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Paper Citation Record · LEDGER

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework

As of 15 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2505.24245.

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

pith.paper-citation-record.v1
2505.24245 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:33:38.368887Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

41 of 41 outbound references displayed

  • verified exact1
  • verified fuzzy16
  • unresolved24
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ac94f1d8-26b4-4e5b-bd0e-27378c5e0056 · outbound

This paper cites GPT-4 Technical Report.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework GPT-4 Technical Report

Reference 1

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source=pdf_text observed=2026-08-07T12:33:34.854572Z digest=sha256:f158f84e4d8d44822d02a46c77c63b000e5e98c32bf16f9fdff420b9f5f7454f

Observation fc9c0cdf-baa6-4b43-bfe7-bbb6c0b7abd8 · outbound

This paper cites Flamingo: a visual language model for few-shot learning.Advances in neural information processing systems, 35:23716–23736,.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Flamingo: a visual language model for few-shot learning.Advances in neural information processing systems, 35:23716–23736,

Reference 2

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source=pdf_text observed=2026-08-07T12:33:34.991095Z digest=sha256:30cb3eb38b9af21be3e088f4a9cc999ab0a8289ae69bcf94578005bd0809eb08

Observation 9c445e6c-2b1a-4abc-a2a4-1333cf76f69c · outbound

This paper cites ShapeNet: An Information-Rich 3D Model Repository.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework ShapeNet: An Information-Rich 3D Model Repository

Reference 3

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source=pdf_text observed=2026-08-07T12:33:35.147821Z digest=sha256:242ed78b9e55ee337c315a5b93f0a2e5034aa59693916a9e1a6ad931564653b8

Observation 17e07eeb-047b-4a72-be1a-3b9a93e0a164 · outbound

This paper cites MeshXL: Neural Coordinate Field for Generative 3D Foundation Models.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework MeshXL: Neural Coordinate Field for Generative 3D Foundation Models

Reference 4

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source=pdf_text observed=2026-08-07T12:33:35.284816Z digest=sha256:b004be1e78cbca9bf9abbde4e4531e019dcb354de4dca8b35256f8b047c40816

Observation 57d24baa-757d-4ad8-8621-23b055defbc0 · outbound

This paper cites Text-to-3d using gaussian splatting.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Text-to-3d using gaussian splatting

Reference 5

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raw_fallback, observed 2026-08-07T12:33:41.406199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-07T12:33:35.426270Z digest=sha256:19cd3d7dbfcfccdaab22504a7de5326a2fa0615797185bff6de925a8ba6e2631

Observation 2237ab6c-7e09-45de-bd10-25c8fa0cd992 · outbound

This paper cites Sdfusion: Multimodal 3d shape completion, reconstruction, and generation.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Sdfusion: Multimodal 3d shape completion, reconstruction, and generation

Reference 6

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raw_fallback, observed 2026-08-07T12:33:41.248123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-07T12:33:35.595436Z digest=sha256:d863139cbe138d443b3563e34fce1e712de59b879ac36bd5b514e957d6bac2fa

Observation acfb5287-8caa-4076-a3e6-0645244eeb67 · outbound

This paper cites Objaverse: A universe of annotated 3d objects.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Objaverse: A universe of annotated 3d objects

Reference 7

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source=pdf_text observed=2026-08-07T12:33:35.693755Z digest=sha256:6a91b66341bffffe8827c4535e3dc1715ea9621bca6a1e304ac1e0cae5e4ab14

Observation 5bb6ce1c-8734-409c-8ecf-c6632d8ccc7a · outbound

This paper cites Masked autoencoders are scalable vision learners.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Masked autoencoders are scalable vision learners

Reference 8

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raw_fallback, observed 2026-08-07T12:33:41.118050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-07T12:33:35.758894Z digest=sha256:1c269b0e65607303acd03d9a0628def675fff7f77eb05a5fcfad83e8028bc086

Observation e04075b3-eaa6-4af1-8705-ce0c097dd671 · outbound

This paper cites DreamTime: An Improved Optimization Strategy for Diffusion-Guided 3D Generation.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework DreamTime: An Improved Optimization Strategy for Diffusion-Guided 3D Generation

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:33:35.822212Z digest=sha256:a81634b7616c4f587ca5dabcea8edc47e961b0b9153f37130d19bab544c154d4

Observation 3bbe6e99-3960-4208-9d45-eb4b158e0da3 · outbound

This paper cites Shap-E: Generating Conditional 3D Implicit Functions.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Shap-E: Generating Conditional 3D Implicit Functions

Reference 10

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source=pdf_text observed=2026-08-07T12:33:35.918512Z digest=sha256:dbaa8203b56c706ff4cec60798af71c050753a38ed399a13cb7aba50c726340a

