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Paper Citation Record · LEDGER

Making LLaMA SEE and Draw with SEED Tokenizer

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 51 inbound Pith citation observations for arXiv:2310.01218.

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

pith.paper-citation-record.v1
2310.01218 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 51 of 51 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:48:03.209548Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T20:16:29.588718Z

Reference resolution

0 of 0 outbound references displayed

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  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 70162ff3-a8ce-43d7-86d6-46e45d12b224 · inbound

Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models cites this paper.

Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models Making LLaMA SEE and Draw with SEED Tokenizer

Reference 49

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verified exact
arxiv_id, observed 2026-05-17T07:44:47.453596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-17T07:44:47.355960Z digest=sha256:ce61062e40018db1562e066d7527ca1c4532bf12cc354db8368ada8527821bc4

Observation 5892f0c8-53f8-4454-b1e4-d0210078372b · inbound

SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation cites this paper.

SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation Making LLaMA SEE and Draw with SEED Tokenizer

Reference 15

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arxiv_id, observed 2026-05-15T22:48:36.390334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-15T22:48:36.010306Z digest=sha256:97f6e0b4c954c1293feaf73864f90424914890706a9c9a17bbf724e5715b1f0d

Observation 239459fe-7563-48fd-a297-eca4d5de87d5 · inbound

Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding cites this paper.

Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding Making LLaMA SEE and Draw with SEED Tokenizer

Reference 11

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verified exact
arxiv_id, observed 2026-05-16T14:58:37.478355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-16T14:58:37.383749Z digest=sha256:92d674b18b0324fed0099ac2e4d8e8893b9e87f649e08969aaf291a65e9ff05c

Observation 9ff8c8a9-dfd0-4fc9-850a-e8352882a5e2 · inbound

Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation cites this paper.

Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation Making LLaMA SEE and Draw with SEED Tokenizer

Reference 11

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verified exact
arxiv_id, observed 2026-05-11T22:09:16.881241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-11T22:09:16.622717Z digest=sha256:8dd8f0769cb3ef42f3edecc9e8ef62eed0d47a3f7af5d3cc643f970efe24ab4a

Observation 46622577-559c-4ea9-afda-31666510f829 · inbound

LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models cites this paper.

LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models Making LLaMA SEE and Draw with SEED Tokenizer

Reference 4

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arxiv_id, observed 2026-05-17T05:19:22.459326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-17T05:19:22.423762Z digest=sha256:0b06cdf679826068734217aae3b0d3c54a7249561ab1c3a2d70aca3187389c4f

Observation 6412ae67-e7cb-450c-b736-4f27b31d55a5 · inbound

Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation cites this paper.

Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation Making LLaMA SEE and Draw with SEED Tokenizer

Reference 28

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arxiv_id, observed 2026-05-15T22:09:16.479085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-15T22:09:16.001309Z digest=sha256:a200d10a5266bdc03c5dcc99a1247fc9582ef285285f034dcb582aa01ce762bc

Observation 6a992442-55ca-4ece-8619-4e6d2a23cb7b · inbound

Instruction-Guided Editing Controls for Images and Multimedia: A Survey in LLM era cites this paper.

Instruction-Guided Editing Controls for Images and Multimedia: A Survey in LLM era Making LLaMA SEE and Draw with SEED Tokenizer

Reference 78

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no resolver link, observed 2026-08-12T20:10:24.255889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T20:10:24.255889Z digest=sha256:6779f1cb191fde443cbc25957c459525c56e8f8785fe7cd4dc80373c3cdceca5

Observation 050a9970-6069-4910-b918-cb01401a1834 · inbound

MUSE-VL: Modeling Unified VLM through Semantic Discrete Encoding cites this paper.

MUSE-VL: Modeling Unified VLM through Semantic Discrete Encoding Making LLaMA SEE and Draw with SEED Tokenizer

Reference 21

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no resolver link, observed 2026-08-12T12:37:38.366549Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T12:37:38.366549Z digest=sha256:1ab8c2e30bb8c28850b11b2401bf46806f4cf37ec6019e79a3352d94c9d102fd

Observation f5df30e3-daed-4e0e-b44a-332eaf4051c5 · inbound

OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation cites this paper.

OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation Making LLaMA SEE and Draw with SEED Tokenizer

Reference 22

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no resolver link, observed 2026-08-12T11:10:54.624006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T11:10:54.624006Z digest=sha256:15d68c6e55bc0774f8a376192264246b2f22d18b221a78ce151c31d8b4f3f899

Observation 2a352133-5948-4b17-8b7c-4bd3dd3493a4 · inbound

X-Prompt: Towards Universal In-Context Image Generation in Auto-Regressive Vision Language Foundation Models cites this paper.

