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Paper Citation Record · LEDGER

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles

As of 9 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2506.21839.

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

pith.paper-citation-record.v1
2506.21839 v2

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:22:37.975270Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

30 of 30 outbound references displayed

  • verified exact3
  • verified fuzzy21
  • unresolved6
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 946c35a8-fa94-49f1-bb15-68d17350fc55 · outbound

This paper cites Puzzlevqa: Diagnosing mul- timodal reasoning challenges of language models with ab- stract visual patterns.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles Puzzlevqa: Diagnosing mul- timodal reasoning challenges of language models with ab- stract visual patterns

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:22:41.741399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T22:22:34.323216Z digest=sha256:906b7aae0e6d49c90b78f91a94f70b635be2776be9cf06ce49243d44d65ace8f

Observation 3f773854-19c6-4bb2-b6a9-e2399a5bd75b · outbound

This paper cites Gemini 1.5 technical report, 2024.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles Gemini 1.5 technical report, 2024

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:22:41.615461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T22:22:34.454500Z digest=sha256:c1b38e1e4513575fa5dec081881960cb6849718c28ee9eb632a0f3409eceac35

Observation 543b54f6-69ea-43ba-b6aa-2af615d3b072 · outbound

This paper cites Black-box Prompt Learning for Pre-trained Language Models.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles Black-box Prompt Learning for Pre-trained Language Models

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-06T22:22:34.503232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:22:34.503232Z digest=sha256:ac933e38987057e63abf26f36ed2a30cba928025d73b0b73a65db55857e57fb8

Observation e1010381-2bc7-4151-8eac-874dc57a0c77 · outbound

This paper cites Puzzles: A benchmark for neural algorithmic reasoning.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles Puzzles: A benchmark for neural algorithmic reasoning

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:22:41.495500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T22:22:34.600900Z digest=sha256:2f4275d2c7f548ccd68478d2755ca43735531ab1b4717964460e908ac006c838

Observation c53b785b-fa06-43ea-a7a7-09cf027523e3 · outbound

This paper cites Large language models empowered agent-based modeling and simulation: A survey and perspectives.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles Large language models empowered agent-based modeling and simulation: A survey and perspectives

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:22:41.376013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T22:22:36.589007Z digest=sha256:43869f4dd94a679879bbb2eba47c0fde403771bcfb1a16678bd397fab5cf2c2a

Observation a071a0ac-c6a0-4450-8c9b-fcb461f83396 · outbound

This paper cites Chain-of-agents: Large language mod- els collaborating on long context tasks.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles Chain-of-agents: Large language mod- els collaborating on long context tasks

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:22:41.268728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T22:22:36.891806Z digest=sha256:22894d14773edb8dccf28d5b1512737275348de1d4b3d6eb062331f275455e0d

Observation 62cdc3a2-1578-44be-8e51-a5f752f1b640 · outbound

This paper cites Optimizing prompts for text-to-image generation.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles Optimizing prompts for text-to-image generation

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:22:41.156819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T22:22:36.947602Z digest=sha256:4912e6c7f62d24512898a02f3fae3faea5c7a38ac74296c3190b3db73a2e6627

Observation f08b0988-97f1-4fd9-81e7-fc3c1c425313 · outbound

This paper cites Prompt-to-prompt image editing with cross-attention control.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles Prompt-to-prompt image editing with cross-attention control

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:22:41.056032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T22:22:37.064332Z digest=sha256:9c4a6dfcf9d99307b24709232b48cb67782439022eea67f9d986dec04fb75335

Observation 2b78d455-ec05-46b2-9a0f-f2aaa46940e0 · outbound

This paper cites Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks

Reference 9

Resolution
verified exact
local_arxiv, observed 2026-08-06T22:22:38.404192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T22:22:37.159277Z digest=sha256:16086fd579fe50c09ac1df67a3c5991db1faebd416109500d2dd2d1d5d6bb73b

Observation 1f20d7ac-0d98-4f5b-9c6a-a79d5f3da5ad · outbound

This paper cites The power of scale for parameter-efficient prompt tuning.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles The power of scale for parameter-efficient prompt tuning

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:22:40.842423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T22:22:37.241718Z digest=sha256:7882031d56fbb63c32ff8bf4a09b22cdee54576ae3bf7a3d9f961c001db2b69a

