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Paper Citation Record · LEDGER

LLaDA2.0: Scaling Up Diffusion Language Models to 100B

As of 5 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 81 inbound Pith citation observations for arXiv:2512.15745.

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

pith.paper-citation-record.v1
2512.15745 v2

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-14T18:53:20.911374Z

measured 124 of 124 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 81 of 81 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T00:36:12.980619Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-11T03:07:51.909523Z

Reference resolution

43 of 43 outbound references displayed

  • verified exact33
  • verified fuzzy2
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch8

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4175717d-ae91-498e-aca3-67f837348599 · outbound

This paper cites Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models

Reference 1

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arxiv_id, observed 2026-05-15T10:59:41.530185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 00d77b3d-fcfe-43bd-a303-d1ea44df15c1 · outbound

This paper cites Program Synthesis with Large Language Models.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B Program Synthesis with Large Language Models

Reference 2

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local_arxiv, observed 2026-05-14T18:53:20.952618Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation a6a1ef40-be6a-48ec-b87a-f1f86fe11a15 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B Evaluating Large Language Models Trained on Code

Reference 3

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local_arxiv, observed 2026-05-14T18:53:20.959850Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation d59b3921-b861-4374-83d4-93cc4dd8f2a9 · outbound

This paper cites DPad: Efficient Diffusion Language Models with Suffix Dropout.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B DPad: Efficient Diffusion Language Models with Suffix Dropout

Reference 4

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arxiv_id, observed 2026-05-14T18:53:20.966735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:1a5a82a59639ab1c3978c6bf14959da68a6f59cccf38d31d7060aad9574c53f7

Observation 3744a359-feec-4af0-9e1f-feb0c8c4d635 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 5

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local_arxiv, observed 2026-05-14T18:53:20.973081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:ab34f9da19ee8be5fdeda5f1b910179a8f3b6d2c46d6a0b92795ff780ed4fdcc

Observation a8172217-5d51-4ab9-84cf-7dd44761b983 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B Training Verifiers to Solve Math Word Problems

Reference 6

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local_arxiv, observed 2026-05-14T18:53:20.978083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:38188407bb1d1fad5bcbabe0aefad64d00c418468ab95c2e5297fac35416b91c

Observation e3f9aaf5-65a7-422b-a362-5f48195194ea · outbound

This paper cites DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

Reference 7

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local_arxiv, observed 2026-05-14T18:53:20.984790Z

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source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:768f39838b6ed657742e76b2ef0dfa77340182d5e7d3f5593ad3e924f0b725a3

Observation 17d1b9ca-423f-438e-a586-6552ca628b97 · outbound

This paper cites Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models

Reference 8

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arxiv_id, observed 2026-05-15T09:09:15.216863Z

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source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:9d42012b0ebbfb877b735248a8060a6a0a2492b8857c2d1f8bdd9238bd78de4f

Observation 81ff8d07-d6e1-4c92-adcb-63797cbd145e · outbound

This paper cites The Llama 3 Herd of Models.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B The Llama 3 Herd of Models

Reference 9

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local_arxiv, observed 2026-05-14T18:53:20.996879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:66c91662484d8103355e9758906de33e8faf0247777127181d6e7e366844f184

Observation 4c12010b-229c-4096-8f18-0970dd1926af · outbound

This paper cites CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution

Reference 10

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arxiv_id, observed 2026-05-14T20:57:16.443569Z

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source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:b4f792190546138fbc0090af2f6fc0504264f1046a730c1aaba66d71bb3aac21

Observation a17d8acc-e574-4337-906e-516bedc540ac · outbound

This paper cites OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems

Reference 11

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local_arxiv, observed 2026-05-14T18:53:21.009448Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:58d7cc9c30699651a744f7ac0bb16131b25f8e49d6dc38bf28304f311ce2be36

Observation 48c64367-5dc8-4ab6-b96e-088c9d52319d · outbound

This paper cites Measuring Massive Multitask Language Understanding.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B Measuring Massive Multitask Language Understanding

Reference 12

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local_arxiv, observed 2026-05-14T18:53:21.015068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 3521ffe5-b619-4a42-a399-24c9086f1a3b · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B Measuring Mathematical Problem Solving With the MATH Dataset

Reference 13

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local_arxiv, observed 2026-05-14T18:53:21.020228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation f35f6ec5-583d-4096-ba57-76b69a750a5f · outbound

This paper cites OCNLI: Original Chinese Natural Language Inference.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B OCNLI: Original Chinese Natural Language Inference

Reference 14

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arxiv_id, observed 2026-05-14T18:53:21.025987Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:8be021dfe534afc0885ac0071d8fcdc37a37dbc189e2c4a955e3627fee076805

Observation 7a403392-e4bc-4cf3-8357-9a123608a81f · outbound

This paper cites GPT-4o System Card.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B GPT-4o System Card

Reference 15

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local_arxiv, observed 2026-05-14T18:53:21.031495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation ac893edd-6e14-432e-8f58-dbda32ea5bb9 · outbound

This paper cites LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 16

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local_arxiv, observed 2026-05-14T18:53:21.038184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:ecd12c33afc5c7127cbf46de417e3de5ff8f8bb853d1889c21b86a8e91023edd

Observation ecb965f2-1d8e-4091-9071-01546399a143 · outbound

This paper cites TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

Reference 17

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local_arxiv, observed 2026-05-14T18:53:21.044229Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 52851571-e324-4a88-b941-eca3ada0dfa3 · outbound

This paper cites CMMLU: Measuring massive multitask language understanding in Chinese.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B CMMLU: Measuring massive multitask language understanding in Chinese

Reference 18

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arxiv_id, observed 2026-05-16T21:01:11.049549Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 340b9087-75a4-4876-8b2e-84f9eebaf37d · outbound

This paper cites LaViDa: A Large Diffusion Language Model for Multimodal Understanding.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B LaViDa: A Large Diffusion Language Model for Multimodal Understanding

Reference 19

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arxiv_id, observed 2026-05-14T18:53:21.058965Z

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Observation 0631f03a-1550-41e2-963b-baa941a9762f · outbound

This paper cites Every activa- tion boosted: Scaling general reasoner to 1 trillion open language foundation.arXiv preprint arXiv:2510.22115.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B Every activa- tion boosted: Scaling general reasoner to 1 trillion open language foundation.arXiv preprint arXiv:2510.22115

Reference 20

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arxiv_id, observed 2026-05-14T18:53:21.067232Z

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Observation cc694f49-8c3f-4e24-afa8-b7b27a817849 · outbound

This paper cites DeepSeek-V3 Technical Report.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B DeepSeek-V3 Technical Report

Reference 21

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local_arxiv, observed 2026-05-14T18:53:21.073800Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 54baad4c-d4b0-41e4-8fd2-f3be0378e8e2 · outbound

This paper cites Tidar: Think in diffusion, talk in autoregression.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B Tidar: Think in diffusion, talk in autoregression