Observation 00f3cf73-ba51-4161-a09e-941db918af30 · outbound

This paper cites 3d gaussian splatting for real-time radiance field rendering.ACM Trans.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework 3d gaussian splatting for real-time radiance field rendering.ACM Trans

Reference 11

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source=pdf_text observed=2026-08-07T12:33:36.005351Z digest=sha256:aa6f1537905c65d5137c7c79fdd97eef52655c572548a907d47e8a3f4867f539

Observation 208ce92a-4569-4530-b08b-bd7a29cb40a2 · outbound

This paper cites Segment any- thing.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Segment any- thing

Reference 12

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source=pdf_text observed=2026-08-07T12:33:36.053994Z digest=sha256:f69749f9f7d1004c9f6acdf318437acfedb2a7de810f17f429a402d7b3eb59eb

Observation d9d2e18f-7b04-48a7-bd52-de60e38c468e · outbound

This paper cites Diffusion- sdf: Text-to-shape via voxelized diffusion.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Diffusion- sdf: Text-to-shape via voxelized diffusion

Reference 13

Resolution
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raw_fallback, observed 2026-08-07T12:33:40.983218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-07T12:33:36.146461Z digest=sha256:4e4bdfd99eb9c41a44f9801692bcdfc10d13ee789697eab8091cca6ff2997090

Observation dedbf24c-5b3a-47e3-9845-6dfd5df62e22 · outbound

This paper cites Autoregressive Image Generation without Vector Quantization.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Autoregressive Image Generation without Vector Quantization

Reference 14

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:33:36.235172Z digest=sha256:b6cd0b5b704c64ea2628107b8f059f9b818759447afcf6717127070b5c22b38f

Observation aa8df983-4bf5-4b52-a281-ffdf2d6e18b1 · outbound

This paper cites Magic3d: High-resolution text-to-3d content creation.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Magic3d: High-resolution text-to-3d content creation

Reference 15

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verified fuzzy
raw_fallback, observed 2026-08-07T12:33:40.763173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-07T12:33:36.300200Z digest=sha256:2725d62f9ce9d6b76bb2402ae5bd02bad1074f699d242507431ffe5485afa8b8

Observation 327095cf-11d8-467d-ac96-8cc0118edb4e · outbound

This paper cites FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow

Reference 16

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local_arxiv, observed 2026-08-07T12:33:38.610708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-07T12:33:36.378809Z digest=sha256:b2263f9ddbcbe099ff1def98658845ccd82d6ed2671c7f4262992ba47a81031f

Observation 57568344-f8a1-4cee-9aa8-c9327e5b38bb · outbound

This paper cites Nerf: Representing scenes as neural radiance fields for view syn- thesis.Communications of the ACM, 65(1):99–106, 2021.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Nerf: Representing scenes as neural radiance fields for view syn- thesis.Communications of the ACM, 65(1):99–106, 2021

Reference 17

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source=pdf_text observed=2026-08-07T12:33:36.480926Z digest=sha256:01e9cebd8b6534794fc64e35d00afd4603bc953431a92f1065b4aadeffbd6427

Observation da0c2d04-8ac2-43d8-bc44-4443fb563095 · outbound

This paper cites Autosdf: Shape priors for 3d comple- tion, reconstruction and generation.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Autosdf: Shape priors for 3d comple- tion, reconstruction and generation

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-07T12:33:36.542130Z digest=sha256:7838512bf12e0444ad69ad01cca389569d32fa227686cf44842bf7278f38213a

Observation c5168fa8-201e-43a0-944d-880c5587a814 · outbound

This paper cites Polygen: An autoregressive generative model of 3d meshes.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Polygen: An autoregressive generative model of 3d meshes

Reference 19

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raw_fallback, observed 2026-08-07T12:33:40.424862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-07T12:33:36.616195Z digest=sha256:4362a616a4056e7e37e0fa0789c0fe12de73fe9b99fb06daa1a18a86ea821b90

Observation 73d07521-8dda-4498-903a-0acc8c80afdd · outbound

This paper cites Point-E: A System for Generating 3D Point Clouds from Complex Prompts.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Point-E: A System for Generating 3D Point Clouds from Complex Prompts

Reference 20

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source=pdf_text observed=2026-08-07T12:33:36.695604Z digest=sha256:a69acbb8a8e9267c66099bc757cb44415a0457b64562dd760aee73757b17b494

Observation 226b3bd2-9914-4aa1-9846-a04f79dd6aba · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework DINOv2: Learning Robust Visual Features without Supervision

Reference 21

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source=pdf_text observed=2026-08-07T12:33:36.756072Z digest=sha256:20a2bec9703dfbd878dd0b93236202b9f52bf278178c4229939e5f14155d4b12

