Gumbel Distillation for Parallel Text Generation | Paper Citation Record

Observation 1079c312-bdf4-4456-9043-50cd3352bcfa · outbound

This paper cites Block diffusion: Interpolating between autoregressive and diffusion language models.

Gumbel Distillation for Parallel Text Generation Block diffusion: Interpolating between autoregressive and diffusion language models

Reference 1

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Observation 663588a9-1035-45f5-a173-7ec16d6f89c4 · outbound

This paper cites Structured denoising diffusion models in discrete state-spaces.

Gumbel Distillation for Parallel Text Generation Structured denoising diffusion models in discrete state-spaces

Reference 2

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Observation 36f8bdf0-eb0f-4bb4-9690-d1c807627996 · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language.

Gumbel Distillation for Parallel Text Generation Piqa: Reasoning about physical commonsense in natural language

Reference 3

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Observation b52ee5e8-afb1-4633-8127-13ceb21eede6 · outbound

This paper cites Language models are few-shot learners.

Gumbel Distillation for Parallel Text Generation Language models are few-shot learners

Reference 4

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Observation 51b3f055-f05c-4cb3-a1ac-486465ffb3ff · outbound

This paper cites Medusa: Simple llm inference acceleration framework with multiple decoding heads.

Gumbel Distillation for Parallel Text Generation Medusa: Simple llm inference acceleration framework with multiple decoding heads

Reference 5

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Observation 4c5fad2b-6e6b-44f3-8534-82361baf36c1 · outbound

This paper cites A continuous time framework for discrete denoising models.

Gumbel Distillation for Parallel Text Generation A continuous time framework for discrete denoising models

Reference 6

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Observation 5736ee0f-5080-4219-b059-592d56eb68dd · outbound

This paper cites One billion word benchmark for measuring progress in statistical language modeling.

Gumbel Distillation for Parallel Text Generation One billion word benchmark for measuring progress in statistical language modeling

Reference 7

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Observation 12f3ef99-bd61-4331-8c7e-2184d30ad033 · outbound

This paper cites Diffusion forcing: Next-token prediction meets full-sequence diffusion.

Gumbel Distillation for Parallel Text Generation Diffusion forcing: Next-token prediction meets full-sequence diffusion

Reference 8

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Observation b63324a3-b22e-4212-a64c-0e216d1b4109 · outbound

This paper cites Accelerating Large Language Model Decoding with Speculative Sampling.

Gumbel Distillation for Parallel Text Generation Accelerating Large Language Model Decoding with Speculative Sampling

Reference 9

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Observation 8447635e-cde6-4c44-baf8-70e5f7a3f626 · outbound

This paper cites Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning.

Gumbel Distillation for Parallel Text Generation Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 10

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Observation 9023f54a-dfc5-4183-92c5-a2251b5d8bca · outbound

This paper cites Boolq: Exploring the surprising difficulty of natural yes/no questions.

Gumbel Distillation for Parallel Text Generation Boolq: Exploring the surprising difficulty of natural yes/no questions

Reference 11

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Observation 8ecbe241-4abc-482d-80f7-b73a41ddd876 · outbound

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

Gumbel Distillation for Parallel Text Generation Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 12

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Observation 98c35d9f-9858-45dd-bb64-e1e9fa0843f3 · outbound

This paper cites A discourse-aware attention model for abstractive summarization of long documents.

Gumbel Distillation for Parallel Text Generation A discourse-aware attention model for abstractive summarization of long documents

Reference 13

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Observation 80246763-8606-413e-8ea6-992ba73fe45c · outbound

This paper cites Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities.

Gumbel Distillation for Parallel Text Generation Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Reference 14

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Observation fa124b1d-d018-48b3-8c15-a23ece9a426f · outbound

This paper cites Gemini diffusion.

Gumbel Distillation for Parallel Text Generation Gemini diffusion

Reference 15

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Observation b19f4727-8cf8-42b3-abc4-4753d9908c7d · outbound

This paper cites Continuous diffusion for categorical data.

Gumbel Distillation for Parallel Text Generation Continuous diffusion for categorical data

Reference 16

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Observation cb4854b3-9d63-4c94-825c-1f25b6ce198d · outbound

This paper cites Unifying autoregressive and diffusion-based sequence generation.

Gumbel Distillation for Parallel Text Generation Unifying autoregressive and diffusion-based sequence generation

Reference 17

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Observation c3af8259-44dd-4843-b0b0-d596040b755d · outbound

This paper cites The language model evaluation harness, 07 2024.