Reference 22

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arxiv_id, observed 2026-05-14T18:53:21.082194Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:79e913296431809aa20b990c9aaf7aaa99efda22fa802aa2c291ee6099a42a4a

Observation ac20d748-c33d-41c6-9a5d-7819811d8066 · outbound

This paper cites VeOmni: Scaling Any Modality Model Training with Model-Centric Distributed Recipe Zoo.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B VeOmni: Scaling Any Modality Model Training with Model-Centric Distributed Recipe Zoo

Reference 23

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arxiv_id, observed 2026-05-14T18:53:21.089942Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 55af2bc7-272d-4e58-8e59-3e64ae8eb8fa · outbound

This paper cites Octopack: Instruction tuning code large language models.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B Octopack: Instruction tuning code large language models

Reference 24

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raw_fallback, observed 2026-05-14T18:53:21.204555Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:c144ca84e68168972a99d7ba44f1321eb4d89eae83078acbc24482d68bab9b4c

Observation 11ed8c08-a3a7-45c8-b016-92c849699431 · outbound

This paper cites Jinjie Ni, Qian Liu, Chao Du, Longxu Dou, Hang Yan, Zili Wang, Tianyu Pang, and Michael Qizhe Shieh.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B Jinjie Ni, Qian Liu, Chao Du, Longxu Dou, Hang Yan, Zili Wang, Tianyu Pang, and Michael Qizhe Shieh

Reference 25

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arxiv_id, observed 2026-05-14T18:53:21.109804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:61b6b40fb500c0d090cbafab58feff0b0b471007589d0f3a5559f15af2313719

Observation cbac3c2e-1c69-46f2-8286-5d56524584fb · outbound

This paper cites PHYBench: Holistic Evaluation of Physical Perception and Reasoning in Large Language Models.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B PHYBench: Holistic Evaluation of Physical Perception and Reasoning in Large Language Models

Reference 26

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arxiv_id, observed 2026-05-14T18:53:21.116805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:997dc1824f052ac0db22eca3f6a431753fa691eedf38bac67d0ec69cda1889c5

Observation 30f2828c-9bb1-4d34-ae48-604b59572902 · outbound

This paper cites Know What You Don't Know: Unanswerable Questions for SQuAD.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B Know What You Don't Know: Unanswerable Questions for SQuAD

Reference 27

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local_arxiv, observed 2026-05-14T18:53:21.123067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:0dfa86a1f957cdfd788e24bfb5c4ad8117cd9db4cd135dcc2b0b4adb9616a2e1

Observation 0fab5f04-a82f-40c2-9da8-cb6f9615a8ea · outbound

This paper cites HARDMath2: A Benchmark for Applied Mathematics Built by Students as Part of a Graduate Class.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B HARDMath2: A Benchmark for Applied Mathematics Built by Students as Part of a Graduate Class

Reference 28

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arxiv_id, observed 2026-05-14T18:53:21.129675Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:b2c6ad4c820b6005274c70a4170d55d7e882175340ac196e670b40c5d4a5110f

Observation 32036520-a8b9-4ecf-9ddc-805c3a57c877 · outbound

This paper cites Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought

Reference 29

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arxiv_id, observed 2026-05-14T18:53:21.136422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:6d932955f7ba0f7fffb868405890f124dcb32a7c1be4148c81861453d5f4a0fb

Observation f53ddb95-1051-4d94-b6ae-45afbc26323b · outbound

This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 30

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local_arxiv, observed 2026-05-14T18:53:21.141813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:408dc7a200b9bea32d3ad9daad4d85e5b24ce26822c4577942ccd54dc257fb8e

Observation 059dcfff-2c7c-4537-a201-129f51d4bf9a · outbound

This paper cites MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning

Reference 31

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arxiv_id, observed 2026-05-14T18:53:21.147548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:2906b723bcc4c5c5e6cab220d88ab72818cad3a1abbb18927dabbe265ccc5b27

Observation 4665d315-c344-4dec-b222-c1f94c848332 · outbound

This paper cites Challenging big-bench tasks and whether chain-of- thought can solve them.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B Challenging big-bench tasks and whether chain-of- thought can solve them

Reference 32

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raw_fallback, observed 2026-05-14T18:53:21.208803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:2b3838bc8631b0fcb0e70ce76b5c654cc810b1ba70da5f13211ff3d1dd96aa9e

Observation 28d0e392-e39a-4e9d-97eb-ce31a2ebb1e0 · outbound

This paper cites WSM: Decay-Free Learning Rate Schedule via Checkpoint Merging for LLM Pre-training.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B WSM: Decay-Free Learning Rate Schedule via Checkpoint Merging for LLM Pre-training

Reference 33

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arxiv_id, observed 2026-05-14T18:53:21.153791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:318d81ff9fba88197ed69a8d3140e7b16e83208b3b360db7d560ca5905ff6f00

Observation 93eaa01e-8b08-42b9-9859-d92bd0278fc1 · outbound

This paper cites SPG: Sandwiched Policy Gradient for Masked Diffusion Language Models.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B SPG: Sandwiched Policy Gradient for Masked Diffusion Language Models

Reference 34

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local_arxiv, observed 2026-05-14T18:53:21.160063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:d15fc901791ff0accaa315f4c92b4a955165f4e3ca81ac9662c9862f2971c455

Observation 80cb5910-dc98-4708-bbaf-9054fa671408 · outbound

This paper cites Diffusion LLMs Can Do Faster-Than-AR Inference via Discrete Diffusion Forcing.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B Diffusion LLMs Can Do Faster-Than-AR Inference via Discrete Diffusion Forcing

Reference 35

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arxiv_id, observed 2026-05-14T18:53:21.165818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:24a6b7da6bb0a279fac2f8ad6c8c6a33b7528466c31cc974832e6dee50093929

Observation 123145a3-4d7b-4b96-88bd-235af4a73839 · outbound

This paper cites CMATH: Can Your Language Model Pass Chinese Elementary School Math Test?.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B CMATH: Can Your Language Model Pass Chinese Elementary School Math Test?

