Pith. sign in

Paper Citation Record · LEDGER

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation

As of 17 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2505.02648.

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

pith.paper-citation-record.v1
2505.02648 v2

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:49:40.533898Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

45 of 45 outbound references displayed

  • verified exact0
  • verified fuzzy14
  • unresolved31
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 536e04f2-4d07-4849-81c4-4445beb33081 · outbound

This paper cites GPT-4 Technical Report.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation GPT-4 Technical Report

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.156099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.156099Z digest=sha256:070ffbe63770f67b25e4e2a65130e8a66385ad906b81c07637eca80f390c20db

Observation 8a1d20ff-6dbe-4616-a128-6e5522673e6b · outbound

This paper cites window”: “large, fills the room, offers a view of the peaceful outdoors.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation window”: “large, fills the room, offers a view of the peaceful outdoors

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:49:41.679853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T00:49:40.166363Z digest=sha256:a47cf9e05baad19e3e1e7df8007de72326c7ec1996c3b6f831a035e43f9b75a5

Observation 312f4eda-8400-4aa4-92e0-801e42f7d179 · outbound

This paper cites Attend-and-excite: Attention-based se- mantic guidance for text-to-image diffusion models.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Attend-and-excite: Attention-based se- mantic guidance for text-to-image diffusion models

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:49:41.654238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T00:49:40.173707Z digest=sha256:fd1d2672031e3e01d62ece9d1ca89cb64a0b76ea62142ec9c71c16ce957f3d62

Observation 41cbec74-e3cb-49ad-b1e2-4a0cfbf72cb3 · outbound

This paper cites PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.181104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.181104Z digest=sha256:b01058e34549f790d289d0c6967c71bd83d20520c836714131df36fffb7d34ba

Observation 5297fe55-ba3d-404c-868d-b4dcd4b96b06 · outbound

This paper cites LLaVA-Interactive: An All-in-One Demo for Image Chat, Segmentation, Generation and Editing.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation LLaVA-Interactive: An All-in-One Demo for Image Chat, Segmentation, Generation and Editing

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.189040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.189040Z digest=sha256:64d244cf5b57a9bc6229ff37dc4dbddb7b76d193163b8f88d75c403b4f670831

Observation 12ef18b1-f046-4e0b-b384-f7ba4614f147 · outbound

This paper cites Palm: Scaling language modeling with pathways.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Palm: Scaling language modeling with pathways

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.196948Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.196948Z digest=sha256:261a73406499f4091d19d478a621002e582801dac79fb18f80c9caceccf38f80

Observation 5fce98c3-d814-45ef-bd38-54b3e5ec8307 · outbound

This paper cites Diffusion models beat gans on image synthesis.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Diffusion models beat gans on image synthesis

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.203284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.203284Z digest=sha256:cbd7ed1e86b054616fdd7b0db73e60656a98b65f1f73dedb82050b3a4250081c

Observation 5f1c0bd0-25c4-4617-877a-4bff346be8af · outbound

This paper cites A woman in a pink shirt and jeans holds a white umbrella in the rain.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation A woman in a pink shirt and jeans holds a white umbrella in the rain

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:49:41.588895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T00:49:40.210341Z digest=sha256:8718bb5c5aaa943f12fd6acf885f9337d5a3ff6b3f58b6cfe62d30e521b47a52

Observation b3ecd4d9-9ff9-4581-aaf1-57909a8356af · outbound

This paper cites RealignDiff: Boosting Text-to-Image Diffusion Model with Coarse-to-fine Semantic Re-alignment.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation RealignDiff: Boosting Text-to-Image Diffusion Model with Coarse-to-fine Semantic Re-alignment

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.218386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.218386Z digest=sha256:f95c8fa6d0cd77df2aa4728d671f6d7d4a1f86de88aee0c5d5d9a2520bf5783d

Observation 0ef5c32b-cf50-45d1-b220-a6111a2c4a68 · outbound

This paper cites Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.226216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.226216Z digest=sha256:707ff9d5ee9145e32719da9a1e1383a4b1585999521f67bf4d2a30dd443a301a