X-Prompt: Towards Universal In-Context Image Generation in Auto-Regressive Vision Language Foundation Models Making LLaMA SEE and Draw with SEED Tokenizer

Reference 23

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no resolver link, observed 2026-08-12T00:56:59.580658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T00:56:59.580658Z digest=sha256:eb5472b2a6e87b4cb15bd21537c1d1bdf21c871fb480555a776e6f73c852a5d6

Observation 340650dd-c441-4c0e-abe9-d8a31787b639 · inbound

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation cites this paper.

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Making LLaMA SEE and Draw with SEED Tokenizer

Reference 16

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no resolver link, observed 2026-08-11T21:30:08.258608Z

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source=pdf_text observed=2026-08-11T21:30:08.258608Z digest=sha256:354b7e61e859a431398bb11cf6ab18508fb330ba182bc9f31a3d18ad6d36fb47

Observation 3179b8c2-6059-45e9-9987-8cb2b594a1a3 · inbound

EgoPlan-Bench2: A Benchmark for Multimodal Large Language Model Planning in Real-World Scenarios cites this paper.

EgoPlan-Bench2: A Benchmark for Multimodal Large Language Model Planning in Real-World Scenarios Making LLaMA SEE and Draw with SEED Tokenizer

Reference 38

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no resolver link, observed 2026-08-11T21:30:00.136000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T21:30:00.136000Z digest=sha256:407d96aa8da2c9366255b79a237ab924fede75e7382aca490a2bdb6b14551386

Observation 78898fc8-66cf-4b12-b871-78e0c9f0cdf3 · inbound

MuMu-LLaMA: Multi-modal Music Understanding and Generation via Large Language Models cites this paper.

MuMu-LLaMA: Multi-modal Music Understanding and Generation via Large Language Models Making LLaMA SEE and Draw with SEED Tokenizer

Reference 20

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no resolver link, observed 2026-08-11T19:31:43.789931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T19:31:43.789931Z digest=sha256:38e764069e6efe4e352cdfa88f86968eb991c1dccc5ac600f27dc62cf80ff27e

Observation 06a20c3f-93ef-455b-9f24-5b6c4a46de20 · inbound

ILLUME: Illuminating Your LLMs to See, Draw, and Self-Enhance cites this paper.

ILLUME: Illuminating Your LLMs to See, Draw, and Self-Enhance Making LLaMA SEE and Draw with SEED Tokenizer

Reference 15

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no resolver link, observed 2026-08-11T19:29:52.601322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T19:29:52.601322Z digest=sha256:4a798833510bbde4b11c110c02a51c97be300318a92429739ccce5bec575cc8e

Observation 93142e31-20c6-43bc-96bf-f63d2547d81f · inbound

SoftVQ-VAE: Efficient 1-Dimensional Continuous Tokenizer cites this paper.

SoftVQ-VAE: Efficient 1-Dimensional Continuous Tokenizer Making LLaMA SEE and Draw with SEED Tokenizer

Reference 31

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no resolver link, observed 2026-08-11T15:33:13.153399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:33:13.153399Z digest=sha256:cec499631ffd2d65d21ab4acb47e6e346eb315691b0dff5ac097c9c929fd7cb0

Observation f155e2a1-a8a8-41b7-9cf0-7ce3355b4522 · inbound

IDEA-Bench: How Far are Generative Models from Professional Designing? cites this paper.

IDEA-Bench: How Far are Generative Models from Professional Designing? Making LLaMA SEE and Draw with SEED Tokenizer

Reference 11

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no resolver link, observed 2026-08-11T14:39:28.390420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:39:28.390420Z digest=sha256:737c918e53555f6200e01000174a2cba9a012f94b891c3fdd658fda2d5b6535f

Observation c8b0ed51-e531-441f-914c-5fd9f51aba65 · inbound

ChatDiT: A Training-Free Baseline for Task-Agnostic Free-Form Chatting with Diffusion Transformers cites this paper.

ChatDiT: A Training-Free Baseline for Task-Agnostic Free-Form Chatting with Diffusion Transformers Making LLaMA SEE and Draw with SEED Tokenizer

Reference 9

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no resolver link, observed 2026-08-11T14:01:05.296562Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:01:05.296562Z digest=sha256:78c1f90198ff46d75177813d7970e1754ca1ef71d89eb189be356cd3ac5f2097

Observation 50ed3788-2b69-4a11-939a-294476e8b714 · inbound

Next Patch Prediction for Autoregressive Visual Generation cites this paper.