Reference 36

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arxiv_id, observed 2026-05-14T18:53:21.171866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:aaa6ffabd9e3ebe23c5f63ad9bc8a58afec03b57237d424a090bd078bf7b8942

Observation 8efbabca-7789-41fe-ad2a-a786d877a9f7 · outbound

This paper cites Dream-Coder 7B: An Open Diffusion Language Model for Code.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B Dream-Coder 7B: An Open Diffusion Language Model for Code

Reference 37

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arxiv_id, observed 2026-05-14T18:53:21.177892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:32a76994c06f6fcc61738be7b08225cca50908ee1591fe3966cdfc9f6e126aba

Observation d029ee52-688d-47af-b8f5-6a61a009a474 · outbound

This paper cites Dream 7B: Diffusion Large Language Models.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B Dream 7B: Diffusion Large Language Models

Reference 38

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local_arxiv, observed 2026-05-14T18:53:21.183283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:be692becb5b2059b743443098607652ad5da3d77de4a5ec4864e5b0aae896f2c

Observation 98fc4c30-09b2-475e-9f49-216c4eaaa1b7 · outbound

This paper cites Discrete diffusion in large language and multimodal models: A survey.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B Discrete diffusion in large language and multimodal models: A survey

Reference 39

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arxiv_id, observed 2026-05-14T18:53:21.189267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:a9f625f51e83ddccc01b6dcdd7c1b5719da5b3f5a46f4d9d3c027267f6a497a9

Observation 648685a0-f329-4c85-b95c-ccb87d6dc540 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 40

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local_arxiv, observed 2026-05-14T18:53:21.194371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:047cdcf922b7b741637ed8d0fd38c20ddd859d6c5c984031818f4d20467e5241

Observation ea268b62-2206-4296-8766-bdf9dc750395 · outbound

This paper cites Evaluating the Performance of Large Language Models on GAOKAO Benchmark.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B Evaluating the Performance of Large Language Models on GAOKAO Benchmark

Reference 41

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arxiv_id, observed 2026-05-17T12:28:32.509177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:14ae089fcc4770ed114229946c4f1039ef2ed2effad6c5363fd89518c7804145

Observation 95fe5f4d-9b50-43ee-bf5a-7f36e1ad2933 · outbound

This paper cites Instruction-Following Evaluation for Large Language Models.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B Instruction-Following Evaluation for Large Language Models

Reference 42

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local_arxiv, observed 2026-05-14T18:53:21.096111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:e5c2b0ded1e8cd90be5fa1fffc3447ad37c538b0e9d47b51c5aca82a05a886a3

Observation cc9255bb-b89a-447f-84f4-c47a579f855e · outbound

This paper cites BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions

Reference 43

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arxiv_id, observed 2026-05-14T19:30:10.582609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T18:53:20.911374Z digest=sha256:a70fd5d33c11e73b0b0fe267c2e83061d9beb0193ea92658f068eee5ecdfdca6

Pith citing papers

Observation a0fc9c13-c7f1-4661-81ff-ae54e3d92d2e · inbound

Training-Trajectory-Aware Token Selection cites this paper.

Training-Trajectory-Aware Token Selection LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 2

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local_arxiv, observed 2026-05-22T11:41:29.581897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-22T11:41:21.275802Z digest=sha256:20a62709b29555696f061250b3db5302b3d4dda7228c7c80e7ad327ba2fac02b

Observation 1aa9a983-ebfc-45df-97e1-b1db0816efd7 · inbound

dgMARK: Decoding-Guided Watermarking for Diffusion Language Models cites this paper.

dgMARK: Decoding-Guided Watermarking for Diffusion Language Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 3

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no resolver link, observed 2026-08-03T06:22:03.580447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T06:22:03.580447Z digest=sha256:6334541455d16c7bc58e713a42d1ae733e5162da0d08adf01a75e58e97ca9368

Observation 76555a29-24b3-4b02-9b3a-4a097edaea0c · inbound

Step-Wise Refusal Dynamics in Autoregressive and Diffusion Language Models cites this paper.

Step-Wise Refusal Dynamics in Autoregressive and Diffusion Language Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 2024

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no resolver link, observed 2026-08-03T05:45:41.842845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T05:45:41.842845Z digest=sha256:138a694e8996a9a3cab883bb54b5206147d59174db9fc7785b15eed94b441f30

Observation 4a6d5c32-bbf4-4cbe-8305-b86c7fb612db · inbound

DSB: Dynamic Sliding Block Scheduling for Diffusion LLMs cites this paper.

DSB: Dynamic Sliding Block Scheduling for Diffusion LLMs LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 3

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no resolver link, observed 2026-08-03T04:07:42.697483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T04:07:42.697483Z digest=sha256:d849e2e4f4e1da4a24bddbec48e7a87b1f234108fc92ff3267213713942ebe57

Observation dc3f62e1-ded3-446a-9021-5aa2c1b08ad2 · inbound

TEAM: Temporal-Spatial Consistency Guided Expert Activation for MoE Diffusion Language Model Acceleration cites this paper.

TEAM: Temporal-Spatial Consistency Guided Expert Activation for MoE Diffusion Language Model Acceleration LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 4

Resolution
metadata mismatch
local_arxiv, observed 2026-05-25T06:50:28.201459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-25T06:49:09.078266Z digest=sha256:e009720665c198cae23b2ef2a38ff9ea8130b5f4d0404dace851ea723eeeadde

Observation fa6f7389-67c6-4ea2-a2be-57670a20d23d · inbound

TEAM: Temporal-Spatial Consistency Guided Expert Activation for MoE Diffusion Language Model Acceleration cites this paper.

TEAM: Temporal-Spatial Consistency Guided Expert Activation for MoE Diffusion Language Model Acceleration LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 4

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no resolver link, observed 2026-08-04T06:08:58.900178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T06:08:58.900178Z digest=sha256:3acb1bece9478272104bbd48f20b385893d9c78cb2dd2cbf8020d2b6107edfdc

Observation 920562e6-a34f-4846-ac6d-8f1285e32232 · 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 LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 5

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unresolved
no resolver link, observed 2026-08-02T23:50:33.240614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T23:50:33.240614Z digest=sha256:45558010c939867cda5417443118a3df74b1392155f6054e4ea104bd83c465fe

Observation 29fdb8eb-1951-4957-a7a6-5cfaba4849de · inbound

A Comparative analysis of Layer-wise Representational Capacity in AR and Diffusion LLMs cites this paper.

A Comparative analysis of Layer-wise Representational Capacity in AR and Diffusion LLMs LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 1

Resolution
verified exact
local_arxiv, observed 2026-05-15T15:06:10.115633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-15T15:03:23.792608Z digest=sha256:9e775685e3b8abf2b1d5b254f1ac931a2a4f6643b647c7a26859aca97afa0165

Observation f60d48eb-0214-417b-aeea-e7bb60fef2f6 · inbound

A Comparative analysis of Layer-wise Representational Capacity in AR and Diffusion LLMs cites this paper.

A Comparative analysis of Layer-wise Representational Capacity in AR and Diffusion LLMs LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 2021

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no resolver link, observed 2026-08-04T05:57:07.905348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T05:57:07.905348Z digest=sha256:3ad75af632b25a79ba260e2a595edc122d5f8e5bbde1d82b294f8a268431598c

Observation 1edaaafb-279c-44b9-8de8-a26b3c7040b6 · inbound

Locally Coherent Parallel Decoding in Diffusion Language Models cites this paper.

