions The masking problem in this case amounts to an instance of SLPN with input dimension N and sample size in [Ω(N log N ), O(N 0.49k)]

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:30:27.589490Z

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Observation 09a4ea35-a5b6-47a6-8c97-537d9f784b97 · outbound

This paper cites Convergence Analysis of Discrete Diffusion Model: Exact Implementation through Uniformization.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Convergence Analysis of Discrete Diffusion Model: Exact Implementation through Uniformization

Reference 3

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Observation 29a53bc6-7939-492a-ba15-b29b99d27b5f · outbound

This paper cites Scaling Diffusion Language Models via Adaptation from Autoregressive Models.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Scaling Diffusion Language Models via Adaptation from Autoregressive Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-08T14:30:26.552074Z

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Observation 3576d7e9-9c8b-4982-a9e4-3edd56ab00fc · outbound

This paper cites The Curious Case of Neural Text Degeneration.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions The Curious Case of Neural Text Degeneration

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-08T14:30:26.563081Z

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Observation 8a399de3-29b5-43b9-9e8e-5aca717eba9b · outbound

This paper cites Task Diversity Shortens the ICL Plateau.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Task Diversity Shortens the ICL Plateau

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-08T14:30:26.573991Z

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Observation 8c43c252-643c-4c56-9e67-9cc13f14db8b · outbound

This paper cites Discrete Copula Diffusion.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Discrete Copula Diffusion

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-08T14:30:26.579065Z

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Observation fd094fe7-6892-41cb-8ce8-8b61edc15d6d · outbound

This paper cites Think While You Generate: Discrete Diffusion with Planned Denoising.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Think While You Generate: Discrete Diffusion with Planned Denoising

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-08T14:30:26.583976Z

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Observation 20e8a1e7-8bfa-46be-9fdb-ee765c32c0f1 · outbound

This paper cites Large Language Diffusion Models.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Large Language Diffusion Models

Reference 15

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Observation 3913cad7-113c-4ba6-9046-e4215344753f · outbound

This paper cites Arrows of Time for Large Language Models.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Arrows of Time for Large Language Models

Reference 17

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Observation d7d0c6ac-993e-48b9-8995-6d8c8c04bd5e · outbound

This paper cites Z., Bezemek, Z., Patel, S., Yao, S., Rector- Brooks, J., Tong, A., and Chatterjee, P.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Z., Bezemek, Z., Patel, S., Yao, S., Rector- Brooks, J., Tong, A., and Chatterjee, P

Reference 18

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Observation c0d9fbef-9157-4304-882e-22ccd31abed2 · outbound

This paper cites Rector-Brooks, J., Hasan, M., Peng, Z., Quinn, Z., Liu, C., Mittal, S., Dziri, N., Bronstein, M., Bengio, Y ., Chatterjee, P., et al.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Rector-Brooks, J., Hasan, M., Peng, Z., Quinn, Z., Liu, C., Mittal, S., Dziri, N., Bronstein, M., Bengio, Y ., Chatterjee, P., et al

Reference 19

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Observation 8974803b-2a83-44a4-b891-b5bcd1173543 · outbound

This paper cites An Overview of Multi-Task Learning in Deep Neural Networks.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions An Overview of Multi-Task Learning in Deep Neural Networks

Reference 20

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Observation eb7ba4f9-f5bb-4b64-892e-2fc9f3e31ed4 · outbound

This paper cites Simple Guidance Mechanisms for Discrete Diffusion Models.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Simple Guidance Mechanisms for Discrete Diffusion Models

Reference 21

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Observation 080dafbd-fd5b-47ee-af33-e71bfdf5d486 · outbound

This paper cites Causal Language Modeling Can Elicit Search and Reasoning Capabilities on Logic Puzzles.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Causal Language Modeling Can Elicit Search and Reasoning Capabilities on Logic Puzzles

Reference 22

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Observation d7718d30-0132-4a37-9a28-b545048721a7 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 23

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Observation 747b8ab0-a13e-4921-b6cb-fb2d19336a54 · outbound

This paper cites Glauber Generative Model: Discrete Diffusion Models via Binary Classification.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Glauber Generative Model: Discrete Diffusion Models via Binary Classification

Reference 24

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Observation 81cc45e6-46db-49a7-8546-d7f7c832866c · outbound

This paper cites Energy-Based Diffusion Language Models for Text Generation.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Energy-Based Diffusion Language Models for Text Generation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-08T14:30:26.643674Z

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Observation db06a411-7239-4560-acec-193bc9737f02 · outbound