Next Patch Prediction for Autoregressive Visual Generation Making LLaMA SEE and Draw with SEED Tokenizer

Reference 26

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no resolver link, observed 2026-08-11T11:37:41.952024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:37:41.952024Z digest=sha256:4ac008f2b1c49e484a191fde87db02fe06746d009dcf53566f9251bd44f777db

Observation c35e9009-05ac-4cb3-b827-e2ef6d93259d · inbound

CoF: Coarse to Fine-Grained Image Understanding for Multi-modal Large Language Models cites this paper.

CoF: Coarse to Fine-Grained Image Understanding for Multi-modal Large Language Models Making LLaMA SEE and Draw with SEED Tokenizer

Reference 6

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source=pdf_text observed=2026-08-11T06:06:07.038110Z digest=sha256:e593723b92047643ff353cf03cf6bfd0ecb3696459093ffc67fb11cca10c5a9e

Observation 643a5392-fc0c-4d17-9e0a-683554bf27d4 · inbound

Next Token Prediction Towards Multimodal Intelligence: A Comprehensive Survey cites this paper.

Next Token Prediction Towards Multimodal Intelligence: A Comprehensive Survey Making LLaMA SEE and Draw with SEED Tokenizer

Reference 132

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-11T14:59:01.695742Z digest=sha256:5768645966f089cd1dd6c8b19981f648531d60fd5a48dbf560ae52b307887f06

Observation 200f4aed-69d5-4c0e-8afd-77519d92a790 · inbound

Visual Large Language Models for Generalized and Specialized Applications cites this paper.

Visual Large Language Models for Generalized and Specialized Applications Making LLaMA SEE and Draw with SEED Tokenizer

Reference 232

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no resolver link, observed 2026-08-10T22:08:09.798294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:08:09.798294Z digest=sha256:dae2d86502f1e6be30c6886caf80007a1155903fd72385520fdd00f885e518c0

Observation 4f9696d8-933d-4d91-8b84-0d05be0fc9a4 · inbound

UniCoRN: Unified Commented Retrieval Network with LMMs cites this paper.

UniCoRN: Unified Commented Retrieval Network with LMMs Making LLaMA SEE and Draw with SEED Tokenizer

Reference 27

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no resolver link, observed 2026-08-08T05:54:15.899573Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:54:15.899573Z digest=sha256:5e1fc5d8ed000bdde143d5466de7ccce4b0489db7515b0114025d728980c5a42

Observation 063107a9-8985-4b3a-ad3b-6b88fed99714 · inbound

DualToken: Towards Unifying Visual Understanding and Generation with Dual Visual Vocabularies cites this paper.

DualToken: Towards Unifying Visual Understanding and Generation with Dual Visual Vocabularies Making LLaMA SEE and Draw with SEED Tokenizer

Reference 13

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verified exact
arxiv_id, observed 2026-05-22T23:52:16.850621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-22T23:51:43.934329Z digest=sha256:33771c15c1ea8fbe5bc239f326d997480051e9e654cd8299f3cdd361be7fa940

Observation edaf81b3-ae26-44a4-8702-9928e36e2e53 · inbound

Generative Multimodal Pretraining with Discrete Diffusion Timestep Tokens cites this paper.

Generative Multimodal Pretraining with Discrete Diffusion Timestep Tokens Making LLaMA SEE and Draw with SEED Tokenizer

Reference 23

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:48:03.209548Z digest=sha256:a145d511c703cb686b5c7d57d2bfcc90e19aeadecb66d381fc9ec50a10d898b5

Observation e3e905b6-e91b-407b-91d8-2cccb9e2a468 · inbound

X-Fusion: Introducing New Modality to Frozen Large Language Models cites this paper.

X-Fusion: Introducing New Modality to Frozen Large Language Models Making LLaMA SEE and Draw with SEED Tokenizer

Reference 41

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no resolver link, observed 2026-08-16T05:18:15.201350Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:18:15.201350Z digest=sha256:e7de8275e17b16dcedb364c91135ccbe4472f186a3b7b8a9aa3063289c735fd4

Observation e25a5b84-4a3e-43ed-ba33-4c57a1d4e3e7 · inbound

TokLIP: Marry Visual Tokens to CLIP for Multimodal Comprehension and Generation cites this paper.