Observation 72b2db93-28b6-46cd-b06a-f444cc5ef701 · outbound

This paper cites DreamFusion: Text-to-3D using 2D Diffusion.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework DreamFusion: Text-to-3D using 2D Diffusion

Reference 22

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source=pdf_text observed=2026-08-07T12:33:36.841766Z digest=sha256:c463a665a106af6a529b931c9a954af0a9b83fd49554cfc0f9673618f7ec06f4

Observation bad6702b-8dc9-4f54-b718-8a6db91abeb2 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Learning transferable visual models from natural language supervi- sion

Reference 23

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source=pdf_text observed=2026-08-07T12:33:36.932320Z digest=sha256:e4045e0867d80ad1a087a5f61408ed2c9635ce5f2769da9f59500a62efc056b1

Observation 687f1646-a635-4c6c-86ff-ec7cf0861352 · outbound

This paper cites Zero-shot text-to-image generation.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Zero-shot text-to-image generation

Reference 24

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raw_fallback, observed 2026-08-07T12:33:40.289153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-07T12:33:37.036565Z digest=sha256:d74f813b0dc3cdf73718584f634c074c9d432a370067b321738180c1ec56a013

Observation 9f5cc8b7-3b0e-4c0e-8f98-321995e2d3c9 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework High-resolution image synthesis with latent diffusion models

Reference 25

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source=pdf_text observed=2026-08-07T12:33:37.110644Z digest=sha256:04cacebbe1940f7d7746ee2c1d83b15c993eabfc48bdb5b14eaba9b05ecdd791

Observation 63366473-daa8-4f29-b3cd-bffc1a179e68 · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.Advances in neural information processing systems, 35:36479–36494, 2022.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Photorealistic text-to-image diffusion models with deep language understanding.Advances in neural information processing systems, 35:36479–36494, 2022

Reference 26

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source=pdf_text observed=2026-08-07T12:33:37.190675Z digest=sha256:cce74d5de38eb50fd829d45d1a1aa514e71fb17086a497cb3a3b1e9383095fee

Observation d29d2450-72a9-4999-b891-6bc0eff2eee2 · outbound

This paper cites Meshgpt: Generating triangle meshes with decoder-only transformers.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Meshgpt: Generating triangle meshes with decoder-only transformers

Reference 27

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raw_fallback, observed 2026-08-07T12:33:40.115863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-07T12:33:37.271378Z digest=sha256:0b7ceaa9b6c34f9b6371a59b8279e4605417050b9876a95f3330a238286dce46

Observation 41822879-05f7-4508-a8d7-6c198db6fc75 · outbound

This paper cites Make-it-3d: High-fidelity 3d creation from a single image with diffusion prior.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Make-it-3d: High-fidelity 3d creation from a single image with diffusion prior

Reference 28

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source=pdf_text observed=2026-08-07T12:33:37.342613Z digest=sha256:7f5bf0b8b7d855070aa7f50983bd528ac411e53de66cfc3c92a031f9f58e1c1e

Observation 2d4d5483-f788-49f6-9f6d-be072f4c38d8 · outbound

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

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework LLaMA: Open and Efficient Foundation Language Models

Reference 29

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source=pdf_text observed=2026-08-07T12:33:37.442699Z digest=sha256:a0d2128c9631987a390e3cbe09d757fdeb79cba0a7dbb0bf49b296203ad08f10

Observation 98fdb654-897f-4ac9-9291-d08f21299333 · outbound

This paper cites Attention is all you need.Advances in Neural Information Processing Systems, 2017.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Attention is all you need.Advances in Neural Information Processing Systems, 2017

Reference 30

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:33:37.523208Z digest=sha256:3616b575068f4f90655ed5e27b960082ce94ee0b7a7df3925fcbc29f26d43938

Observation 85e0ad5c-177f-4b2e-ba1d-068861af7717 · outbound

This paper cites Hd- fusion: Detailed text-to-3d generation leveraging multiple noise estimation.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Hd- fusion: Detailed text-to-3d generation leveraging multiple noise estimation

Reference 31

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raw_fallback, observed 2026-08-07T12:33:39.964739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-07T12:33:37.613870Z digest=sha256:71702c88fbd9104c304cedf5aa4ca8114481a853dd295baf76b65188c46ebf1c

Observation e1c5edcd-23a3-49ae-8406-8b8553df24be · outbound

This paper cites Disn: Deep implicit surface network for high-quality single-view 3d reconstruction.Ad- vances in neural information processing systems, 32, 2019.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Disn: Deep implicit surface network for high-quality single-view 3d reconstruction.Ad- vances in neural information processing systems, 32, 2019

Reference 32

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raw_fallback, observed 2026-08-07T12:33:39.778378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-07T12:33:37.693451Z digest=sha256:521dbe958be00eeafe083457999da9ae7c5e68c2eeeacd860e27dfbdc41080fe