Gumbel Distillation for Parallel Text Generation The language model evaluation harness, 07 2024

Reference 18

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Observation a61d24a0-6eda-42a4-b43c-ea433e7a3df8 · outbound

This paper cites Discrete flow matching.

Gumbel Distillation for Parallel Text Generation Discrete flow matching

Reference 19

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Observation effb11fb-8044-4924-a972-d2464dff165d · outbound

This paper cites Mask-predict: Parallel decoding of conditional masked language models.

Gumbel Distillation for Parallel Text Generation Mask-predict: Parallel decoding of conditional masked language models

Reference 20

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Observation c52e6d14-27e4-4cc7-a794-8af2968825f9 · outbound

This paper cites Better & faster large language models via multi-token prediction.

Gumbel Distillation for Parallel Text Generation Better & faster large language models via multi-token prediction

Reference 21

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Observation 1a9487dd-3783-4aab-9f33-836709e17331 · outbound

This paper cites Openwebtext corpus.

Gumbel Distillation for Parallel Text Generation Openwebtext corpus

Reference 22

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Observation 8cd22687-ec41-4436-b46d-730f2cd15303 · outbound

This paper cites Diffuseq: Sequence to sequence text generation with diffusion models.

Gumbel Distillation for Parallel Text Generation Diffuseq: Sequence to sequence text generation with diffusion models

Reference 23

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Observation bb421df1-1fe4-4e8f-909c-a9340d9ad700 · outbound

This paper cites Scaling diffusion language models via adaptation from autoregressive models.

Gumbel Distillation for Parallel Text Generation Scaling diffusion language models via adaptation from autoregressive models

Reference 24

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Observation c3e223f8-0189-415f-8d63-b7ca2c3432dc · outbound

This paper cites Non-Autoregressive Neural Machine Translation.

Gumbel Distillation for Parallel Text Generation Non-Autoregressive Neural Machine Translation

Reference 25

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Observation 042e5328-b762-49e0-9f9d-c1e69ea3d53b · outbound

This paper cites Levenshtein transformer.

Gumbel Distillation for Parallel Text Generation Levenshtein transformer

Reference 26

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Observation e35478e7-ba6e-43d3-91b2-4c4a24f2a60e · outbound

This paper cites MiniLLM: On-Policy Distillation of Large Language Models.

Gumbel Distillation for Parallel Text Generation MiniLLM: On-Policy Distillation of Large Language Models

Reference 27

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Observation a2218cab-296f-4157-8603-fe380fad81a5 · outbound

This paper cites an unresolved cited work.

Gumbel Distillation for Parallel Text Generation Unresolved cited work

Reference 28

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Observation 01839513-d99a-4fda-9e4d-0ac7def12727 · outbound

This paper cites SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular Control.

Gumbel Distillation for Parallel Text Generation SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular Control

Reference 29

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Observation 28be9710-b24b-40ba-b746-05ff0c7fcba1 · outbound

This paper cites DiffusionBERT: Improving Generative Masked Language Models with Diffusion Models.

Gumbel Distillation for Parallel Text Generation DiffusionBERT: Improving Generative Masked Language Models with Diffusion Models

Reference 30

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Observation a41001a0-e8f5-4535-9e46-77394c432a94 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Gumbel Distillation for Parallel Text Generation Distilling the Knowledge in a Neural Network

Reference 31

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Observation d0abd055-e645-40ac-b88b-c58fde3a751c · outbound

This paper cites Denoising diffusion probabilistic models.

Gumbel Distillation for Parallel Text Generation Denoising diffusion probabilistic models

Reference 32

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Observation 20c8d6a9-c758-46c3-bb3b-ff83f57477ca · outbound

This paper cites The curious case of neural text degeneration.

Gumbel Distillation for Parallel Text Generation The curious case of neural text degeneration

Reference 33

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Observation 9c9c630b-daba-4452-83ba-8eda3b14276f · outbound

This paper cites Argmax flows and multinomial diffusion: Learning categorical distributions.

Gumbel Distillation for Parallel Text Generation Argmax flows and multinomial diffusion: Learning categorical distributions

Reference 34

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Observation 783fe19d-6e41-4de3-bbfc-9034e94d3382 · outbound

This paper cites Accelerating diffusion llms via adaptive parallel decoding.

Gumbel Distillation for Parallel Text Generation Accelerating diffusion llms via adaptive parallel decoding

Reference 35

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Observation b801e866-c62f-4236-bf29-669ce95f95b2 · outbound

This paper cites Any-Order Flexible Length Masked Diffusion.