This paper cites Beyond Autoregression: Discrete Diffusion for Complex Reasoning and Planning.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Beyond Autoregression: Discrete Diffusion for Complex Reasoning and Planning

Reference 26

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Observation e3692237-7bc8-44bc-9571-a8bb078fada1 · outbound

This paper cites TinyLlama: An Open-Source Small Language Model.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions TinyLlama: An Open-Source Small Language Model

Reference 27

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Observation f472a23a-6263-4708-a269-052cc1f7914d · outbound

This paper cites Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling

Reference 28

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Observation 49205f45-5c7d-4a25-8f2b-013ccd93bc00 · outbound

This paper cites A Reparameterized Discrete Diffusion Model for Text Generation.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions A Reparameterized Discrete Diffusion Model for Text Generation

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-08T14:30:26.663511Z

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Observation 29a22153-4f1e-4320-9340-2ba4dbb5dc01 · outbound

This paper cites left-to-right.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions left-to-right

Reference 31

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Observation fb0c09e8-1f73-4296-b6c0-6c26eac71244 · outbound

This paper cites left-to-right.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions left-to-right

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:30:27.644984Z

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Observation 71d17a1d-f2ed-44dd-90b6-33477054b7ec · outbound

This paper cites Recently, discrete diffusion models have emerged as a promising approach for discrete data apart from autoregressive models.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Recently, discrete diffusion models have emerged as a promising approach for discrete data apart from autoregressive models

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:30:27.630597Z

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Observation ba5d92fa-a074-4e4b-b9c3-e2b1ab5b581f · outbound

This paper cites Building on these intuitions, (Shih et al., 2022; Hoogeboom et al., 2021a) proposed any-order modeling, which allows a model to generate in any desired order.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Building on these intuitions, (Shih et al., 2022; Hoogeboom et al., 2021a) proposed any-order modeling, which allows a model to generate in any desired order

Reference 34

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Observation 649038e1-f737-4c0e-ac4e-e9624d1a12a0 · outbound

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Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Unresolved cited work

Reference 35

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Observation 638059af-1f93-41f3-aeaf-7ef5f4a40e76 · outbound

This paper cites Theorem B.8 (Theorem 2.1 in (Alaoui & Gamarnik, 2024)4).

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Theorem B.8 (Theorem 2.1 in (Alaoui & Gamarnik, 2024)4)

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:30:27.575645Z

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Observation 083c517b-5ba7-4eb5-ac7c-eeee4712e83a · outbound

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Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Unresolved cited work

Reference 40

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Observation 160d0c7b-964c-4ba8-9fef-eec79366d879 · outbound

This paper cites The prediction is that in this regime, no efficient algorithm can achieve optimal recovery (Krzakala & Zdeborov´a, 2009).

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions The prediction is that in this regime, no efficient algorithm can achieve optimal recovery (Krzakala & Zdeborov´a, 2009)

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:30:27.519217Z

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Observation 160aa1b7-dfed-446a-8a18-6aa31d5e2d4f · outbound

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Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Unresolved cited work

Reference 42

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Observation 51fc588f-019f-432d-a00e-bb8147138038 · outbound

This paper cites In particular, we use AdamW optimizer (Loshchilov & Hutter, 2017), setting β1 = 0.9, β2 = 0.95, and a weight decay of 0.1 and L =.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions In particular, we use AdamW optimizer (Loshchilov & Hutter, 2017), setting β1 = 0.9, β2 = 0.95, and a weight decay of 0.1 and L =

Reference 43

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Observation e3bdcbc2-c265-4c1f-a4f6-fd06569fd117 · outbound

This paper cites For the Sudoku dataset, we use 6M GPT-2 model, and for the Zebra dataset, we use 19M model.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions For the Sudoku dataset, we use 6M GPT-2 model, and for the Zebra dataset, we use 19M model

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:30:27.441627Z

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Observation adc66e30-97da-4ea4-bdb6-e41e5ae78fb3 · outbound

This paper cites Let x(n) be a sequence with n tokens being masked from x0, and xi(n) denotes the ith token value of the sequence x(n).