Observation f5a21a5d-befc-42b6-9b48-98317ace5895 · outbound

This paper cites Mccd: Multi-agent collaboration-based compositional diffusion for complex text-to-image genera- tion.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles Mccd: Multi-agent collaboration-based compositional diffusion for complex text-to-image genera- tion

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:22:40.672279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T22:22:37.288845Z digest=sha256:4319c1d6bbaaa5cd433affcce081515174ddffd73ce4bcac584569dc042a5a59

Observation 9e63d4a6-a5aa-4e97-a72a-9fdb815a8966 · outbound

This paper cites Hunyuan-dit: A powerful multi-resolution diffusion trans- former with fine-grained chinese understanding, 2024.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles Hunyuan-dit: A powerful multi-resolution diffusion trans- former with fine-grained chinese understanding, 2024

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-06T22:22:37.322981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:22:37.322981Z digest=sha256:4ff08e79bedcaf011dd653b1b7d6d93ddb5dc3942a4dc6ba843d5213dd58bc15

Observation 0fe8d9f7-f33c-41be-a81f-d3ea83f8e89b · outbound

This paper cites Compositional visual generation with composable diffusion models.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles Compositional visual generation with composable diffusion models

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:22:40.525871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T22:22:37.355272Z digest=sha256:13d8ed4cf625c7522243c2f81ad8d99018ca168294ccd8382aa24695e6e10e65

Observation a36997a6-d648-4909-bc5d-697ff35e30ff · outbound

This paper cites Dynamic prompt optimizing for text- to-image generation.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles Dynamic prompt optimizing for text- to-image generation

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:22:40.350748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T22:22:37.399620Z digest=sha256:ab48151612d0fcecff7bf70ca1464bfb4e27f3d6987c48ca034d042d6119c7c1

Observation c43521dd-ce42-4182-97de-fc58a85cce2a · outbound

This paper cites Gpt-4o technical report, 2024.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles Gpt-4o technical report, 2024

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:22:40.162623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T22:22:37.440356Z digest=sha256:8d0a8bdbfa8c123eade1e458aace8b97b038576260532692e174d59b831fbd30

Observation ad90beeb-3409-4110-9c4a-84325711f900 · outbound

This paper cites Training language models to follow instructions with human feed- back.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles Training language models to follow instructions with human feed- back

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:22:39.987802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T22:22:37.486365Z digest=sha256:685fe6d66e742960753a0d64b61ea0aa11decf8580583cdd0addaa13848c15f3

Observation 1913383c-0188-4ca0-b75b-88d8452474df · outbound

This paper cites Generative Agents: Interactive Simulacra of Human Behavior.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles Generative Agents: Interactive Simulacra of Human Behavior

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-06T22:22:37.533570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:22:37.533570Z digest=sha256:04ea6715a5af52cc1604acc5d712d8747483255e2da5cce170f2bca5bd00f9fc

Observation 3b1d43b9-a404-43f9-a7e7-e4fda9f92728 · outbound

This paper cites Solving puzzles with an ensemble of chain-of-thought prompts.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles Solving puzzles with an ensemble of chain-of-thought prompts

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:22:39.809420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T22:22:37.567394Z digest=sha256:99c3f4aedb7c156f0c603a5d62b7e402246797cd3dfc5ebc9eaf1a5c495e00e4

Observation c6c33d1c-dd97-4772-a09b-f2d3b93f99e8 · outbound

This paper cites Autoprompt: Eliciting knowl- edge from language models with automatically generated prompts.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles Autoprompt: Eliciting knowl- edge from language models with automatically generated prompts

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:22:39.616174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T22:22:37.596221Z digest=sha256:1a9e3c2b663f345658d888ccb27b279d89f0cab331b3a1f52c18a6b6e27fc868

Observation 9e7485a5-97c6-4e0e-a6d5-c455d18d171b · outbound

This paper cites Molecularity: a fast and efficient criterion for probing superconductivity.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles Molecularity: a fast and efficient criterion for probing superconductivity

Reference 20

Resolution
verified exact
local_arxiv, observed 2026-08-06T22:22:38.254624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T22:22:37.635390Z digest=sha256:986088f7ae3d897658750376d9574436c5a775d2cab91e555892cc8a535061c5