Locally Coherent Parallel Decoding in Diffusion Language Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 2

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verified exact
local_arxiv, observed 2026-05-21T12:05:04.914128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-21T12:04:40.899362Z digest=sha256:22c363cd76fb6b463873fc39a2e20825f0caf124c46490eaf50f14d5c9d520ae

Observation 45222165-0b6e-4bc4-882e-aeba8495d945 · inbound

Dependency-Guided Parallel Decoding in Discrete Diffusion Language Models cites this paper.

Dependency-Guided Parallel Decoding in Discrete Diffusion Language Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 3

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arxiv_id, observed 2026-05-14T18:53:21.210170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-13T20:37:29.906503Z digest=sha256:5992cb2d05268e74be8f421239956ec370f9a7b3f763f9943c420b0f04e4e324

Observation 29dde67c-399c-430a-97e0-21ed3c88492c · inbound

VASR: Variance-Aware Systematic Resampling for Reward-Guided Diffusion cites this paper.

VASR: Variance-Aware Systematic Resampling for Reward-Guided Diffusion LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 7

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verified exact
arxiv_id, observed 2026-05-14T18:53:21.210170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-10T18:08:03.771557Z digest=sha256:21f75ef034a163644c855f7bb4a5314114098acb7fe924cddaa1ee46c66779f6

Observation 40415775-1010-4220-88cb-5509a2685472 · inbound

VASR: Variance-Aware Systematic Resampling for Reward-Guided Diffusion cites this paper.

VASR: Variance-Aware Systematic Resampling for Reward-Guided Diffusion LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-14T18:53:21.210170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-13T01:08:01.321456Z digest=sha256:31362344ba6ccb3042d141b9cb4ebaade2df1d8f1aec1a611513482ebc64ea1c

Observation b3e9ea55-bd62-47b3-869d-2f2a1c50d817 · inbound

DMax: Aggressive Parallel Decoding for dLLMs cites this paper.

DMax: Aggressive Parallel Decoding for dLLMs LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-14T18:53:21.210170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-10T17:58:17.880199Z digest=sha256:59b45e26824f061f62453c496c260558c49e6c6e4cc4925fd2ceb10d14819c04

Observation 12594179-ddca-4ec0-967c-e0fa93041e18 · inbound

DMax: Aggressive Parallel Decoding for dLLMs cites this paper.

DMax: Aggressive Parallel Decoding for dLLMs LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 10

Resolution
verified exact
local_arxiv, observed 2026-05-19T16:47:40.076018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-19T16:46:56.743268Z digest=sha256:8f9d4efef0145099104b4245816f55bbcb4aac5daa00a036cb7171201a62844e

Observation dfd6a35d-d3ad-4856-ac38-d4321a01a29d · inbound

ECHO: Efficient Chest X-ray Report Generation with One-step Block Diffusion cites this paper.

ECHO: Efficient Chest X-ray Report Generation with One-step Block Diffusion LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-14T18:53:21.210170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-10T17:54:54.441128Z digest=sha256:c0e7a5a8d8978d99b0142a91cb5cda2d3e7eaaac5a9c2d474641e0b5b309aa84

Observation bf8a28ee-ca14-430b-a886-b9fec70f99b0 · inbound

ECHO: Efficient Chest X-ray Report Generation with One-step Block Diffusion cites this paper.

ECHO: Efficient Chest X-ray Report Generation with One-step Block Diffusion LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 4

Resolution
verified exact
local_arxiv, observed 2026-05-21T08:54:05.846658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-21T08:52:04.531938Z digest=sha256:f51aab36f5f038c188b689d9b39ab1ec46a6fa7aeb07cd58465f50d3467773d8

Observation 606f88ca-937f-4997-9323-511070360c99 · inbound

DepCap: Adaptive Block-Wise Parallel Decoding for Efficient Diffusion LM Inference cites this paper.

DepCap: Adaptive Block-Wise Parallel Decoding for Efficient Diffusion LM Inference LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-14T18:53:21.210170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-10T08:50:10.064801Z digest=sha256:2e9c7b94b790d11587ea1105bac2a5bf4edf1abc79f6c1f724f7d1ee49a6f4b2

Observation 4fd03cae-bf81-41f0-b7bb-d430896728b9 · inbound

Stability-Weighted Decoding for Diffusion Language Models cites this paper.

Stability-Weighted Decoding for Diffusion Language Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 3

Resolution
metadata mismatch
arxiv_id, observed 2026-05-14T18:53:21.210170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-10T06:12:37.804816Z digest=sha256:a352cc1d093aea57675d85cd582a7fafb2be07b8579b0d63083e276f0cbfeca3

Observation 99ab31a6-e3eb-410f-9bec-09427c30e152 · inbound

LLaDA2.0-Uni: Unifying Multimodal Understanding and Generation with Diffusion Large Language Model cites this paper.

LLaDA2.0-Uni: Unifying Multimodal Understanding and Generation with Diffusion Large Language Model LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-14T18:53:21.210170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-10T00:49:38.156237Z digest=sha256:bc1886994539270387af4db2782275402ce34c31009c88de5481ec42005942b8

Observation 96ad1d12-408d-4b84-8772-26e51e4a42d5 · inbound

Simple Self-Conditioning Adaptation for Masked Diffusion Models cites this paper.

Simple Self-Conditioning Adaptation for Masked Diffusion Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-14T18:53:21.210170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-07T16:38:22.306140Z digest=sha256:1056de2f580e41b966aeb46b1a06fe8bbbaa316ee65f9a02885cbe07e070d5f9

Observation 6adb80a2-e705-4bf8-a236-74a4592f01e1 · inbound

Focus on the Core: Empowering Diffusion Large Language Models by Self-Contrast cites this paper.

Focus on the Core: Empowering Diffusion Large Language Models by Self-Contrast LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-14T18:53:21.210170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-09T14:59:49.792976Z digest=sha256:0de142a798e20678c11da45e1f5e2441469aaf0c8f0140bddc3ac97d83cab2d1

Observation 48c73390-d4e3-4284-8820-81bd039aeb49 · inbound

ReflectDrive-2: Reinforcement-Learning-Aligned Self-Editing for Discrete Diffusion Driving cites this paper.

ReflectDrive-2: Reinforcement-Learning-Aligned Self-Editing for Discrete Diffusion Driving LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 90

Resolution
verified exact
arxiv_id, observed 2026-05-14T18:53:21.210170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-05-08T16:06:47.108382Z digest=sha256:6d3013be9faf9b176406b4bcc4f15d922bd9d0e9848643a74496b0d617fbc587

Observation cd4c4097-0382-4d95-8bfe-b24c7bae66a8 · inbound

ReflectDrive-2: Reinforcement-Learning-Aligned Self-Editing for Discrete Diffusion Driving cites this paper.