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Let x(n) be a sequence with n tokens being masked from x0, and xi(n) denotes the ith token value of the sequence x(n)

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:30:27.426534Z

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Observation f4311bb5-79db-4f82-9ed8-049ef7b77a57 · outbound

This paper cites 3.3 this implies a range of masking fractions at which Ω(1) fraction of masking problems are computationally hard.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions 3.3 this implies a range of masking fractions at which Ω(1) fraction of masking problems are computationally hard

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:30:27.561832Z

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Observation 4ae739bd-465e-49fd-91db-70bf36db96aa · outbound

This paper cites To attain a proxy MDM for the Bayes optimal predictor, we further train it for 5 × 104 iterations.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions To attain a proxy MDM for the Bayes optimal predictor, we further train it for 5 × 104 iterations

Reference 512

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:30:27.456127Z

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Observation b73e98bd-08c8-49b5-921f-4760f318d6fe · outbound

This paper cites Scaling up Masked Diffusion Models on Text.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Scaling up Masked Diffusion Models on Text

Reference 2008

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Observation 5fe1ab87-98ac-40b2-a22d-729a381255dd · outbound

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Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Unresolved cited work

Reference 2009

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:30:27.547087Z

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Observation ab7b43b3-8d12-47ab-806d-25df3894b371 · outbound

This paper cites BERT: Pre-training of deep bidirectional transformers for lan- guage understanding.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions BERT: Pre-training of deep bidirectional transformers for lan- guage understanding

Reference 2011

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:30:27.686837Z

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Observation d98187dd-a582-42f6-8540-bbb602a7fdbd · outbound

This paper cites Autoregressive Diffusion Models.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Autoregressive Diffusion Models

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-08T14:30:26.568575Z

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Observation 7e72164c-a837-4f8b-b1ec-18f6749ab3c5 · outbound

This paper cites Training Compute-Optimal Large Language Models.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Training Compute-Optimal Large Language Models

Reference 2020

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Observation 216d8b06-891c-496d-8dda-f2f0d45217ea · outbound

This paper cites Reverse Training to Nurse the Reversal Curse.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Reverse Training to Nurse the Reversal Curse

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-08T14:30:26.546360Z

Source-reported events for the cited work

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Observation c4f92b62-e5ad-434d-91f3-0d056214a6e3 · outbound

This paper cites Efficient Training of Language Models to Fill in the Middle.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Efficient Training of Language Models to Fill in the Middle

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-08T14:30:26.525629Z

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Observation 0ba1a31a-e36c-44b9-a518-7cb3b123c37c · outbound

This paper cites Related works Discrete diffusion models.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Related works Discrete diffusion models

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:30:27.672921Z

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Observation 0a407f53-3870-479f-a04c-a46cc2441bd5 · outbound

This paper cites Premise Order Matters in Reasoning with Large Language Models.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Premise Order Matters in Reasoning with Large Language Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-08T14:30:26.536327Z

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Observation e0579f42-412d-4bf4-b0e9-f001140f1390 · outbound

This paper cites Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-08T14:30:26.599525Z

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Observation 5ae789bc-d2b7-490d-a1eb-89e911c623ad · outbound

This paper cites We also note that unless otherwise specified, we maintain the same training configuration throughout the paper.

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions We also note that unless otherwise specified, we maintain the same training configuration throughout the paper

Reference 2048

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:30:27.470404Z

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Pith citing papers

Observation 73e3c32e-5a1d-46d3-b944-e5ee4a85b4b8 · inbound

Theoretical Benefit and Limitation of Diffusion Language Model cites this paper.

Theoretical Benefit and Limitation of Diffusion Language Model Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 30

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Observation 2ef2e5c8-0bcf-4e0b-8515-c33e7a294742 · inbound

dKV-Cache: The Cache for Diffusion Language Models cites this paper.

dKV-Cache: The Cache for Diffusion Language Models Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 27

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Observation 43f65ed7-2bae-46c6-a305-931092ef3461 · inbound

Sudoku-Bench: Evaluating creative reasoning with Sudoku variants cites this paper.

Sudoku-Bench: Evaluating creative reasoning with Sudoku variants Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-07T15:08:29.031812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e911319-7361-4d72-8b4a-5841c0f56d71 · inbound

Accelerated Sampling from Masked Diffusion Models via Entropy Bounded Unmasking cites this paper.

Accelerated Sampling from Masked Diffusion Models via Entropy Bounded Unmasking Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T12:35:39.784074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 449b9824-08f5-4a6f-8112-d8945380d6c1 · inbound

Any-Order Flexible Length Masked Diffusion cites this paper.

Any-Order Flexible Length Masked Diffusion Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T13:20:05.294669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1553b1ed-37af-4d21-b099-ebc5fba74812 · inbound

Revolutionizing Reinforcement Learning Framework for Diffusion Large Language Models cites this paper.