Gumbel Distillation for Parallel Text Generation Any-Order Flexible Length Masked Diffusion

Reference 36

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Observation 1e03e69e-7cb5-4eb4-9f13-47c53061a5d8 · outbound

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Gumbel Distillation for Parallel Text Generation Unresolved cited work

Reference 37

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Observation ee7a1c28-bcee-4160-aa57-1f27c0ddf712 · outbound

This paper cites Stochastic beams and where to find them: The gumbel-top-k trick for sampling sequences without replacement.

Gumbel Distillation for Parallel Text Generation Stochastic beams and where to find them: The gumbel-top-k trick for sampling sequences without replacement

Reference 38

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Observation 973fe76f-8e3c-4784-89c4-f3597b2d4ca1 · outbound

This paper cites Cllms: Consistency large language models.

Gumbel Distillation for Parallel Text Generation Cllms: Consistency large language models

Reference 39

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Observation aecfe734-1003-4215-a417-87f4c35d0f0e · outbound

This paper cites Mercury: Ultra-Fast Language Models Based on Diffusion.

Gumbel Distillation for Parallel Text Generation Mercury: Ultra-Fast Language Models Based on Diffusion

Reference 40

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Observation ca4957fa-464d-4b75-a6f2-b2eb455702c5 · outbound

This paper cites Fast inference from transformers via speculative decoding.

Gumbel Distillation for Parallel Text Generation Fast inference from transformers via speculative decoding

Reference 41

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Observation ccd90081-8e8b-4377-b178-a850f05b399a · outbound

This paper cites Diffusion-lm improves controllable text generation.

Gumbel Distillation for Parallel Text Generation Diffusion-lm improves controllable text generation

Reference 42

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Observation 2c983ae5-a3d6-4fdc-a3db-0b09751dd990 · outbound

This paper cites Flow Matching for Generative Modeling.

Gumbel Distillation for Parallel Text Generation Flow Matching for Generative Modeling

Reference 43

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Observation a785ac37-2e5a-46b1-832f-ab93954fa40e · outbound

This paper cites DeepSeek-V3 Technical Report.

Gumbel Distillation for Parallel Text Generation DeepSeek-V3 Technical Report

Reference 44

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Observation 4da25fce-245c-4068-8af3-90535d941606 · outbound

This paper cites Discrete Copula Diffusion.

Gumbel Distillation for Parallel Text Generation Discrete Copula Diffusion

Reference 45

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Observation 447d1555-825b-4910-a909-f7e4ceac8254 · outbound

This paper cites Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals.

Gumbel Distillation for Parallel Text Generation Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals

Reference 46

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Observation 1fe23702-0d5e-4d4f-ad7d-5e618691910b · outbound

This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.

Gumbel Distillation for Parallel Text Generation Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 47

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Observation 7cd59e65-2b37-483c-b132-52a3f0555eff · outbound

This paper cites Discrete diffusion modeling by estimating the ratios of the data distribution.

Gumbel Distillation for Parallel Text Generation Discrete diffusion modeling by estimating the ratios of the data distribution

Reference 48

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This paper cites Maddison, Daniel Tarlow, and Tom Minka.

Gumbel Distillation for Parallel Text Generation Maddison, Daniel Tarlow, and Tom Minka

Reference 49

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Observation f63d1aae-b7b7-4e8b-95d0-907cdde597af · outbound

This paper cites Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz.

Gumbel Distillation for Parallel Text Generation Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz

Reference 50

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Observation d21b0bb5-90e3-474f-8771-443fdec0958f · outbound

This paper cites Pointer Sentinel Mixture Models.

Gumbel Distillation for Parallel Text Generation Pointer Sentinel Mixture Models

Reference 51

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Observation 0872b1bd-6c62-4045-be27-73f664a4c6fb · outbound

This paper cites Can a suit of armor conduct electricity? a new dataset for open book question answering.

Gumbel Distillation for Parallel Text Generation Can a suit of armor conduct electricity? a new dataset for open book question answering

Reference 52

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Observation 337edcbf-c282-4f36-bf47-f24e74ba0af9 · outbound

This paper cites Scaling up Masked Diffusion Models on Text.

Gumbel Distillation for Parallel Text Generation Scaling up Masked Diffusion Models on Text

Reference 53

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Observation c48ef82c-cdf9-4481-9be6-69096d4e3049 · outbound

This paper cites Large Language Diffusion Models.

Gumbel Distillation for Parallel Text Generation Large Language Diffusion Models

Reference 54

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This paper cites Your absorbing discrete diffusion secretly models the conditional distributions of clean data.