Observation eee02389-cf48-41fb-8fe0-c7a476b4611e · outbound

This paper cites Ulip: Learning a unified representation of language, images, and point clouds for 3d understanding.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Ulip: Learning a unified representation of language, images, and point clouds for 3d understanding

Reference 33

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:33:37.768481Z digest=sha256:f0d46e81847e574797ec1b15da6dff7940fd98d5b18a62446cd56f900b052c0e

Observation af029760-1e12-476b-8a70-9e9892f3da45 · outbound

This paper cites Points-to-3d: Bridging the gap be- tween sparse points and shape-controllable text-to-3d gener- ation.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Points-to-3d: Bridging the gap be- tween sparse points and shape-controllable text-to-3d gener- ation

Reference 34

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verified fuzzy
raw_fallback, observed 2026-08-07T12:33:39.579199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-07T12:33:37.856553Z digest=sha256:db25d1d0a203d91f7a230eca5d4985a4019c3b0a79071071d2ac37b6efb59137

Observation 4be91d63-555f-43a0-afc8-64631d1e3a10 · outbound

This paper cites 3dilg: Ir- regular latent grids for 3d generative modeling.Advances in Neural Information Processing Systems, 35:21871–21885,.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework 3dilg: Ir- regular latent grids for 3d generative modeling.Advances in Neural Information Processing Systems, 35:21871–21885,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:33:39.406195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-07T12:33:37.943418Z digest=sha256:7c105210de1841b089a28bbe38adbf339a6eb05ca48ef029f947176cd8ea8e39

Observation 765be0fa-94b9-4959-a1ab-aa2adb10b946 · outbound

This paper cites 3dshape2vecset: A 3d shape representation for neu- ral fields and generative diffusion models.ACM Transactions on Graphics (TOG), 42(4):1–16, 2023.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework 3dshape2vecset: A 3d shape representation for neu- ral fields and generative diffusion models.ACM Transactions on Graphics (TOG), 42(4):1–16, 2023

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:33:39.264135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-07T12:33:38.016442Z digest=sha256:2a5143134c71bae0d2fdcdf81eb4a1e4b362ce60eb784bb6d5bc5eab5e46e829

Observation 1adbf657-3076-4a16-bed6-286a73275ddb · outbound

This paper cites GaussianCube: A Structured and Explicit Radiance Representation for 3D Generative Modeling.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework GaussianCube: A Structured and Explicit Radiance Representation for 3D Generative Modeling

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-07T12:33:38.104585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:33:38.104585Z digest=sha256:23ed3d262a094304dfdb94d7ad1f9ba7824c455890763894ef005e304bce8263

Observation d26a4439-31d4-496f-bb74-dd5e7854e083 · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Adding conditional control to text-to-image diffusion models

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-07T12:33:38.157033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:33:38.157033Z digest=sha256:a94de85373c83dd5778eb08c6c1b0d33f263d071e3bfc53eae3d616e09596ee1

Observation 09731d65-093a-4de5-b92f-484bbfbb6a53 · outbound

This paper cites Clay: A controllable large-scale generative model for creat- ing high-quality 3d assets.ACM Transactions on Graphics (TOG), 43(4):1–20, 2024.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Clay: A controllable large-scale generative model for creat- ing high-quality 3d assets.ACM Transactions on Graphics (TOG), 43(4):1–20, 2024

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:33:39.091365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-07T12:33:38.230413Z digest=sha256:c2addb29a5b4243348f242adc6fab84f49c177f154c26aa98d95346df1652ef5

Observation f8ab4ab2-90ac-4046-96ae-78b2e6bd8874 · outbound

This paper cites Michelangelo: Conditional 3d shape generation based on shape-image-text aligned latent representation.Advances in Neural Information Processing Systems, 36, 2024.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Michelangelo: Conditional 3d shape generation based on shape-image-text aligned latent representation.Advances in Neural Information Processing Systems, 36, 2024

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:33:38.937115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-07T12:33:38.302030Z digest=sha256:454e71d93e5d7a71c8ad4ce56881236357b8d82f34f5f269e29b03673211a447

Observation 861e2db1-8939-48a8-9724-d2f3b863eea2 · outbound

This paper cites HiFA: High-fidelity Text-to-3D Generation with Advanced Diffusion Guidance.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework HiFA: High-fidelity Text-to-3D Generation with Advanced Diffusion Guidance

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-07T12:33:38.368887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:33:38.368887Z digest=sha256:d34e6aa08d29c74e14d6746c9f85b0ec6caeb99f6bfd03dacb82370874d0e3b1

Pith citing papers

No inbound Pith citation observations are available.