Observation b6748258-551f-4f87-a8f0-59fc8d51ff61 · outbound

This paper cites Layoutgpt: Compositional visual plan- ning and generation with large language models.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Layoutgpt: Compositional visual plan- ning and generation with large language models

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.234783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.234783Z digest=sha256:b983b72eccbdea18976c6e2c1177539793e87fcb555f524eee724307f9d43012

Observation d59b63e1-8eaa-4bb1-9602-da9882228fb6 · outbound

This paper cites LLM Blueprint: Enabling Text-to-Image Generation with Complex and Detailed Prompts.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation LLM Blueprint: Enabling Text-to-Image Generation with Complex and Detailed Prompts

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.241163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.241163Z digest=sha256:9bce5350ca079ab1d044b6f1b93321aa96bc44d7097fb7197070caadcc06e09b

Observation 94682f17-b4f8-48bd-a178-93b8163395ba · outbound

This paper cites Denoising dif- fusion probabilistic models.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Denoising dif- fusion probabilistic models

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.250265Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.250265Z digest=sha256:8da0831e909c51ea139ec297a0f7b9f369fe63c77db37a392a45ab90c7e8463c

Observation cf6e5e12-6603-4d3e-b659-d2b72840d812 · outbound

This paper cites ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.258273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.258273Z digest=sha256:f92f47129322c6af0be7ee14103f5794cfb5249ab32599e65925d5aa1f50a4af

Observation 2ee3d702-a412-4570-a509-135a876bd871 · outbound

This paper cites T2i-compbench: A comprehensive bench- mark for open-world compositional text-to-image genera- tion.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation T2i-compbench: A comprehensive bench- mark for open-world compositional text-to-image genera- tion

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:49:41.524056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T00:49:40.267440Z digest=sha256:1f49b959f140ab7ed390ba021f625d10cfe5a8f20c7e4c657f19c37a75a702e9

Observation dc7308be-f579-4325-a0fe-c5eb67449f22 · outbound

This paper cites Aligning Text-to-Image Models using Human Feedback.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Aligning Text-to-Image Models using Human Feedback

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.275309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.275309Z digest=sha256:bd33292693f3ea954ce3a36c4a72dbbdde7b57a5cbc4d0478fb0b63305b92726

Observation 972a67c2-e7cd-48b4-a700-b98a4a35fad6 · outbound

This paper cites Parrot: Pareto-optimal multi-reward reinforce- ment learning framework for text-to-image generation.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Parrot: Pareto-optimal multi-reward reinforce- ment learning framework for text-to-image generation

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:49:41.497889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T00:49:40.287068Z digest=sha256:b160d63fc9d64c65a151c7ad338d9d460d6e3c478024f6c8bc395168080d5480

Observation 600d663c-b53e-41ce-91fe-72f4fc8ece24 · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.296688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.296688Z digest=sha256:6762eb067b394d12bebfc11c82d5585a45ca1a2d9926af18977609ade4657eef

Observation 2d32d33e-c1aa-45cd-bc46-66fa06848293 · outbound

This paper cites Gligen: Open-set grounded text-to-image generation.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Gligen: Open-set grounded text-to-image generation

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.305777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.305777Z digest=sha256:7149b18948cbe17517e3dedefb800065f06263380b140ba30afb0e9d1a0a86b8

Observation 1112b31f-e2a4-4a0a-815d-1dc42d069ae5 · outbound

This paper cites LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.316575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.316575Z digest=sha256:2313ba53065b4d77e3515219b6cd059f468e7ee3da835bbfb3c120801f507ea8

Observation e00e4295-3931-40eb-a8ec-36a5c8e342b7 · outbound

This paper cites Improved baselines with visual instruction tuning.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Improved baselines with visual instruction tuning

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:49:41.422788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T00:49:40.323469Z digest=sha256:9935269efae68cc02f3806d1ff431bd5c6b1d7a608761f8dc18510064fa19ca8

Observation e65ed4c8-ee80-4bc8-bc02-196c4f23ff6a · outbound

This paper cites LLM4GEN: Leveraging Semantic Representation of LLMs for Text-to-Image Generation.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation LLM4GEN: Leveraging Semantic Representation of LLMs for Text-to-Image Generation

