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Paper Citation Record · LEDGER

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation

As of 5 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 3 inbound Pith citation observations for arXiv:2602.06442.

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

pith.paper-citation-record.v1
2602.06442 v2

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T03:57:32.398916Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

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

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-21T07:56:34.034047Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T07:59:50.522621Z

Reference resolution

29 of 29 outbound references displayed

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External citation measurements

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Outbound references

Observation 2907d4be-e570-4d53-9ef7-a282d5ab61ae · outbound

This paper cites Qwen2.5-VL Technical Report.

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation Qwen2.5-VL Technical Report

Reference 1

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source=pdf_text observed=2026-08-03T03:57:30.811900Z digest=sha256:a9c9e1d8475857d5fc9de9c29209c0107c88bd6e7d617c4d3797d72dd880a08f

Observation ef345832-07bb-482c-a011-3bf70c7ad677 · outbound

This paper cites BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset.

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset

Reference 3

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source=pdf_text observed=2026-08-03T03:57:31.101636Z digest=sha256:bd008c7c3f5ebd75ca395e2317defec341605e24d5053b955520d9c40c4565b8

Observation 20f3229a-bad8-4d1f-be56-23be805287e4 · outbound

This paper cites Emerging Properties in Unified Multimodal Pretraining.

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation Emerging Properties in Unified Multimodal Pretraining

Reference 4

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source=pdf_text observed=2026-08-03T03:57:31.225142Z digest=sha256:68a7eb2b07cf65f5c5b99f94d77be5d0d23f7f22eb49768bcd638ecd2dadd062

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

This paper cites Making LLaMA SEE and Draw with SEED Tokenizer.

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

Reference 6

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source=pdf_text observed=2026-08-03T03:57:31.361767Z digest=sha256:90be38b08a3e878d429db356e3c8fdc8b701e26254a4fc64ed873c8d8edef4d1

Observation ef0865f9-b12b-490f-bff0-48fe7b014036 · outbound

This paper cites GPT-4o System Card.

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation GPT-4o System Card

Reference 8

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source=pdf_text observed=2026-08-03T03:57:31.571665Z digest=sha256:e85e76a92595a85d48198031660a2f6ef52226f5e7c138269576f1b01a24e04e

Observation 7e475d0b-2024-49f8-83b7-f4f35ba4c980 · outbound

This paper cites Kimi K2: Open Agentic Intelligence.

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation Kimi K2: Open Agentic Intelligence

Reference 9

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source=pdf_text observed=2026-08-03T03:57:31.603641Z digest=sha256:6291bed2650819718e1fd49335f2802d1c2b2b413ab739ac6f66d60b3f00d6a3

Observation 150451ff-f20c-4385-8020-d5fd9b445ce3 · outbound

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

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation Mogao: An Omni Foundation Model for Interleaved Multi-Modal Generation

Reference 10

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source=pdf_text observed=2026-08-03T03:57:31.714604Z digest=sha256:ff8a0b1449f1fa4c097a2613afdeffbdc48919351576a55b913ffeae49a91b44

Observation 360fbe7a-fbaa-41c4-917b-6ea53d0d5214 · outbound

This paper cites UniWorld-V1: High-Resolution Semantic Encoders for Unified Visual Understanding and Generation.

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation UniWorld-V1: High-Resolution Semantic Encoders for Unified Visual Understanding and Generation

Reference 11

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source=pdf_text observed=2026-08-03T03:57:31.746659Z digest=sha256:5b11d452c0426f35b24b86c99b767f4fd562008ea2f03a0ec81ec2defcba23e2

Observation b310d75e-d630-4110-aadd-ba18a0631ed2 · outbound

This paper cites Flow Matching for Generative Modeling.

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation Flow Matching for Generative Modeling

Reference 12

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source=pdf_text observed=2026-08-03T03:57:31.778476Z digest=sha256:c3bbdf665eb32f0ce377ae71d5b02828e265f264296b660ea908835340d29a02

Observation e45b428e-7aef-49e4-8149-7bb639a84684 · outbound

This paper cites Transfer between Modalities with MetaQueries.