ReflectDrive-2: Reinforcement-Learning-Aligned Self-Editing for Discrete Diffusion Driving LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 90

Resolution
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arxiv_id, observed 2026-05-14T18:53:21.210170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-05-13T01:48:36.105389Z digest=sha256:964d5b00faeedbfe544079ad7bfc4358d2a24d1c80709f0018862a8d81dfe52e

Observation 07523fd8-9b6d-429f-9976-48cc9c1e0a3f · inbound

dFlowGRPO: Rate-Aware Policy Optimization for Discrete Flow Models cites this paper.

dFlowGRPO: Rate-Aware Policy Optimization for Discrete Flow Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 146

Resolution
verified exact
arxiv_id, observed 2026-05-14T18:53:21.210170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-05-12T03:52:05.779559Z digest=sha256:1584f11a9d1616a7759bec117cfa955011e4122b6f65e77e17d39c210ac6704e

Observation 8c8d8c97-0e1f-4f3d-a2ce-5824448440cb · inbound

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

BadDLM: Backdooring Diffusion Language Models with Diverse Targets LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-14T18:53:21.210170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-12T04:30:13.417357Z digest=sha256:1a71adb8ff1d60fa444532a68b52fbd8562468400a5432b74133ba4759e89969

Observation 59815ad2-e213-41c3-8222-c036b250280a · inbound

TAD: Temporal-Aware Trajectory Self-Distillation for Fast and Accurate Diffusion LLM cites this paper.

TAD: Temporal-Aware Trajectory Self-Distillation for Fast and Accurate Diffusion LLM LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-05-14T18:53:21.210170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-12T05:01:03.570848Z digest=sha256:163731205ef8910169de3387bf30305782671aab67b54b1ce7e09f6f0294663d

Observation 3252877a-a718-40d6-9f48-ecbb4aa70aa1 · inbound

TrajDLM: Topology-Aware Block Diffusion Language Model for Trajectory Generation cites this paper.

TrajDLM: Topology-Aware Block Diffusion Language Model for Trajectory Generation LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-14T18:53:21.210170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-12T02:57:11.330058Z digest=sha256:57274f94d0c4070edda023064c5087d0c0f58d79db1ca873a4a0be69e5f5e642

Observation 23c5f481-6b83-451a-a430-04561e561e37 · inbound

Relative Score Policy Optimization for Diffusion Language Models cites this paper.

Relative Score Policy Optimization for Diffusion Language Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 62

Resolution
verified exact
arxiv_id, observed 2026-05-14T18:53:21.210170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-05-12T03:47:42.196931Z digest=sha256:3ce0564bdb1c99c04df6a03e7ebad093ed2da70278eb657fd7d483fef11be460

Observation 269feddb-01d4-4815-875e-d4246141ffc6 · inbound

Infinite Mask Diffusion for Few-Step Distillation cites this paper.

Infinite Mask Diffusion for Few-Step Distillation LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-14T18:53:21.210170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-12T04:05:57.880725Z digest=sha256:6a0125220db95cbdd059dceaec2858ca47d13b965457195a1f2db181566f583f

Observation 0ffb6047-a756-48e8-a485-8f298d569648 · inbound

LEAP: Unlocking dLLM Parallelism via Lookahead Early-Convergence Token Detection cites this paper.

LEAP: Unlocking dLLM Parallelism via Lookahead Early-Convergence Token Detection LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-14T18:53:21.210170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-13T00:57:24.203834Z digest=sha256:e5107f1c58cbde9694e549a36c2f4e728df346617b8b06504b357e364b7e4499

Observation 3ee89c69-1a9c-439f-8036-26aa3f62e884 · inbound

BitLM: Unlocking Multi-Token Language Generation with Bitwise Continuous Diffusion cites this paper.

BitLM: Unlocking Multi-Token Language Generation with Bitwise Continuous Diffusion LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-14T18:53:21.210170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-13T01:54:23.273159Z digest=sha256:d56fd42a4f6daf9829cce50b70a0fcdb2615b1d9c0907d22ef7ae4233be16632

Observation 6d5f2368-e30b-4b53-a9aa-632ac323878e · inbound

Block-R1: Rethinking the Role of Block Size in Multi-domain Reinforcement Learning for Diffusion Large Language Models cites this paper.

Block-R1: Rethinking the Role of Block Size in Multi-domain Reinforcement Learning for Diffusion Large Language Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-14T18:53:21.210170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-13T07:03:00.503644Z digest=sha256:4800210e9008d5c95e7fc8efd2a5ebef3325092981451d209a1d30ec531029b5

Observation 790b9ce7-ab62-4013-ab80-da3222aeb784 · inbound

Block-R1: Rethinking the Role of Block Size in Multi-domain Reinforcement Learning for Diffusion Large Language Models cites this paper.

Block-R1: Rethinking the Role of Block Size in Multi-domain Reinforcement Learning for Diffusion Large Language Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 8

Resolution
verified exact
local_arxiv, observed 2026-05-14T21:19:28.293703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T21:06:01.667173Z digest=sha256:de8eaf2c93c2589aace38f1eb9004ac6e7e8e377ae9a88364ed97b88e5ca47e1

Observation 4d266446-d491-4d40-bdb5-42a3feade11c · inbound

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

Differences in Text Generated by Diffusion and Autoregressive Language Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 4

Resolution
verified exact
local_arxiv, observed 2026-05-14T20:59:27.045479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T20:59:06.804446Z digest=sha256:9313c1060cd8c278648325e14c951bb8971a29bd714b870a60b58f7fa692fd85

Observation 3a2c8f4e-89db-4c7e-8be9-d090b53f9f5b · inbound

Understanding and Accelerating the Training of Masked Diffusion Language Models cites this paper.

Understanding and Accelerating the Training of Masked Diffusion Language Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 3

Resolution
verified exact
local_arxiv, observed 2026-05-14T20:39:28.906087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T20:31:04.491385Z digest=sha256:a076b4c189255bb3ce66f2d88c630b642158e2c41c4b6d5b23e96dbe155779b3

Observation 870564a4-9d7b-406d-b921-2d53fd8f3d2a · inbound

Understanding and Accelerating the Training of Masked Diffusion Language Models cites this paper.

Understanding and Accelerating the Training of Masked Diffusion Language Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-02T14:15:45.887013Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T14:15:45.887013Z digest=sha256:98fc0b3c3c683e72f99d1cf46ee94c7079cad14136b45efcde66c65b92401fb6

Observation 1536b044-789b-41a7-b9a7-653d24e19b45 · inbound

BlockVLA: Accelerating Autoregressive VLA via Block Diffusion Finetuning cites this paper.

BlockVLA: Accelerating Autoregressive VLA via Block Diffusion Finetuning LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-14T18:53:21.210170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T17:51:48.905620Z digest=sha256:ebb01937d9e5ffa3b4ca06934546491fc7d05e1cfd09a85e1f3d92d412e4e239

Observation 1ad87f19-eaa1-4e10-b84b-e140848f110f · inbound

Factorization-Error-Free Discrete Diffusion Language Model via Speculative Decoding cites this paper.