Observation 1db03cf4-b0ed-4ad3-bf56-03874186e5c9 · outbound

This paper cites Multi- modal llm as an agent for unified image generation and edit- ing.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles Multi- modal llm as an agent for unified image generation and edit- ing

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:22:39.453685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T22:22:37.658379Z digest=sha256:6e3f00e7b8ae11c3c6721dc4d2a963355cce6c5096c5123190274a1358217ef8

Observation 256d0496-01e3-45a9-85f4-655e941dbce7 · outbound

This paper cites How do mul- timodal large language models handle complex multimodal reasoning? placing them in an extensible escape game, 2025.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles How do mul- timodal large language models handle complex multimodal reasoning? placing them in an extensible escape game, 2025

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:22:39.243063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T22:22:37.696016Z digest=sha256:5f55caa1f0ed83f760684e5bbda6b5ba8c60a5fac8ae8d835e09c26580a8d311

Observation 938217b0-e3cb-4b72-8a0f-6d2ee026fb95 · outbound

This paper cites Universal prompt optimizer for safe text- to-image generation.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles Universal prompt optimizer for safe text- to-image generation

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:22:39.092792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T22:22:37.716300Z digest=sha256:65ca95b1ec1f3fd1982c07412c276302760c90e7a441f7e80fef86b06888f233

Observation 89956f12-ef58-444e-8931-0061b6584a64 · outbound

This paper cites Planning with multi-constraints via collaborative language agents.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles Planning with multi-constraints via collaborative language agents

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:22:38.932165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T22:22:37.744513Z digest=sha256:3ba97b811a1ede0daa758fd89d13f1b57a997d6a3f6927d0b936c520e1947f59

Observation de09dd1d-c85d-4b2f-ad64-3f8a49d63083 · outbound

This paper cites MM-StoryAgent: Immersive Narrated Storybook Video Generation with a Multi-Agent Paradigm across Text, Image and Audio.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles MM-StoryAgent: Immersive Narrated Storybook Video Generation with a Multi-Agent Paradigm across Text, Image and Audio

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-06T22:22:37.779984Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:22:37.779984Z digest=sha256:0cda960175edc38cd7f14aa714f34c89a223cdc11a8c22737dca2eee9416e844

Observation 38f709d8-3a45-402a-a049-1a51bc49f3f1 · outbound

This paper cites Long-CLIP: Unlocking the Long-Text Capability of CLIP.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles Long-CLIP: Unlocking the Long-Text Capability of CLIP

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-06T22:22:37.804567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:22:37.804567Z digest=sha256:0fddda46d45cbbf2c4ee4195c67ae49d254320f99387b214bc2b4a8b9c494ad5

Observation 915030c8-9e92-4eae-ab4a-740437d99f3d · outbound

This paper cites Reflective multi- agent collaboration based on large language models.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles Reflective multi- agent collaboration based on large language models

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:22:38.732618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T22:22:37.846755Z digest=sha256:4a03189d0ad8315da8e1605637214a21960e121ef1d8b526f1b69d5702109270

Observation 41218781-c19d-4ad5-b2aa-365c72b82a1b · outbound

This paper cites LightVA: Lightweight Visual Analytics with LLM Agent-Based Task Planning and Execution.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles LightVA: Lightweight Visual Analytics with LLM Agent-Based Task Planning and Execution

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-06T22:22:37.891458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:22:37.891458Z digest=sha256:507738e7f01ee4a4a6437560500d4135f21d900cf612ab752438555a7430b627

Observation 9c3fdd54-dd1b-4524-a283-3686e38e0ffd · outbound

This paper cites Migc: Multi-instance generation controller for text-to-image synthesis.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles Migc: Multi-instance generation controller for text-to-image synthesis

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:22:38.577181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T22:22:37.933399Z digest=sha256:1bb5c4162ad41dfd4eebdf7679297416848bafbe8718195e6448d1fa4f06adc5

Observation 017079d2-b27c-443c-86be-8eb4d6569c44 · outbound

This paper cites Constrained Proximal Policy Optimization.

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles Constrained Proximal Policy Optimization

Reference 30

Resolution
verified exact
local_arxiv, observed 2026-08-06T22:22:38.095282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T22:22:37.975270Z digest=sha256:a119f966a7a3ad5cc5f67806414e38b2058b934d63ecd4cf6b32ace70669ec90

Pith citing papers

No inbound Pith citation observations are available.