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation Transfer between Modalities with MetaQueries

Reference 15

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source=pdf_text observed=2026-08-03T03:57:31.903060Z digest=sha256:37fe744b868f772cf5f2b3cad909954e59c35dc6ec0d061c8fe8784acc298d51

Observation 6a850956-3c6d-40e9-9cd6-ff6b79593112 · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 16

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source=pdf_text observed=2026-08-03T03:57:31.963242Z digest=sha256:9f8b3b4b345910b87adbcc6399b61338b520963acd7d8b70fe1674313c84b022

Observation abeb05d0-d4e9-48ea-8191-0be8fb91b434 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 17

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source=pdf_text observed=2026-08-03T03:57:32.004110Z digest=sha256:ac02203eb8252c7d1ba12db838af34a3e9de3e7c41f63fce42a5ff7285610b2c

Observation 8854c87b-93c1-45fc-926c-f848d8caded8 · outbound

This paper cites LMFusion: Adapting Pretrained Language Models for Multimodal Generation.

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation LMFusion: Adapting Pretrained Language Models for Multimodal Generation

Reference 18

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source=pdf_text observed=2026-08-03T03:57:32.062558Z digest=sha256:bd66505a7d007f62d3092d9e637fbc476cf35b18e13c5a7615367ddc42c209be

Observation 70a2320a-4766-4f16-bc25-36940e3df089 · outbound

This paper cites Emu: Generative Pretraining in Multimodality.

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation Emu: Generative Pretraining in Multimodality

Reference 19

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source=pdf_text observed=2026-08-03T03:57:32.091846Z digest=sha256:a8e4cf4ba9e0a6fc3e91ec42bc6f65325193139cf66e3480335c93e4eade835c

Observation 2c8263df-b1d4-4b31-b4ae-6c37c2b39d35 · outbound

This paper cites MetaMorph: Multimodal Understanding and Generation via Instruction Tuning.

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation MetaMorph: Multimodal Understanding and Generation via Instruction Tuning

Reference 20

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source=pdf_text observed=2026-08-03T03:57:32.139758Z digest=sha256:a5a51f3af5b502f14234515ba89ae15138d6dc3e5d96c64b7220b6a64edae540

Observation 2fe808b7-d894-4cdd-a37e-c15a761e98e2 · outbound

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

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation ILLUME: Illuminating Your LLMs to See, Draw, and Self-Enhance

Reference 21

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source=pdf_text observed=2026-08-03T03:57:32.187422Z digest=sha256:79da79977873e1b8ca4d4d4e07ea038074825bf7bc8974b3c7b971ab46af7c10

Observation db5753a8-095a-4b55-b1ee-ccbdbc7c3197 · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 22

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source=pdf_text observed=2026-08-03T03:57:32.251379Z digest=sha256:092389383b089bf741d380f24e49d82cd45785f0d5c591bba95f6742705515bc

Observation e2361bb0-b0ae-485e-8c1b-78343abbc5aa · outbound

This paper cites Show-o: One Single Transformer to Unify Multimodal Understanding and Generation.

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation Show-o: One Single Transformer to Unify Multimodal Understanding and Generation

Reference 23

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source=pdf_text observed=2026-08-03T03:57:32.281430Z digest=sha256:96c05af2401c993caf36bdd8737d79a92662f95b9b9694b75d0125f7f147b3a3

Observation f2053d36-e18c-4b43-9566-42a2ccfc4c53 · outbound

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

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation MUSE-VL: Modeling Unified VLM through Semantic Discrete Encoding

Reference 24

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source=pdf_text observed=2026-08-03T03:57:32.313692Z digest=sha256:fc0f59c78b7889831d67178d10f6b30347bd29b5cbc03cf5a23b494e677a7de2

Observation ca0abd6c-d774-40a8-b0ef-2d5656a2f60d · outbound

This paper cites Qwen2.5 Technical Report.

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation Qwen2.5 Technical Report

Reference 25

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source=pdf_text observed=2026-08-03T03:57:32.341809Z digest=sha256:52c77456e0e9e76410b0d98313de52da1dbb64a1cd7cf993699ffcca50ba476d

Observation 3c3f005f-105c-4236-a0e2-1d02d5f23d12 · outbound

This paper cites MMaDA: Multimodal Large Diffusion Language Models.

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation MMaDA: Multimodal Large Diffusion Language Models

Reference 26

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source=pdf_text observed=2026-08-03T03:57:32.391604Z digest=sha256:cc95be29d9f30a5879452ffc4896d1a94b3bb709eea5aaa240376c1bc1a714f7

Observation 6eb90171-2c19-491d-a7a4-8f669af613f5 · outbound

This paper cites MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities.

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities

Reference 27

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source=pdf_text observed=2026-08-03T03:57:32.394391Z digest=sha256:a5875f441dc9ecb39e9be4a009a6f00c9b227be21e48badbb82104de367a6fb9

Observation be1acf60-4fa2-4e23-b65b-843ccb443109 · outbound

This paper cites PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel.