Factorization-Error-Free Discrete Diffusion Language Model via Speculative Decoding LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 2

Resolution
verified exact
local_arxiv, observed 2026-05-15T02:53:33.539967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-15T02:51:41.856616Z digest=sha256:802144189dee8ca71ddb64805c9bdd30b1eb71b8943bf50724ad9430afd1455c

Observation 4680be7c-4435-472f-b07e-17454b447905 · inbound

From Table to Cell: Attention for Better Reasoning with TABALIGN cites this paper.

From Table to Cell: Attention for Better Reasoning with TABALIGN LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 7

Resolution
verified exact
local_arxiv, observed 2026-05-15T01:33:27.331933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-15T01:31:03.738527Z digest=sha256:d0e110466ca841bf9b4d2d0ed22b1f44d5a25796e577f347861944aa999154ed

Observation c2ea7126-2ea2-4d7d-b2e7-2a8552d2c8d5 · inbound

Elastic-dLLM: Position Preserving Context Compression and Augmentation of Diffusion LLMs cites this paper.

Elastic-dLLM: Position Preserving Context Compression and Augmentation of Diffusion LLMs LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 3

Resolution
verified exact
local_arxiv, observed 2026-05-20T13:13:17.942220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-20T13:13:05.695349Z digest=sha256:73b484cfbb62fe545f5084f7d36641f9e8047a64bd6c81906aca73e38b5180db

Observation 00bcf1f7-fbe1-44f3-adc6-934c17a35432 · inbound

TIDE: Efficient and Lossless MoE Diffusion LLM Inference with I/O-aware Expert Offload cites this paper.

TIDE: Efficient and Lossless MoE Diffusion LLM Inference with I/O-aware Expert Offload LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 7

Resolution
metadata mismatch
local_arxiv, observed 2026-05-20T05:13:03.722203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-20T05:08:07.318040Z digest=sha256:230bb7d4b537489e2446389ee2e99cc85848102a5b2c02fdb55f8b6a64bb5b16

Observation 251757ed-dd99-48c4-bac9-8a2ea42ca470 · inbound

PulseCol: Periodically Refreshed Column-Sparse Attention for Accelerating Diffusion Language Models cites this paper.

PulseCol: Periodically Refreshed Column-Sparse Attention for Accelerating Diffusion Language Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 3

Resolution
verified exact
local_arxiv, observed 2026-05-21T05:09:38.738780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-21T05:05:55.767705Z digest=sha256:e647b89972fcaf569516e9b52f3ec478885e8a31e4cac6b277eda89c75cbb897

Observation 51027602-d7de-4501-a050-936d93d87aa6 · inbound

Extracting Training Data from Diffusion Language Models via Infilling cites this paper.

Extracting Training Data from Diffusion Language Models via Infilling LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 4

Resolution
verified exact
local_arxiv, observed 2026-06-30T16:04:53.273530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-30T15:56:55.547579Z digest=sha256:3945bf0f18d771dbae950562fff5defd46f134da828855f8de0df399be401cf2

Observation d908f75d-5c66-4b7b-9982-d3479c347e4a · inbound

TUBE: Tangent Upper Bound on Evidence for Discrete Diffusion Language Models cites this paper.

TUBE: Tangent Upper Bound on Evidence for Discrete Diffusion Language Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 4

Resolution
verified exact
local_arxiv, observed 2026-06-30T15:34:48.432341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-30T15:24:48.413685Z digest=sha256:be7dec152acc77bdf799d83bbe87c55e8124aaf908eb073a46162e59c14bdd3f

Observation da0b99bf-7616-418a-b951-dd3f6cd15c06 · inbound

Optimus: Elastic Decoding for Efficient Diffusion LLM Serving cites this paper.

Optimus: Elastic Decoding for Efficient Diffusion LLM Serving LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 8

Resolution
verified exact
local_arxiv, observed 2026-06-30T00:24:04.313139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-30T00:15:58.082725Z digest=sha256:9f8da361812ec0bea9c9311750d737632258137d618bed34efc951888defede3

Observation b3b7718d-8fd0-446f-9b08-73e71381b57f · inbound

$D^2$-Monitor: Dynamic Safety Monitoring for Diffusion LLMs via Hesitation-Aware Routing cites this paper.

$D^2$-Monitor: Dynamic Safety Monitoring for Diffusion LLMs via Hesitation-Aware Routing LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 11

Resolution
verified exact
local_arxiv, observed 2026-06-29T22:03:59.826196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-29T22:03:25.762772Z digest=sha256:fe6ab6af43fa7ea96ad35ddb1ef46074e508c4fef88e3e279da09c9495bec580

Observation 94bc8347-8c32-4ffa-bfa8-0b1410c3844d · inbound

Looped Diffusion Language Models cites this paper.

Looped Diffusion Language Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 4

Resolution
verified exact
local_arxiv, observed 2026-06-29T23:14:01.140257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-29T23:13:12.343355Z digest=sha256:0ec10057c626a4cef3eb5c37831da8678a8a24046bf5aa81a41ff0b11984dcc3

Observation 686944ff-643e-409d-8b7c-cb9a391e0028 · inbound

GDSD: Reinforcement Learning as Guided Denoiser Self-Distillation for Diffusion Language Models cites this paper.

GDSD: Reinforcement Learning as Guided Denoiser Self-Distillation for Diffusion Language Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 4

Resolution
verified exact
local_arxiv, observed 2026-06-29T08:53:16.327756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-29T08:44:53.969301Z digest=sha256:b502789da663f28d86ad9ce60b984d92940dfaa2ce4033a4219c6fbd88f7e731

Observation b0b19b3c-6c06-4cd6-8eb0-5236d28d8b7c · inbound

dMoE: dLLMs with Learnable Block Experts cites this paper.

dMoE: dLLMs with Learnable Block Experts LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 11

Resolution
verified exact
local_arxiv, observed 2026-06-28T22:52:45.017324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-28T22:50:51.900169Z digest=sha256:5bca6a7a77cd15b9a2830927ef429f0abb0636c0aa426db7278f4e0165fd117b

Observation df96bbc9-79f7-4f88-a43e-ede695996318 · inbound

Decoding in Order-Agnostic Language Models: Chain-Rule Deviation and Uniform Spreading cites this paper.

Decoding in Order-Agnostic Language Models: Chain-Rule Deviation and Uniform Spreading LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 4

Resolution
verified exact
local_arxiv, observed 2026-07-01T20:46:13.563458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-28T17:47:16.712668Z digest=sha256:d9e251b954e47e09d0cc294b5aa27d677bf6752cf1598981ff3ddb36fac38501

Observation 1359391e-50f2-49ba-b468-b2a03efd25fc · inbound

DSL-LLaDA: Scaling Continuous Denoising to 8B Masked Diffusion LMs cites this paper.