TokLIP: Marry Visual Tokens to CLIP for Multimodal Comprehension and Generation Making LLaMA SEE and Draw with SEED Tokenizer

Reference 26

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no resolver link, observed 2026-08-15T23:09:10.714439Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T23:09:10.714439Z digest=sha256:8562c40c5eb0fd0498573c50a75b6b646d15ec62940756437fb2a94dffdb997b

Observation 644111f9-966a-420c-b84c-94abc0b65c1e · inbound

Mogao: An Omni Foundation Model for Interleaved Multi-Modal Generation cites this paper.

Mogao: An Omni Foundation Model for Interleaved Multi-Modal Generation Making LLaMA SEE and Draw with SEED Tokenizer

Reference 22

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verified exact
arxiv_id, observed 2026-05-17T07:24:04.826174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-17T07:24:04.460276Z digest=sha256:91d3cf088f5f70d1d68956e6acad9c050129eac6e29b363ddb883db10871bf4a

Observation a2f59b40-9cad-4d1e-be7b-b3962e0fecb5 · inbound

Slot-MLLM: Object-Centric Visual Tokenization for Multimodal LLM cites this paper.

Slot-MLLM: Object-Centric Visual Tokenization for Multimodal LLM Making LLaMA SEE and Draw with SEED Tokenizer

Reference 14

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verified exact
arxiv_id, observed 2026-05-22T02:10:56.112562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-22T02:06:35.204166Z digest=sha256:bd533dc8fa827b31b37741051c53957e3642ee0ed1100b99e5febd83294ebea2

Observation 11016a83-596a-4a6e-bee4-aad63d187c44 · inbound

FUDOKI: Discrete Flow-based Unified Understanding and Generation via Kinetic-Optimal Velocities cites this paper.

FUDOKI: Discrete Flow-based Unified Understanding and Generation via Kinetic-Optimal Velocities Making LLaMA SEE and Draw with SEED Tokenizer

Reference 17

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:04:54.366169Z digest=sha256:28651506bf682ac2bcdafa05f66c1b2c87883ff68bd4c266338fa850489658ee

Observation cda3c8cc-5278-4f38-8deb-67b5537f8dae · inbound

Video-Holmes: Can MLLM Think Like Holmes for Complex Video Reasoning? cites this paper.

Video-Holmes: Can MLLM Think Like Holmes for Complex Video Reasoning? Making LLaMA SEE and Draw with SEED Tokenizer

Reference 21

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arxiv_id, observed 2026-05-17T05:40:55.995559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-17T05:40:55.944288Z digest=sha256:2234d9688f3f8322537c710ffc5aafedaa1807ab4abb310a41c6dd5bec38c8c6

Observation 24ac5404-754b-4718-b8f3-717d54631f7d · inbound

VideoREPA: Learning Physics for Video Generation through Relational Alignment with Foundation Models cites this paper.

VideoREPA: Learning Physics for Video Generation through Relational Alignment with Foundation Models Making LLaMA SEE and Draw with SEED Tokenizer

Reference 14

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:45:06.140712Z digest=sha256:9cf86713dba7c7a028976a27cc1b2ed9973f21fc5917001cec736627bbb52b98

Observation d3487a38-dc62-4b86-bca4-cf93de2dbdb1 · inbound

FocusDiff: Advancing Fine-Grained Text-Image Alignment for Autoregressive Visual Generation through RL cites this paper.

FocusDiff: Advancing Fine-Grained Text-Image Alignment for Autoregressive Visual Generation through RL Making LLaMA SEE and Draw with SEED Tokenizer

Reference 11

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no resolver link, observed 2026-08-07T10:23:12.502691Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:23:12.502691Z digest=sha256:adab50222069aaf7a7ff5bca57d9a724f429fa1d5f791f064113fd6ec582c1c8

Observation 87f0b4e0-468c-4b1e-8e6f-67262c9afe7f · inbound

Vision as a Dialect: Unifying Visual Understanding and Generation via Text-Aligned Representations cites this paper.

Vision as a Dialect: Unifying Visual Understanding and Generation via Text-Aligned Representations Making LLaMA SEE and Draw with SEED Tokenizer

Reference 20

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no resolver link, observed 2026-08-15T18:46:10.655820Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T18:46:10.655820Z digest=sha256:528a5f7cd0232edd2d2e9e5121416a34ddf85b89cc8b33d54a35b9a84509becb

Observation 05fce673-ba5d-4366-9c62-e29eeb872097 · inbound

IGD: Instructional Graphic Design with Multimodal Layer Generation cites this paper.