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.329434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.329434Z digest=sha256:fafaf03fa3bfb716c4481df36709101c77949d173ead09c16a354a6a12269647

Observation 03ca9635-eb25-4c0e-ba71-74e9d49dd97e · outbound

This paper cites Compositional visual generation with composable diffusion models.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Compositional visual generation with composable diffusion models

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:49:41.396730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T00:49:40.335711Z digest=sha256:7b6180e88f5a3faed1b11619fa85023c2e40e0b29ab601680ec94fcc846212b4

Observation 22e247bb-7bcd-4c31-84e5-273852d87b81 · outbound

This paper cites T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.346005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.346005Z digest=sha256:29744c33eaaceaeb9f2a0eb1416efedcd8456a46b4058f4130c3ce3f073c3ad6

Observation 70060076-583c-4692-a10f-c66982826750 · outbound

This paper cites GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.354813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.354813Z digest=sha256:159fd7711a56e9b36e2919d3912de32c6e2a09a36232a9ac4ec320055e4d4895

Observation 11ec256e-6359-400a-9f67-6537797f4ead · outbound

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

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.362068Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.362068Z digest=sha256:5c0395f96d08734305ff77d38723a31656ffde971d8dc014f86f96ad0c4cf6ac

Observation 3ffed5f7-4e5b-4983-a154-80e706675b85 · outbound

This paper cites Layoutllm-t2i: Eliciting layout guidance from llm for text-to-image generation.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Layoutllm-t2i: Eliciting layout guidance from llm for text-to-image generation

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:49:41.350338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T00:49:40.368810Z digest=sha256:20ab854d1ab030dab3f4b37f93771a75d644d52672f25dfcadc43f13e187d074

Observation 4dc398ec-97f0-4a39-b23d-9bf5e4c134f7 · outbound

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

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Learning transferable visual models from natural language supervi- sion

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.381467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.381467Z digest=sha256:1f952bea1f7b88b2bfc33fc162847e3009b369c66f63d8deb73e0e421d1bebe4

Observation c8a30bf5-ae97-48b4-97e9-57743876e3bf · outbound

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

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.390117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.390117Z digest=sha256:162ce4513972566ff59e2f15a6f59f682ec330dcc79626148e6bfd97efb24a20

Observation e2eb31fe-efcf-4c8b-a07c-9f831ebbba8a · outbound

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

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation High-resolution image synthesis with latent diffusion models

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:49:41.301543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T00:49:40.404680Z digest=sha256:d4ae5b21a6f4325fcf5373b4a3ae4402f06a69cc341a60c1eed00f7f23a40d0a

Observation 4b5a6001-dccd-4828-a4ff-9d475dffadc9 · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Photorealistic text-to-image diffusion models with deep language understanding

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.417473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.417473Z digest=sha256:d78b1cd8028f24c3694510bf7641f8a244cc50aa06009cd93614fb26fd155b52

Observation 9b239947-c635-4f4d-82c6-aad15d775abd · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Deep unsupervised learning using nonequilibrium thermodynamics

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.426876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.426876Z digest=sha256:f90d925abf325ca2492afd86032d956a838802904cad3aa55992ccde5c38312d

Observation 8c5594ee-a889-4531-93f2-f698442c5d14 · outbound

This paper cites Dreamsync: Aligning text- to-image generation with image understanding feedback.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Dreamsync: Aligning text- to-image generation with image understanding feedback

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:49:41.244023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T00:49:40.432154Z digest=sha256:3c455128c3730cb416a466a109da52e451b6b43cf187c2c4cb270c6693e5c4e8

Observation 28e3a8ce-3b64-428c-a284-ed8f5c1c6493 · outbound

This paper cites Galactica: A Large Language Model for Science.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Galactica: A Large Language Model for Science

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.440341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.440341Z digest=sha256:9644d9c32c75df529b865b9f5011f7c38663b62c4b5a54830dc99a8474f8c0db

Observation 4aa0b6b4-3413-4b8e-bcef-94c9d4a08885 · outbound

This paper cites Boxdiff: Text-to-image synthesis with training-free box-constrained diffusion.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Boxdiff: Text-to-image synthesis with training-free box-constrained diffusion