Gumbel Distillation for Parallel Text Generation Your absorbing discrete diffusion secretly models the conditional distributions of clean data

Reference 55

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Observation 7e6957a4-bc7f-4ed7-879c-3d50c25a6af1 · outbound

This paper cites The lambada dataset: Word prediction requiring a broad discourse context.

Gumbel Distillation for Parallel Text Generation The lambada dataset: Word prediction requiring a broad discourse context

Reference 56

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Observation 8a006e0b-84df-47a0-8ca9-5d272c49ff72 · outbound

This paper cites Scalable diffusion models with transformers.

Gumbel Distillation for Parallel Text Generation Scalable diffusion models with transformers

Reference 57

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Observation 804c2f43-4d63-4b24-b032-0f60d249053f · outbound

This paper cites Mauve: Measuring the gap between neural text and human text using divergence frontiers.

Gumbel Distillation for Parallel Text Generation Mauve: Measuring the gap between neural text and human text using divergence frontiers

Reference 58

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Observation 2c2a6bb5-81b3-42ad-b219-c6f3543d9bfb · outbound

This paper cites Language models are unsupervised multitask learners.

Gumbel Distillation for Parallel Text Generation Language models are unsupervised multitask learners

Reference 59

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This paper cites Simple and effective masked diffusion language models.

Gumbel Distillation for Parallel Text Generation Simple and effective masked diffusion language models

Reference 60

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Observation d61acc41-1ade-4c33-b31b-238ba8c9fd89 · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at scale.

Gumbel Distillation for Parallel Text Generation Winogrande: An adversarial winograd schema challenge at scale

Reference 61

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Observation 45a00f9f-e97f-4945-be09-b66ca875b0d1 · outbound

This paper cites Your LLM Knows the Future: Uncovering Its Multi-Token Prediction Potential.

Gumbel Distillation for Parallel Text Generation Your LLM Knows the Future: Uncovering Its Multi-Token Prediction Potential

Reference 62

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Observation ba04d43d-6c97-400b-a926-88ce208fb7e2 · outbound

This paper cites Accelerating Transformer Inference for Translation via Parallel Decoding.

Gumbel Distillation for Parallel Text Generation Accelerating Transformer Inference for Translation via Parallel Decoding

Reference 63

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Observation 369c3aeb-bf87-4877-9f70-8fb871b11b7f · outbound

This paper cites Simplified and generalized masked diffusion for discrete data.

Gumbel Distillation for Parallel Text Generation Simplified and generalized masked diffusion for discrete data

Reference 64

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Observation 8e8ea362-f93e-4fc3-a9a2-59118bd329be · outbound

This paper cites Diffusionblocks: Blockwise training for generative models via score-based diffusion.

Gumbel Distillation for Parallel Text Generation Diffusionblocks: Blockwise training for generative models via score-based diffusion

Reference 65

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Observation f2b2d2dc-aace-4f01-8b03-63e4105ddc25 · outbound

This paper cites Ideas in Inference-time Scaling can Benefit Generative Pre-training Algorithms.

Gumbel Distillation for Parallel Text Generation Ideas in Inference-time Scaling can Benefit Generative Pre-training Algorithms

Reference 66

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Observation 3e0dfd65-bc9c-48e7-b6e5-46cce8f96b63 · outbound

This paper cites Denoising diffusion implicit models.

Gumbel Distillation for Parallel Text Generation Denoising diffusion implicit models

Reference 67

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Observation 67b5c60b-8464-483a-9a20-d9ecadba7750 · outbound

This paper cites Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed Inference.

Gumbel Distillation for Parallel Text Generation Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed Inference

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-02T17:49:55.759973Z

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Observation 030d6895-28db-4061-984c-ffadd6a53bcc · outbound

This paper cites Roformer: Enhanced transformer with rotary position embedding.

Gumbel Distillation for Parallel Text Generation Roformer: Enhanced transformer with rotary position embedding

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-02T17:49:55.907757Z

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Observation 4556300d-d2dc-4d7c-aab6-64644361512b · outbound

This paper cites Score-based Continuous-time Discrete Diffusion Models.

Gumbel Distillation for Parallel Text Generation Score-based Continuous-time Discrete Diffusion Models

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-02T17:49:56.071326Z

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Observation f50dbbae-a828-4aca-b6d1-93a936a51bf8 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Gumbel Distillation for Parallel Text Generation LLaMA: Open and Efficient Foundation Language Models

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-02T17:49:56.076090Z

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Observation 85c468bc-7370-440f-b59e-1d4230e3885a · outbound

This paper cites Attention is all you need.