Revolutionizing Reinforcement Learning Framework for Diffusion Large Language Models Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T22:56:05.196551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b6bd5dd-0b28-436a-8044-aa8bd7b12cb1 · inbound

Fine-Tuning Masked Diffusion for Provable Self-Correction cites this paper.

Fine-Tuning Masked Diffusion for Provable Self-Correction Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T13:15:31.716146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56bfbda1-683b-4fb9-8d47-3eedf29945a3 · inbound

Coevolutionary Continuous Discrete Diffusion: Make Your Diffusion Language Model a Latent Reasoner cites this paper.

Coevolutionary Continuous Discrete Diffusion: Make Your Diffusion Language Model a Latent Reasoner Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-18T10:16:14.022179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fec7feb1-b6b8-4129-9369-9fee9c7b174e · inbound

Orchestrating Dual-Boundaries: An Arithmetic Intensity Inspired Acceleration Framework for Diffusion Language Models cites this paper.

Orchestrating Dual-Boundaries: An Arithmetic Intensity Inspired Acceleration Framework for Diffusion Language Models Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T06:48:07.504557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fbc823c4-a07f-4cbd-9dd9-d894542b9dca · inbound

The Flexibility Trap: Rethinking the Value of Arbitrary Order in Diffusion Language Models cites this paper.

The Flexibility Trap: Rethinking the Value of Arbitrary Order in Diffusion Language Models Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T09:03:18.430987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f209714-d395-4eef-9b0d-3360128e187d · inbound

Can I Have Your Order? Monte-Carlo Tree Search for Slot Filling Ordering in Diffusion Language Models cites this paper.

Can I Have Your Order? Monte-Carlo Tree Search for Slot Filling Ordering in Diffusion Language Models Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T23:50:35.687236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27b1c985-13df-4f1d-8d20-52e66d90387f · inbound

Improving Sampling for Masked Diffusion Models via Information Gain cites this paper.

Improving Sampling for Masked Diffusion Models via Information Gain Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-25T07:35:29.553802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e9b30012-3594-4bb1-b7f0-c952572a09ec · inbound

NI Sampling: Accelerating Discrete Diffusion Sampling by Token Order Optimization cites this paper.

NI Sampling: Accelerating Discrete Diffusion Sampling by Token Order Optimization Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-10T06:01:13.651543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2a270bd4-ece4-49c9-9000-5c95cf327229 · inbound

Dream-Cubed: Controllable Generative Modeling in Minecraft by Training on Billions of Cubes cites this paper.

Dream-Cubed: Controllable Generative Modeling in Minecraft by Training on Billions of Cubes Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-11T13:31:02.693815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b73b694c-31ba-44c9-9f19-96b90f55ac1d · inbound

Leveraging Pretrained Language Models as Energy Functions for Glauber Dynamics Text Diffusion cites this paper.

Leveraging Pretrained Language Models as Energy Functions for Glauber Dynamics Text Diffusion Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:55:40.945408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bcf2ee0a-9d5c-43b2-8c08-3f2ac6e6a856 · inbound

DVD: Discrete Voxel Diffusion for 3D Generation and Editing cites this paper.

DVD: Discrete Voxel Diffusion for 3D Generation and Editing Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:20:56.041772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 562dd056-20fa-44c6-b398-6f276be30f7e · inbound

DVD: Discrete Voxel Diffusion for 3D Generation and Editing cites this paper.

DVD: Discrete Voxel Diffusion for 3D Generation and Editing Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-07-01T13:35:45.865646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9b467d38-df70-4eb6-80ab-ba9ba05acfc1 · inbound

BadDLM: Backdooring Diffusion Language Models with Diverse Targets cites this paper.

BadDLM: Backdooring Diffusion Language Models with Diverse Targets Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:11:26.021507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 53fe7f81-04c3-4510-8d9b-59349b00f96c · inbound

Differences in Text Generated by Diffusion and Autoregressive Language Models cites this paper.

Differences in Text Generated by Diffusion and Autoregressive Language Models Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:59:27.216349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 18a23232-b700-4907-b2d2-190b480ffa37 · inbound

Machine Unlearning for Masked Diffusion Language Models cites this paper.

Machine Unlearning for Masked Diffusion Language Models Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-20T10:28:12.183936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3eda5b08-5efa-4399-8b57-d744b69c60dc · inbound

Learned Relay Representations for Forward-Thinking Discrete Diffusion Models cites this paper.

Learned Relay Representations for Forward-Thinking Discrete Diffusion Models Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-05-25T06:00:22.949155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cbceaca5-1261-4336-9be3-144ec8575287 · inbound

Learned Relay Representations for Forward-Thinking Discrete Diffusion Models cites this paper.