IGD: Instructional Graphic Design with Multimodal Layer Generation Making LLaMA SEE and Draw with SEED Tokenizer

Reference 18

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no resolver link, observed 2026-08-06T17:47:41.979247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:47:41.979247Z digest=sha256:e3d674e632acc23402fdc2a6573c185afeade91bd93438c43da7afbed755f4fd

Observation 0035c43f-fdc4-476a-b219-aceb791b2b21 · inbound

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models cites this paper.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Making LLaMA SEE and Draw with SEED Tokenizer

Reference 20

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no resolver link, observed 2026-08-05T22:35:50.726451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T22:35:50.726451Z digest=sha256:fb2b2082d6fd4b4e7265f783cda12ea6042dac14b63b8a80ec7ca7991277976e

Observation a97b6fa1-1915-4ead-9d96-76dff61ce866 · inbound

TBAC-UniImage: Unified Understanding and Generation by Ladder-Side Diffusion Tuning cites this paper.

TBAC-UniImage: Unified Understanding and Generation by Ladder-Side Diffusion Tuning Making LLaMA SEE and Draw with SEED Tokenizer

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-05T21:41:11.885994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T21:41:11.885994Z digest=sha256:734e70d2f0e574405551dc2ac1ca8d5193848042bbc5176202a1505ba91901cc

Observation 27c96fe5-b1de-46d0-b399-c6852e091228 · inbound

Sample-efficient Integration of New Modalities into Large Language Models cites this paper.

Sample-efficient Integration of New Modalities into Large Language Models Making LLaMA SEE and Draw with SEED Tokenizer

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-05T06:00:27.412176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T06:00:27.412176Z digest=sha256:2f7f7f1567198d5b9e9404141e179a04b4941eb89fed39f74717c079ab46f156

Observation 94b9f1ac-59f5-48b0-99f9-c221d39aee55 · inbound

UniECG: Understanding and Generating ECG in One Unified Model cites this paper.

UniECG: Understanding and Generating ECG in One Unified Model Making LLaMA SEE and Draw with SEED Tokenizer

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-04T15:45:10.086435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T15:45:10.086435Z digest=sha256:3d8186b776f8091ccab060beb7558f00fb0f78c4f2e70ac3267e558a6658d851

Observation 05cd402b-5186-48e7-9b3e-bded583af4b3 · inbound

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation cites this paper.

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation Making LLaMA SEE and Draw with SEED Tokenizer

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-03T03:57:31.361767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T03:57:31.361767Z digest=sha256:4b398586fb73842efcaed0d032381b93affc9842d6825f5689595671e817e460

Observation d778adb8-b3db-4394-b248-abdbd10180b3 · inbound

End-to-End Autoregressive Image Generation with 1D Semantic Tokenizer cites this paper.

End-to-End Autoregressive Image Generation with 1D Semantic Tokenizer Making LLaMA SEE and Draw with SEED Tokenizer

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-11T15:36:05.964626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-09T19:41:03.302303Z digest=sha256:b03d5cc8c61447af24d66534152889b485f0b25fd66ac4409ad821dbb145af03

Observation f43ffe58-d574-4613-868b-02f1b8e1feb9 · inbound

SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture cites this paper.

SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture Making LLaMA SEE and Draw with SEED Tokenizer

Reference 39

Resolution
verified exact
arxiv_id, observed 2026-05-13T05:17:18.821835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-13T05:12:37.339084Z digest=sha256:ffdad80950e167016e9be689d2d85da4830f95c10619737a5cf6e79375215514

Observation 186a5098-c54c-4649-8fac-6b9f4cbc284c · inbound

When Recovery Matters: The Blind Spot of Surrogate Privacy in MLLM Editing cites this paper.

When Recovery Matters: The Blind Spot of Surrogate Privacy in MLLM Editing Making LLaMA SEE and Draw with SEED Tokenizer

Reference 15

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T17:07:13.122612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-06-27T22:08:57.792229Z digest=sha256:cd2fe0d2c5967e97ff251aacaafc5b62d77bbeb85d7e97d2a3138275d0794f17

Observation 7805d954-f801-4294-a566-f745f9568dd5 · inbound

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers cites this paper.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Making LLaMA SEE and Draw with SEED Tokenizer

Reference 60

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T14:38:28.919623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-06-27T07:01:07.362430Z digest=sha256:2fe67e4b1af0cbc56306d7cb12228e21ff15cf07d978f309d83b356b77caed11

Observation 8a32fd32-f805-420a-a075-5372a1a9d8d8 · inbound

InterleaveThinker: Reinforcing Agentic Interleaved Generation cites this paper.