Gumbel Distillation for Parallel Text Generation Attention is all you need

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-02T17:49:56.080589Z

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Observation c8f26ac7-76d0-4dfe-9ce0-91a9433c2be1 · outbound

This paper cites u tte, Janis Fluri, Yuhui Ding, Antonio Orvieto, Bernhard Sch \.

Gumbel Distillation for Parallel Text Generation u tte, Janis Fluri, Yuhui Ding, Antonio Orvieto, Bernhard Sch \

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-02T17:49:56.084750Z

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Observation e860b944-be3e-494d-b3c1-cdf74dc471d9 · outbound

This paper cites Remasking discrete diffusion models with inference-time scaling.

Gumbel Distillation for Parallel Text Generation Remasking discrete diffusion models with inference-time scaling

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-02T17:49:56.089680Z

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Observation 8e24d814-6c66-4164-838b-a3844b318cd1 · outbound

This paper cites Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decoding.

Gumbel Distillation for Parallel Text Generation Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decoding

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-02T17:49:56.094300Z

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Observation 8a0dd78c-64f7-4f70-8151-f812b4d9305c · outbound

This paper cites Ar-diffusion: Auto-regressive diffusion model for text generation.

Gumbel Distillation for Parallel Text Generation Ar-diffusion: Auto-regressive diffusion model for text generation

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-02T17:49:56.098508Z

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Observation 57418943-87ff-414a-ac71-93984eef6c7b · outbound

This paper cites A survey on non-autoregressive generation for neural machine translation and beyond.

Gumbel Distillation for Parallel Text Generation A survey on non-autoregressive generation for neural machine translation and beyond

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-02T17:49:56.102520Z

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Observation 6a783128-ee35-41e7-bd78-c67e13663d48 · outbound

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

Gumbel Distillation for Parallel Text Generation Energy-Based Diffusion Language Models for Text Generation

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-02T17:49:56.106702Z

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Observation 5c488aee-be9a-42c3-b53a-a05f015a99b1 · outbound

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

Gumbel Distillation for Parallel Text Generation Dream 7B: Diffusion Large Language Models

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-02T17:49:56.111129Z

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Observation ab4d5f49-0a90-4eb4-a5bc-68bcce3fa7fa · outbound

This paper cites Hellaswag: Can a machine really finish your sentence? In Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2019.

Gumbel Distillation for Parallel Text Generation Hellaswag: Can a machine really finish your sentence? In Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2019

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-02T17:49:56.115518Z

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Observation dabd7702-7885-4bca-9338-93185b096ff6 · outbound

This paper cites Character-level convolutional networks for text classification.

Gumbel Distillation for Parallel Text Generation Character-level convolutional networks for text classification

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-02T17:49:56.119756Z

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Observation f4aa8f09-1781-428f-a0b4-336a500c0dd1 · outbound

This paper cites Masked diffusion models are secretly time-agnostic masked models and exploit inaccurate categorical sampling.

Gumbel Distillation for Parallel Text Generation Masked diffusion models are secretly time-agnostic masked models and exploit inaccurate categorical sampling

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-02T17:49:56.124588Z

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Observation 8d9a7144-4930-4070-8eac-b4bf0575ac65 · outbound

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

Gumbel Distillation for Parallel Text Generation A Reparameterized Discrete Diffusion Model for Text Generation

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-02T17:49:56.128730Z

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Observation 54d84331-7116-4acd-a7eb-13bf9a5b6c79 · outbound

This paper cites Di$\mathtt{[M]}$O: Distilling Masked Diffusion Models into One-step Generator.

Gumbel Distillation for Parallel Text Generation Di$\mathtt{[M]}$O: Distilling Masked Diffusion Models into One-step Generator

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-02T17:49:56.133260Z

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Observation fc640a8c-afe4-4da4-a885-e66610d7c4bc · outbound

This paper cites write newline.

Gumbel Distillation for Parallel Text Generation write newline

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-02T17:49:56.137563Z

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Observation 931c4a30-f29c-4c86-accc-e9978ff013ec · outbound

This paper cites @esa (Ref.

Gumbel Distillation for Parallel Text Generation @esa (Ref

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-02T17:49:56.142799Z

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Observation 683727e4-5b9c-48ba-92db-54077803cfce · outbound

This paper cites an unresolved cited work.

Gumbel Distillation for Parallel Text Generation Unresolved cited work

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-02T17:49:56.147667Z

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Observation c863a1ac-5e8e-4b15-ac6f-346660989205 · outbound

This paper cites requires knowing that ``Francisco.

Gumbel Distillation for Parallel Text Generation requires knowing that ``Francisco

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-02T17:49:56.151991Z

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