Learned Relay Representations for Forward-Thinking Discrete Diffusion Models Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T17:24:56.657854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 61ab2ed5-f5e0-44d3-b4b2-8535f3cf6cc8 · inbound

Looped Diffusion Language Models cites this paper.

Looped Diffusion Language Models Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-06-29T23:14:01.233835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 723d248e-d4b7-4dc8-9b37-76a587a455f3 · inbound

Fixed-Point Masked Generative Modeling cites this paper.

Fixed-Point Masked Generative Modeling Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-06-28T23:22:47.311991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3b4c2b8a-c273-4092-9b07-3132d93f3b52 · inbound

Adaptive Order Policies for Masked Diffusion cites this paper.

Adaptive Order Policies for Masked Diffusion Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 125

Resolution: verified exact; arxiv_id, observed 2026-06-28T23:42:49.852077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 447343f6-155d-4d50-8f20-76e34eff5d9d · inbound

Greedy Coordinate Diffusion: Effective and Semantically Coherent Adversarial Attacks via Diffusion Guidance cites this paper.

Greedy Coordinate Diffusion: Effective and Semantically Coherent Adversarial Attacks via Diffusion Guidance Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-07-03T17:08:43.456267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cc42d0f7-66a2-433b-8808-5799b9d059f8 · inbound

VoidPadding: Let [VOID] Handle Padding in Masked Diffusion Language Models so that [EOS] Can Focus on Semantic Termination cites this paper.

VoidPadding: Let [VOID] Handle Padding in Masked Diffusion Language Models so that [EOS] Can Focus on Semantic Termination Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-07-03T21:18:59.415750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e33f9926-e033-49e2-95dd-43c885a76833 · inbound

HERALD: High-Throughput Block Diffusion LLM Serving via CPU-GPU Cooperative KV Cache Retrieval cites this paper.

HERALD: High-Throughput Block Diffusion LLM Serving via CPU-GPU Cooperative KV Cache Retrieval Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-04T06:09:37.893553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9ca19fe0-953c-4045-87e7-55dd8ff041cd · inbound

HERALD: High-Throughput Block Diffusion LLM Serving via CPU-GPU Cooperative KV Cache Retrieval cites this paper.

HERALD: High-Throughput Block Diffusion LLM Serving via CPU-GPU Cooperative KV Cache Retrieval Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T04:42:24.004422Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 141d10e3-4a78-4148-b518-d503eb1b40c1 · inbound

Posterior Refinement: Fast Language Generation via Any-Order Flow Maps cites this paper.

Posterior Refinement: Fast Language Generation via Any-Order Flow Maps Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-04T17:09:58.841357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c3da5f8a-1093-44ec-a48d-29ed37552aae · inbound

Flow Reasoning Models: Scaling Reasoning Through Iterative Self-Refinement cites this paper.

Flow Reasoning Models: Scaling Reasoning Through Iterative Self-Refinement Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T08:04:28.391758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2a17ccb4-4df9-4ffa-b53e-e7c6fda83cbc · inbound

Set Diffusion: Interpolating Token Orderings Between Autoregression and Diffusion for Fast and Flexible Decoding cites this paper.

Set Diffusion: Interpolating Token Orderings Between Autoregression and Diffusion for Fast and Flexible Decoding Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 126

Resolution: verified exact; arxiv_id, observed 2026-07-03T17:38:43.660283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation adf67ca4-000e-483b-bee8-d0b37172eda4 · inbound

Token Time Continuous Diffusion for Language Modeling cites this paper.

Token Time Continuous Diffusion for Language Modeling Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T14:47:38.747246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0763a10f-93ad-47f4-8df0-be35922051a6 · inbound

Induction in Both Directions: A Mechanistic Analysis of In-Context Learning in Masked Diffusion Language Models cites this paper.

Induction in Both Directions: A Mechanistic Analysis of In-Context Learning in Masked Diffusion Language Models Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T22:03:47.298320Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ad39670-df90-4732-a662-909b84523784 · inbound

From Interface to Inference: Eliciting Any-Order Inference from Any-Order Models cites this paper.

From Interface to Inference: Eliciting Any-Order Inference from Any-Order Models Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T14:25:36.566046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4fc0e3f-e488-4920-94dc-c5359c886ca1 · inbound

Retrofitting Linear Attention into Diffusion Language Models cites this paper.

Retrofitting Linear Attention into Diffusion Language Models Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-10T04:14:52.223396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.