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 28

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source=pdf_text observed=2026-08-03T03:57:32.396743Z digest=sha256:6c4e3b9be3d3dc232d2c882a793feeede2e8bd356d03f2b5384f031e02b0e990

Observation 83f8ce4c-3982-4480-99c2-50b04e0364fc · outbound

This paper cites Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model.

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model

Reference 29

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source=pdf_text observed=2026-08-03T03:57:32.398916Z digest=sha256:e942606cc5819184e9d661d92703d05b0ec8d6287be7fa0baaaac3f5bb2fd8f3

Observation 68ba6f10-bb0e-4033-aa1f-93991c4745d0 · outbound

This paper cites MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts.

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts

Reference 2017

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source=pdf_text observed=2026-08-03T03:57:31.841665Z digest=sha256:5ced532bea3098a50b6ea135ffb8797a8ea82e94be84d278ca87311310551f7c

Observation 98cecb5d-a55f-4bae-8366-16434ed774b6 · outbound

This paper cites World Model on Million-Length Video And Language With Blockwise RingAttention.

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation World Model on Million-Length Video And Language With Blockwise RingAttention

Reference 2022

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source=pdf_text observed=2026-08-03T03:57:31.810711Z digest=sha256:b5cfde13f1e2c6bf9b055fa310158920c91b62d635e043e6090e603efad6a629

Observation f47e244c-4af5-41cd-940d-71392046a618 · outbound

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

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation

Reference 2023

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source=pdf_text observed=2026-08-03T03:57:31.509265Z digest=sha256:43ef0d2b415f402d0ffa674230e8a3fcebba770b7c68ce59b8de38512d830b4c

Observation 1dd2ceb7-962a-4614-94e6-c625e89e68c6 · outbound

This paper cites Chameleon: Mixed-Modal Early-Fusion Foundation Models.

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation Chameleon: Mixed-Modal Early-Fusion Foundation Models

Reference 2024

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source=pdf_text observed=2026-08-03T03:57:30.931086Z digest=sha256:b1f48f261cbdb88158d07dfb499fa6e6eddc9dc79da8d92192ec1a5fafc452ab

Observation 71627a80-7ae3-4956-b9e9-a5e4cd7a5fb8 · outbound

This paper cites DreamLLM: Synergistic Multimodal Comprehension and Creation.

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation DreamLLM: Synergistic Multimodal Comprehension and Creation

Reference 2025

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source=pdf_text observed=2026-08-03T03:57:31.307713Z digest=sha256:378c50ee7325be9ac62726719d1e7fe54d70d22f74112e4292bae6e559e099c6

Pith citing papers

Observation 38fdb81e-6299-4b00-bdb2-00d92e0fe034 · inbound

Meta-CoT: Enhancing Granularity and Generalization in Image Editing cites this paper.

Meta-CoT: Enhancing Granularity and Generalization in Image Editing ChatUMM: Robust Context Tracking for Conversational Interleaved Generation

Reference 7

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arxiv_id, observed 2026-06-02T04:04:28.060339Z

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

source=pdf_text observed=2026-05-08T04:30:28.636915Z digest=sha256:76d326e15adc11031182cb08497374cdae6c0b97e606ff191f9c454fc387d642

Observation 0568b999-1e95-4f48-aee1-1049582a42c6 · inbound

Lance: Unified Multimodal Modeling by Multi-Task Synergy cites this paper.

Lance: Unified Multimodal Modeling by Multi-Task Synergy ChatUMM: Robust Context Tracking for Conversational Interleaved Generation

Reference 21

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source=pdf_text observed=2026-05-20T11:46:52.658984Z digest=sha256:4341bbb8d04011609736ae80a02d127fbc2fa41606afb103fb7a27bd05dd8604

Observation 02508724-e637-43cf-b990-f1efea13a798 · inbound

Lance: Unified Multimodal Modeling by Multi-Task Synergy cites this paper.

Lance: Unified Multimodal Modeling by Multi-Task Synergy ChatUMM: Robust Context Tracking for Conversational Interleaved Generation

Reference 21

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arxiv_id, observed 2026-06-02T04:04:28.060339Z

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source=pdf_text observed=2026-05-21T07:56:34.034047Z digest=sha256:d38ceca188cb23366db2d0eec85f92d2d027d52a36e654d59489949c05dceaa4