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Paper Citation Record · LEDGER

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective

As of 4 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2607.24507.

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

pith.paper-citation-record.v1
2607.24507 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-31T12:59:59.660062Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

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  • verified fuzzy0
  • unresolved31
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

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Outbound references

Observation 20dec4cc-077b-4faf-a809-9af71771a494 · outbound

This paper cites Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg.

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg

Reference 1

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source=pdf_text observed=2026-07-31T12:59:56.825300Z digest=sha256:abf5556e99810a2bb9c43c72eb26f8a60f21241a86a61110985dc16af7010965

Observation bf01ca5e-1551-4432-be6e-ffa3575b40e4 · outbound

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

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective PIQA: Reasoning about physical commonsense in natural language

Reference 2

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source=pdf_text observed=2026-07-31T12:59:56.908605Z digest=sha256:532f1a1730dc72b3aebaab3699e373a3e182cae36dba8e6a8dc656c11a3b26f9

Observation 16cc943a-a353-4ba7-90cc-cdc0f71c824c · outbound

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

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective A continuous time framework for discrete denoising models

Reference 3

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source=pdf_text observed=2026-07-31T12:59:56.969223Z digest=sha256:8e869a00dc36a815ab62785d328b9a8907750d846688c06963b65739892f97f3

Observation 2a725c2a-de36-4557-ae39-be344bee1a76 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective Training Verifiers to Solve Math Word Problems

Reference 4

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source=pdf_text observed=2026-07-31T12:59:57.028496Z digest=sha256:befed3f2a47952aaa62df3920560e452833a8a53e475004c80fc120f294c5784

Observation 12ecbfe5-956c-4c7a-9779-0bd571f53de4 · outbound

This paper cites Discrete flow matching.

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective Discrete flow matching

Reference 5

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source=pdf_text observed=2026-07-31T12:59:57.156767Z digest=sha256:1e1c12b6ae27fc63a7fd7a2ea33a683dd592585ecfbb8fa987ee7d5f4696c5a6

Observation 71cf3df6-2750-4c2f-b652-a4f7143e81c1 · outbound

This paper cites Masked diffusion models are secretly learned-order autoregressive models.arXiv preprint arXiv:2511.19152, 2025.

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective Masked diffusion models are secretly learned-order autoregressive models.arXiv preprint arXiv:2511.19152, 2025

Reference 6

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source=pdf_text observed=2026-07-31T12:59:57.243727Z digest=sha256:95990fd8f68fdb95996514acb47ee2b8fd3817814a71ffb7855fd1e0ea42412b

Observation 3f3d2279-350e-4d5f-aea4-800dc97d41eb · outbound

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

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective Scaling diffusion language models via adaptation from autoregressive models

Reference 7

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source=pdf_text observed=2026-07-31T12:59:57.304927Z digest=sha256:7d1a8ab47771b1ffa4a8ae2a8a291c23d817d05bacb4939dda052a3f059d110d

Observation f578e697-884b-48f0-901a-3602f42aaf67 · outbound

This paper cites Hashimoto.

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective Hashimoto

Reference 8

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source=pdf_text observed=2026-07-31T12:59:57.398402Z digest=sha256:3db7043e44a9574a1cd3cf0ba657d29139c3f1c893454644a22d456e11ec4885

Observation 3b424685-db0e-4d48-95be-fd87ff40e6f0 · outbound

This paper cites Denoising diffusion probabilistic models.

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective Denoising diffusion probabilistic models

Reference 9

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source=pdf_text observed=2026-07-31T12:59:57.556210Z digest=sha256:a0ec3b7541e13090f3da1f530310a2d3ee30427b1ca339e925ad68f5378f3239

Observation e21bf4ba-d235-49cd-a5d3-30d4c242c0a9 · outbound

This paper cites Gritsenko, Jasmijn Bastings, Ben Poole, Rianne van den Berg, and Tim Salimans.

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective Gritsenko, Jasmijn Bastings, Ben Poole, Rianne van den Berg, and Tim Salimans

Reference 10

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source=pdf_text observed=2026-07-31T12:59:57.650442Z digest=sha256:022f34eecf9a5b58434acd7b059559b120510626a60ce8857c3f48fc68af41ec

Observation 035f786b-dac6-4866-9726-37d358d43fdd · outbound

This paper cites Neural Continuous-Time Markov Chain: Discrete Diffusion via Decoupled Jump Timing and Direction.