DSL-LLaDA: Scaling Continuous Denoising to 8B Masked Diffusion LMs LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 4

Resolution
metadata mismatch
local_arxiv, observed 2026-07-01T20:46:14.274937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-28T17:39:11.170970Z digest=sha256:0882666d7ec94f2ab9ec2c2f844652160e15e5b813e1beecdfadd662d8f0af36

Observation d056a4d7-1895-46d3-8939-952cfa5db869 · inbound

SimSD: Simple Speculative Decoding in Diffusion Language Models cites this paper.

SimSD: Simple Speculative Decoding in Diffusion Language Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 6

Resolution
verified exact
local_arxiv, observed 2026-07-01T22:56:19.514977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-28T14:58:00.723105Z digest=sha256:cdaefc77bc9eceda1368795bf4b349b11f0e9f6a34ce49c7432c1a347a1e8de9

Observation 2f215eea-3f38-4a31-ac9b-68f429b3ea2c · inbound

MaskForge: Structure-Aware Adaptive Attacks for Jailbreaking Diffusion Large Language Models cites this paper.

MaskForge: Structure-Aware Adaptive Attacks for Jailbreaking Diffusion Large Language Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 6

Resolution
verified exact
local_arxiv, observed 2026-07-01T23:56:24.395256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-28T13:43:51.171443Z digest=sha256:20029f7d07d566d0ac8e5c181e9be712947f8e121bd74cf3e0e9af3bcdce5df4

Observation 77978734-4a0c-468c-b6b5-3ad0dbbbedb4 · inbound

Data-Efficient Autoregressive-to-Diffusion Language Models via On-Policy Distillation cites this paper.

Data-Efficient Autoregressive-to-Diffusion Language Models via On-Policy Distillation LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 6

Resolution
verified exact
local_arxiv, observed 2026-07-02T13:26:58.854711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-28T01:21:52.821813Z digest=sha256:cb8588b3a28346890cdf29b25ee1a63414d576c55b9642ea224c179a8836932c

Observation 160c9771-1d3a-40d2-90ec-6e0d90abf4eb · inbound

Data-Constrained Language Model Pretraining: Improved Regularization and Scaling Laws cites this paper.

Data-Constrained Language Model Pretraining: Improved Regularization and Scaling Laws LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 45

Resolution
metadata mismatch
local_arxiv, observed 2026-07-02T16:47:09.801223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-27T22:24:01.067388Z digest=sha256:426eed6f2b153d39b1c580e8f3c26e91fb6da790edfc777987d3b08129caf14d

Observation 86092ac0-8e22-45f0-8edc-9d2cbbc431f1 · inbound

Unified Energy for Invariant and Independent Decoding in Diffusion Language Models cites this paper.

Unified Energy for Invariant and Independent Decoding in Diffusion Language Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 4

Resolution
verified exact
local_arxiv, observed 2026-07-03T01:07:29.842970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-27T16:51:04.460958Z digest=sha256:6857c353d082b3504c12f7e5bcca7543195d380b3f559d8069f4ddc55aebaca9

Observation 611599ff-5fd1-4009-88c5-e9b05e923242 · 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 LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 40

Resolution
verified exact
local_arxiv, observed 2026-07-03T21:18:59.376265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-27T00:42:43.220998Z digest=sha256:344f30e5ef1493ccae685da3bf6b736fc2188640728d7381c8497717ecd946b0

Observation 468159fb-c3b4-47d7-9661-5694f1944f8f · inbound

Learning from the Self-future: On-policy Self-distillation for dLLMs cites this paper.

Learning from the Self-future: On-policy Self-distillation for dLLMs LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 12

Resolution
verified exact
local_arxiv, observed 2026-07-03T20:28:55.659824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-27T01:20:14.919054Z digest=sha256:b0e3025f4f0370a2699741cc904ba8aba3ab3e4a4a15657ace2eb20b362c70fb

Observation dc8738d5-5022-437b-ac97-d5cc843b92b1 · inbound

PerceptionDLM: Parallel Region Perception with Multimodal Diffusion Language Models cites this paper.

PerceptionDLM: Parallel Region Perception with Multimodal Diffusion Language Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 4

Resolution
verified exact
local_arxiv, observed 2026-07-04T00:49:18.039142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-26T20:59:26.886235Z digest=sha256:0d9159e54097704401d1ae851f7eb859d5ab70e6bf513ae354e2c72b87ed9ac9

Observation 74c101ad-53ac-4eaf-bb4a-a677af5f394f · 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 LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 4

Resolution
verified exact
local_arxiv, observed 2026-07-04T06:09:37.904123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-26T14:44:11.281545Z digest=sha256:70b9059c53797111f31acb3b5699b4873af4b1812ece281cf9c87b4edbc60e39

Observation 43d93d9a-f6f8-45b3-aecb-5781a6ad902a · 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 LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-04T04:42:23.978529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T04:42:23.978529Z digest=sha256:ae86ee2ad2b118cec8e19ae749ae8f5c3e9ae76a5182de69f73ff32fe0ceba26

Observation d9420ae1-68ee-4834-ba89-3650b4a473a3 · inbound

When Top-1 Fails: Calibrating LoRA Monitors for Masked Diffusion LMs cites this paper.

When Top-1 Fails: Calibrating LoRA Monitors for Masked Diffusion LMs LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 40

Resolution
verified exact
local_arxiv, observed 2026-07-04T16:19:56.317788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-26T00:49:34.365193Z digest=sha256:a78cfa85cd26c2cd24fb7c6977861a09789879542235c1256bdd84272033a8ad

Observation c04aa8b5-6863-40c5-a46c-2c51b335813a · inbound

Masked Diffusion Decoding as $x$-Prediction Flow cites this paper.

Masked Diffusion Decoding as $x$-Prediction Flow LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 5

Resolution
verified exact
local_arxiv, observed 2026-06-30T09:34:34.799143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-30T09:27:28.863343Z digest=sha256:c47f393e7faeb564d1f66ce2fb87e31fc10aa56217779887d9c03dfc98a68e64

Observation 21208153-95e8-4b7e-9e43-3ec068dd15f5 · inbound

Multi-Block Diffusion Language Models cites this paper.

Multi-Block Diffusion Language Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 27

Resolution
verified exact
local_arxiv, observed 2026-06-30T08:54:29.548537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-30T08:53:31.494892Z digest=sha256:edf1ce166c4a8bfa7ddabb4d2b0f48c29d1baf287d63d32b3d7b4820eed43200

Observation c0fe317b-7427-49ad-b747-21740ca223c6 · inbound

Multi-Block Diffusion Language Models cites this paper.

Multi-Block Diffusion Language Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 27

Resolution
verified exact
local_arxiv, observed 2026-07-01T07:05:28.290602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-07-01T07:04:06.161855Z digest=sha256:4bf36bcfc2e322ae03e6106c604d4e0ca13dfb553419a8e286f55783503697c1

Observation 5acb4afe-f819-4349-8638-3107355a20b3 · inbound

Understanding Evaluation Illusion in Diffusion Large Language Models cites this paper.