InterleaveThinker: Reinforcing Agentic Interleaved Generation Making LLaMA SEE and Draw with SEED Tokenizer

Reference 53

Resolution
verified exact
arxiv_id, observed 2026-07-03T15:08:32.891323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-06-27T06:42:34.126336Z digest=sha256:c00a1b3c0bba95eb86be770e38de33395602a3f7d4ff9037f0c15d3f54cdb902

Observation f42d80b3-3afe-45bd-8138-7db669b41d8f · inbound

SingGuard: A Policy-Adaptive Multimodal LLM Guardrail with Dynamic Reasoning cites this paper.

SingGuard: A Policy-Adaptive Multimodal LLM Guardrail with Dynamic Reasoning Making LLaMA SEE and Draw with SEED Tokenizer

Reference 253

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T09:59:44.766332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-06-26T09:19:50.623741Z digest=sha256:0f5a3952b8d3f1d80dde28a3a87add974efce55ca91ca81c0d546c7c2e51262e

Observation 1208ccc4-baff-48ac-b94c-2a2fb21b9c1a · inbound

SingGuard: A Policy-Adaptive Multimodal LLM Guardrail with Dynamic Reasoning cites this paper.

SingGuard: A Policy-Adaptive Multimodal LLM Guardrail with Dynamic Reasoning Making LLaMA SEE and Draw with SEED Tokenizer

Reference 252

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T18:55:59.718833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-06-29T01:18:19.195007Z digest=sha256:9e8ed30ae28df07868773991d749c1b3f0d4737fca3cc1dc079b8d94a5f63a9c

Observation a4de679e-c1e1-4667-ac3b-b459f967aebe · inbound

Illuminating Unified Multimodal Model for Free-form Interleaved Text-Image Generation cites this paper.

Illuminating Unified Multimodal Model for Free-form Interleaved Text-Image Generation Making LLaMA SEE and Draw with SEED Tokenizer

Reference 18

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T06:04:20.951281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-06-30T06:04:07.327934Z digest=sha256:03aa4a9048e0d2b668fafe70ffa5e49fc18853ec46bdf67b3ca0ae63635b80be

Observation 7c8cef05-b7ad-465a-a62c-34c53062e132 · inbound

ProLaViT: Learning Progressive Latent Visual Thoughts in Structured Latent Space cites this paper.

ProLaViT: Learning Progressive Latent Visual Thoughts in Structured Latent Space Making LLaMA SEE and Draw with SEED Tokenizer

Reference 8

Resolution
unresolved
no resolver link, observed 2026-07-12T06:13:16.894125Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T06:13:16.894125Z digest=sha256:8e19bc4a86b51c40b2dc7440f1f084f43794f49eae33196104b9c44e0fb8246a

Observation a434d4f3-337d-46d7-978f-8e0dea4dad31 · inbound

MentalThink: Shaping Thoughts in Mental SVG World cites this paper.

MentalThink: Shaping Thoughts in Mental SVG World Making LLaMA SEE and Draw with SEED Tokenizer

Reference 130

Resolution
unresolved
no resolver link, observed 2026-07-12T01:50:59.184754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T01:50:59.184754Z digest=sha256:ced585d7dd1bd4be5b2a61502a6fd0a4b0246bd771d170ef32bfeadb879e7fc6

Observation 59d22b61-4684-47c4-b9ba-0d776255867f · inbound

Tree-of-Thoughts Reasoning for Text-to-Image In-Context Learning cites this paper.

Tree-of-Thoughts Reasoning for Text-to-Image In-Context Learning Making LLaMA SEE and Draw with SEED Tokenizer

Reference 25

Resolution
verified exact
local_arxiv, observed 2026-07-09T20:16:29.589913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-07-09T19:57:27.683657Z digest=sha256:ed2b10e08a80d473b675067f3180c59753d3a9dbe19fdd30d18ad08db9ae4614

Observation 7ff494b4-408f-4432-80d2-6df5a068c518 · inbound

Twins: Learn to Predict Unified Representations with Focal Loss cites this paper.

Twins: Learn to Predict Unified Representations with Focal Loss Making LLaMA SEE and Draw with SEED Tokenizer

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-01T04:29:48.538845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T04:29:48.538845Z digest=sha256:ded4ad48bb2f7b78183c64e8bb61882367075963baf370b8fb5c6a7013855522