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:49:41.210063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T00:49:40.448329Z digest=sha256:6e40ae6b36b8d7487db0b3169fd704cb03c3b92314a9c85f520f8800e2e604c4

Observation fe034dd2-80b6-4e10-be96-68f0743b4d54 · outbound

This paper cites Imagere- ward: Learning and evaluating human preferences for text- to-image generation.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Imagere- ward: Learning and evaluating human preferences for text- to-image generation

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.457557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.457557Z digest=sha256:7bec384c02715d461e468843e78c050d5b3412426faa666a05ef576c1e049150

Observation 665dd3d9-d71d-4ad6-866c-36f6c1232703 · outbound

This paper cites Baichuan 2: Open Large-scale Language Models.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Baichuan 2: Open Large-scale Language Models

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.468540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.468540Z digest=sha256:51ae9354327b4a816f54b06efe3a0eecc20775232efba7ac98cb30f4ecfc9e46

Observation 16c462ad-0b0d-4ccf-baa4-9a043736144a · outbound

This paper cites Diffusion models: A comprehensive survey of methods and applications.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Diffusion models: A comprehensive survey of methods and applications

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.477240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.477240Z digest=sha256:31327fdbaa1c8e8f5bd09d5b7f14c34d02266cfead4d790c89ac04bfbfb08132

Observation 078a6c36-fc47-49ba-b11b-3a5b546d0555 · outbound

This paper cites Mastering text-to-image dif- fusion: Recaptioning, planning, and generating with multi- modal llms.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Mastering text-to-image dif- fusion: Recaptioning, planning, and generating with multi- modal llms

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:49:41.150419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T00:49:40.483763Z digest=sha256:638a8c2778a3f76289e5df4718043210d7a306fae1393a1e41211b1182a49634

Observation 93f4bd02-342e-44e7-a7e8-19c0219156be · outbound

This paper cites Cross-modal con- textualized diffusion models for text-guided visual genera- tion and editing.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Cross-modal con- textualized diffusion models for text-guided visual genera- tion and editing

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:49:41.120523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T00:49:40.490051Z digest=sha256:5576c660fdf567a207cc035da931b25e0841805325f783f489d7bfc3d814b6c2

Observation ebcc0686-331d-4e5e-b081-8c386acf71f3 · outbound

This paper cites Reco: Region-controlled text-to-image genera- tion.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Reco: Region-controlled text-to-image genera- tion

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.499062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.499062Z digest=sha256:9e20fdc2f9aa20cb8cfbc838233f31c49f59aa477d2c6a3009c84f6fc697bd76

Observation 22bdfded-832b-4164-9ca8-aceb6aff4c1e · outbound

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

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Adding conditional control to text-to-image diffusion models

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.507176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.507176Z digest=sha256:23b3594bab0d48e98834052d69335ad39065fb164d0f3eb87235fb13945a177c

Observation fb7dbd99-3aaf-4743-97d9-5177824e8429 · outbound

This paper cites RealCompo: Balancing Realism and Compositionality Improves Text-to-Image Diffusion Models.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation RealCompo: Balancing Realism and Compositionality Improves Text-to-Image Diffusion Models

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.513664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.513664Z digest=sha256:421530edf433f40a2016441804949cfcb3578eb9090192f9bc8068ccc3042e7b

Observation 8f2a4c29-0255-4fc8-b08e-5dff52218725 · outbound

This paper cites Sur-adapter: Enhancing text-to-image pre-trained diffusion models with large language models.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation Sur-adapter: Enhancing text-to-image pre-trained diffusion models with large language models

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:49:41.050394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T00:49:40.526787Z digest=sha256:b136e712f9fbe49e38ac61bfb6026bb8ed89f36099422f85cd0606e602b4f2ad

Observation c416b4cd-95eb-477a-a420-d26ac6984cc8 · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-16T00:49:40.533898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:49:40.533898Z digest=sha256:0f3f2740e9cbc85f1ab3d2ede880c5df9e9b162a39661e9d1a9848a731b77898

Pith citing papers

No inbound Pith citation observations are available.