Understanding Evaluation Illusion in Diffusion Large Language Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 4

Resolution
verified exact
local_arxiv, observed 2026-06-30T07:54:21.830492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-30T07:52:46.014306Z digest=sha256:515397bf46acddd95117944697ec6f7fb353662ba013918f016ba3b4791f37a3

Observation fdc6e66b-4660-48d0-b670-00addbd29a28 · inbound

Understanding Evaluation Illusion in Diffusion Large Language Models cites this paper.

Understanding Evaluation Illusion in Diffusion Large Language Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 4

Resolution
verified exact
local_arxiv, observed 2026-07-02T20:57:22.858880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-07-02T20:52:08.330407Z digest=sha256:7ebfa955b12a9c076c49093e4d13c50fe2bba390289c69a0a59acb206b39d7fa

Observation 45644394-85eb-4425-b213-f38ed0f7cd14 · inbound

DPPE: Rethinking Camera-Based Positional Encoding for Scaling Multi-View Transformers cites this paper.

DPPE: Rethinking Camera-Based Positional Encoding for Scaling Multi-View Transformers LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 44

Resolution
verified exact
local_arxiv, observed 2026-07-01T10:15:44.858875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-07-01T05:39:21.642584Z digest=sha256:5ee7a58418c44e22b7180882e533192dde8cd2f251e21a2ca8c26da8929cf32c

Observation 67dfd7bf-37f5-4a16-869c-f5c679559e45 · inbound

SLIM-RL: Risk-Budgeted Random-Masking RL for Diffusion LLMs Without Trajectory Slicing cites this paper.

SLIM-RL: Risk-Budgeted Random-Masking RL for Diffusion LLMs Without Trajectory Slicing LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 3

Resolution
metadata mismatch
local_arxiv, observed 2026-07-02T19:07:17.201188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-07-02T19:05:59.651008Z digest=sha256:87dc0b6ed1913b8fb1bd3aa1598bf528fe50484d1001e35b85f70002e5b531a5

Observation 03ec835e-a87c-491c-bb23-241cb4434303 · inbound

TACG: Trajectory-Aware Commit Gating for Diffusion Language Model Decoding cites this paper.

TACG: Trajectory-Aware Commit Gating for Diffusion Language Model Decoding LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 3

Resolution
unresolved
no resolver link, observed 2026-07-12T03:56:26.770729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T03:56:26.770729Z digest=sha256:463f4f06b53cb66cbaef47b1d193e108ee327a3189ea5f20773b78815f102d57

Observation 045768ee-11a9-4d41-9674-5a84016fdc3c · inbound

Sangam: Efficiently Serving Diffusion LLMs with the AR Stack cites this paper.

Sangam: Efficiently Serving Diffusion LLMs with the AR Stack LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 8

Resolution
unresolved
no resolver link, observed 2026-07-11T20:58:04.182764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T20:58:04.182764Z digest=sha256:f571fab82f89a5e67d46e074a35d957cb8130c3b7883f018a39861578963afbe

Observation c4413e59-3e50-4ec0-9c90-dba1983060ed · inbound

Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding cites this paper.

Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 50

Resolution
verified exact
local_arxiv, observed 2026-07-11T03:07:51.938767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-07-11T03:04:12.500342Z digest=sha256:dc7a9ff178b2cb8381c7571e441eb6d7257c1d25254e87e852e147feaf7d0be2

Observation 183f6b36-1026-470c-b252-68db9bde9ea4 · inbound

CODA: Algorithm-Hardware Co-design for Edge Video Diffusion via NMP-Enabled Compute-Cache Operator Disaggregation cites this paper.

CODA: Algorithm-Hardware Co-design for Edge Video Diffusion via NMP-Enabled Compute-Cache Operator Disaggregation LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-02T00:49:32.353686Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T00:49:32.353686Z digest=sha256:e18262105899a18856fd602a9c2aec04bb2a5a2b485714a98ffa8ce7bb3c5338

Observation e1cffa7c-eff4-4f2a-b8d3-e49e97bfbb94 · inbound

Adaptive Multi-Step Lookahead Decoding for Diffusion Language Models cites this paper.

Adaptive Multi-Step Lookahead Decoding for Diffusion Language Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-01T22:45:09.896002Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T22:45:09.896002Z digest=sha256:e80412dace7b0074e994ff4686e86a4b616a19e44cfc8cab08c7c4366eee9a2e

Observation 3d6a276a-4d55-4ba8-9deb-9c2c9aa8173e · inbound

Masked Diffusion Language Models are Strong and Steerable Text-Based World Models for Agentic RL cites this paper.

Masked Diffusion Language Models are Strong and Steerable Text-Based World Models for Agentic RL LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-02T14:50:09.425202Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T14:50:09.425202Z digest=sha256:6fae4920582edffb41fb93115be8f02332ce68ecb2d45d68d205aa3d9c860d22

Observation 1066b677-c40f-44a3-9ec6-b05b1980126f · inbound

FlowBlock: Wavefront-Parallel Decoding for Self-Correcting Diffusion Language Models cites this paper.

FlowBlock: Wavefront-Parallel Decoding for Self-Correcting Diffusion Language Models LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-01T17:28:54.899015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T17:28:54.899015Z digest=sha256:2403d3f330f3453fa6fdcf2faf84ef46d08274cbc4d0935f7ed5b507ec0f4266

Observation e67b9939-1eb0-499f-9932-21dd604ad827 · inbound

AdaFlash: Adaptive Speculative Decoding via On-Policy Distilled Diffusion Drafters cites this paper.

AdaFlash: Adaptive Speculative Decoding via On-Policy Distilled Diffusion Drafters LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-01T13:10:37.449415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T13:10:37.449415Z digest=sha256:2697b2c8d30e387e75fa1c36043647820521ddf50e01b120ce953976cfd585c9

Observation 12042f15-0c8d-4fea-8550-dee32b7df065 · inbound

Context-weighted Discrete Flow Matching cites this paper.

Context-weighted Discrete Flow Matching LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-01T07:34:06.672977Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T07:34:06.672977Z digest=sha256:14d6658e8d0c2bbab831135b708cf4cb8bc93224bc8e13db24201dbd71623b82

Observation 62c6eebe-f29d-4af4-930e-3cb07c7e3ba1 · 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 LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-01T14:25:38.284457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T14:25:38.284457Z digest=sha256:682d731b9d52ab95690f6d2d4113bfa45b4dd0f368da3e6db6a4660ee5e6d6f7

Observation b998e15c-99e5-43e2-a9eb-f0f848c88a2c · inbound

Escaping Confidence Trap: Evolutionary Decoding for Mathematical Reasoning in Diffusion LLMs cites this paper.

Escaping Confidence Trap: Evolutionary Decoding for Mathematical Reasoning in Diffusion LLMs LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-05T00:36:12.980619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T00:36:12.980619Z digest=sha256:451e74968e94e192e6013615effca9f5af8541e860e3eceaf60cb17e33880d8b