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective Neural Continuous-Time Markov Chain: Discrete Diffusion via Decoupled Jump Timing and Direction

Reference 11

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source=pdf_text observed=2026-07-31T12:59:57.748731Z digest=sha256:fa72360cba0ea78fdcabb6fd2dcaa1b2a77d35bb8f097aa64716c26a2cae53a7

Observation af94d7c2-31d1-40b7-91d8-9711cd8b6d0e · outbound

This paper cites Mean-to-Score Discrete Diffusion: Posterior-Mean Denoisers for Score Entropy.

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective Mean-to-Score Discrete Diffusion: Posterior-Mean Denoisers for Score Entropy

Reference 12

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source=pdf_text observed=2026-07-31T12:59:57.852406Z digest=sha256:d02d5aeb28f122925f071c9280e8133c8b51e75d019ac7e589a5c5ff320e48d0

Observation 2897dc50-2f75-45f1-b7a5-0bd8965d1fef · outbound

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

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective Discrete diffusion modeling by estimating the ratios of the data distribution

Reference 13

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source=pdf_text observed=2026-07-31T12:59:57.905226Z digest=sha256:5e983a19b814638e4021890f6bbb944a5d5a0c4b080caf5664c3af90e2629131

Observation 68321145-643d-4fb9-b5de-94ed3fbe1f69 · outbound

This paper cites Large Language Diffusion Models.

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective Large Language Diffusion Models

Reference 14

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source=pdf_text observed=2026-07-31T12:59:57.972472Z digest=sha256:af6fd319e0c4ce74dad7958975df59796c1048045e25f938c3d9bebef70c8174

Observation fd0bab8c-9701-4f90-af1b-10231875ea78 · outbound

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

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective Your absorbing discrete diffusion secretly models the conditional distributions of clean data

Reference 15

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source=pdf_text observed=2026-07-31T12:59:58.036774Z digest=sha256:43832076f0561b7f167c01f1d44e62cb1ecbb537b80c53e228350b3ce266a1a7

Observation 0646a49f-2e69-475f-a4a4-97ed2f623452 · outbound

This paper cites The FineWeb datasets: Decanting the web for the finest text data at scale.

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective The FineWeb datasets: Decanting the web for the finest text data at scale

Reference 16

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source=pdf_text observed=2026-07-31T12:59:58.097824Z digest=sha256:12f6a8acbd28ff0d1c5224c6a35d72f2b2da19f2727f6309994e7ac4314533b4

Observation 14b3d9dc-d09a-4308-a534-9fe6683ddb97 · outbound

This paper cites Language models are unsupervised multitask learners.

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective Language models are unsupervised multitask learners

Reference 17

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source=pdf_text observed=2026-07-31T12:59:58.242287Z digest=sha256:886a0487f582f949c3385bce375596718b646ea4049f69dc41a3270eeef71893

Observation a5cfe1bb-a2cc-4416-808b-a5feaa4ef972 · outbound

This paper cites Chiu, Alexander Rush, and Volodymyr Kuleshov.

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective Chiu, Alexander Rush, and Volodymyr Kuleshov

Reference 18

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source=pdf_text observed=2026-07-31T12:59:58.301643Z digest=sha256:2afb8dca8c49c9e3f37379c4b5adce1f1dbf97f20858cefb4f5294cde896c460

Observation 87ada045-686d-453e-83d4-bdcf86a6a7bd · outbound

This paper cites Chiu, and Volodymyr Kuleshov.

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective Chiu, and Volodymyr Kuleshov

Reference 19

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source=pdf_text observed=2026-07-31T12:59:58.369175Z digest=sha256:78e2553a3f72efa560829cac45e53fa82f1b2008bf950c7ca6e15c90dd40e5eb

Observation 281315f7-f37c-43c5-81a6-a6ef109bbd53 · outbound

This paper cites WinoGrande: Anadversarial winograd schema challenge at scale.Communications of the ACM, 64(9):99–106, 2021.

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective WinoGrande: Anadversarial winograd schema challenge at scale.Communications of the ACM, 64(9):99–106, 2021

Reference 20

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source=pdf_text observed=2026-07-31T12:59:58.427255Z digest=sha256:4200f43febc170b494a3a6efc2b428afd2f651364d0eac28b626f0b799c308d2

Observation 68277356-feb8-4660-abe1-e3035e9242cd · outbound

This paper cites Social IQa: Commonsense reasoning about social interactions.

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective Social IQa: Commonsense reasoning about social interactions

Reference 21

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source=pdf_text observed=2026-07-31T12:59:58.516906Z digest=sha256:e58c2d4457de9707a8c9f3b3010a01d07f0104450fb196952e1a81d5e3572859

Observation a3fa85d6-7851-44f2-a41c-9b981eb334a2 · outbound

This paper cites an unresolved cited work.

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective Unresolved cited work

Reference 22

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source=pdf_text observed=2026-07-31T12:59:58.605577Z digest=sha256:f7598a5305fe5efb62d966abb13076e23c70923bca5e02e0278a84db36e68a1a

Observation 1474d328-5903-4715-9561-0c0f5751a588 · outbound

This paper cites Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole.

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole

Reference 23

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source=pdf_text observed=2026-07-31T12:59:58.682133Z digest=sha256:df437f94bd47333483e7a337b12abae4b5ea4cdff36d72829c84c570ee08b848

Observation c6e89e59-f489-488f-85e5-2e4382b36b2e · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 24

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source=pdf_text observed=2026-07-31T12:59:58.738385Z digest=sha256:d9498605b0c219c8cad73ecfedb84055510ae3edf7285cabe2f8712d349eb5a1

Observation e0ec3971-3b63-443b-8af8-f3b617337c12 · outbound

This paper cites A deep and tractable density estimator.

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective A deep and tractable density estimator

Reference 25

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source=pdf_text observed=2026-07-31T12:59:58.795006Z digest=sha256:3ced2b325add7bccb3a3a2a397dd1145529e65cde879f8add0828d3c2019765e

Observation 6f248dad-a9aa-4bfe-b87f-9ec91dc2d0f2 · outbound

This paper cites Generalized interpolating discrete diffusion.

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective Generalized interpolating discrete diffusion

Reference 26

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source=pdf_text observed=2026-07-31T12:59:58.951381Z digest=sha256:fc29bbb121bc0ee5e833a54ca00f649072dd14da4a9e8a22f06545b7a90948d8

Observation 2bfd36be-cf3a-4878-97f9-21f84650bea8 · outbound

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

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective Dream 7B: Diffusion Large Language Models

Reference 27

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source=pdf_text observed=2026-07-31T12:59:59.050918Z digest=sha256:fee362cd4b4923ad01325101bb02914a9dcfdc4a21aa0b63523013ab44e4ae0f

Observation 89a7a05d-e444-403c-a122-dc2904829241 · outbound

This paper cites HellaSwag: Can a ma- chine really finish your sentence? InProceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4791–4800, 2019.

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective HellaSwag: Can a ma- chine really finish your sentence? InProceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4791–4800, 2019

Reference 28

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source=pdf_text observed=2026-07-31T12:59:59.169037Z digest=sha256:27569edd2cc81cd7b461d4933c286c422aa63bc935c942b60aa1a3fee2595bd5

Observation a2fb57e2-f0ca-4d29-abf5-09148d861e4b · outbound

This paper cites 2.fork= 1,...,Kdo x0∼p data, t∼Unif[10 −3,1), z t∼q t|0(·|x 0).

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective 2.fork= 1,...,Kdo x0∼p data, t∼Unif[10 −3,1), z t∼q t|0(·|x 0)

Reference 29

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source=pdf_text observed=2026-07-31T12:59:59.362634Z digest=sha256:57277e7c6d3afc5d0ac68d99287ab6eeb9ced8ae7768f3bb7f22f62b872ae9c3

Observation c01b2b2a-868c-4c98-bea0-7ca77f62e08f · outbound

This paper cites 8ton D".

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective 8ton D"

Reference 30

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source=pdf_text observed=2026-07-31T12:59:59.421473Z digest=sha256:3e6949a5d161c9dd184501fe2031d14da96a769329524bdfd87e3ba5990fe9e3

Observation 1b7ec7f2-e09f-4ab4-b7b4-0730e40b9c0e · outbound

This paper cites - Results of liquids are applied into the blood so that sensors are sensed not with notable local overlay.

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective - Results of liquids are applied into the blood so that sensors are sensed not with notable local overlay

Reference 31

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source=pdf_text observed=2026-07-31T12:59:59.531883Z digest=sha256:361ecaef162abb58de90875aaa8dc7f3bf2d8a2480ac45885226b77dafe63d0d

Observation b7c8ccb3-1c8b-47b4-8166-420f64018744 · outbound

This paper cites While working with retailer, we preliminaryised the idea of giving the retailer (or shootyp to get that) exact power-bind we could offer it in 2016.

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective While working with retailer, we preliminaryised the idea of giving the retailer (or shootyp to get that) exact power-bind we could offer it in 2016

Reference 32

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source=pdf_text observed=2026-07-31T12:59:59.660062Z digest=sha256:928298a3bc356b7122f8a7cebded22c5d7d16372da371773735478b9c79b2ae7

Pith citing papers

No inbound Pith citation observations are available.