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Paper Citation Record · LEDGER

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios

As of 16 August 2026, this Paper Citation Record lists 87 of 87 outbound references and 3 inbound Pith citation observations for arXiv:2605.27589.

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

pith.paper-citation-record.v1
2605.27589 v1

Coverage vector

measured 87 of 87 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T18:23:22.987086Z

measured 90 of 90 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:23:57.855823Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T00:42:28.645519Z

Reference resolution

87 of 87 outbound references displayed

  • verified exact29
  • verified fuzzy0
  • unresolved53
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9817bffe-18d3-4a50-837e-55dd37ce36a5 · outbound

This paper cites Don’t just assume; look and answer: Overcoming priors for visual question answering.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Don’t just assume; look and answer: Overcoming priors for visual question answering

Reference 1

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:726603b3b79b256880c1290f97e0ef04542bacd4bcb41f997c08f4c0be6a982f

Observation c75cbfc4-9f54-4357-a9ff-a898761b3113 · outbound

This paper cites CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning

Reference 2

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arxiv_id, observed 2026-06-29T18:23:50.372993Z

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=2026-06-29T18:23:22.987086Z digest=sha256:5eadf726f61c7d49c1fe0cad0446607aab4d430b6a14ef92440ab644b31887f2

Observation 7cfd86f1-b242-4fd6-92ef-9e9ec2370a0f · outbound

This paper cites World Simulation with Video Foundation Models for Physical AI.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios World Simulation with Video Foundation Models for Physical AI

Reference 3

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local_arxiv, observed 2026-06-29T18:23:50.356445Z

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=2026-06-29T18:23:22.987086Z digest=sha256:63772fa1994b8f5d69a55a10a9353a617163b9416684855215c8bec891eac36a

Observation 9aab2aee-4dec-4410-9613-f22203df1d5c · outbound

This paper cites Diffusion for world modeling: Visual details matter in atari.Advances in Neural Information Processing Systems, 37:58757–58791, 2024.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Diffusion for world modeling: Visual details matter in atari.Advances in Neural Information Processing Systems, 37:58757–58791, 2024

Reference 4

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Source-reported events for the cited work

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:fb7c45f5e20a402b186b2bb5e8311d809feea4c3ef6f48807eca467b24324c0e

Observation d6b6c614-3f3b-4e95-be0e-e4f949ff3ca4 · outbound

This paper cites Craft: A benchmark for causal reasoning about forces and interactions.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Craft: A benchmark for causal reasoning about forces and interactions

Reference 5

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:2c0a6d6ed8545f014e98b87f720a3bd2fae7eecd3a663d461573943ccc84c01b

Observation 12a4c019-abaa-4e24-8929-201afb63a3e9 · outbound

This paper cites Video pretraining (vpt): Learning to act by watching unlabeled online videos.Advances in Neural Information Processing Systems, 35:24639–24654, 2022.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Video pretraining (vpt): Learning to act by watching unlabeled online videos.Advances in Neural Information Processing Systems, 35:24639–24654, 2022

Reference 6

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:80ace906750ecbcdbedb21749299eeebb39ab1abac4e485b9917cd02eb1b0518

Observation 3cc44c25-4f97-4711-981d-5fdb8aba95aa · outbound

This paper cites Phyre: A new benchmark for physical reasoning.Advances in Neural Information Processing Systems, 32, 2019.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Phyre: A new benchmark for physical reasoning.Advances in Neural Information Processing Systems, 32, 2019

Reference 7

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:893089aa262d3e4a1dc38fcdd225a31409cd0e36fa484f2ac96bb5a28b74dd97

Observation c91c5537-f295-4cd6-b394-257b7eeb2e4d · outbound

This paper cites VideoPhy: Evaluating Physical Commonsense for Video Generation.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios VideoPhy: Evaluating Physical Commonsense for Video Generation

Reference 8

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local_arxiv, observed 2026-06-29T18:23:50.440484Z

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

source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:ac653b01d3ff3682ce1b62efc137b2317d8766f130479c546fe295822f019ad7

Observation 2dc8fd89-4e71-47a0-8c81-0f5c24d823d7 · outbound

This paper cites VideoPhy-2: A Challenging Action-Centric Physical Commonsense Evaluation in Video Generation.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios VideoPhy-2: A Challenging Action-Centric Physical Commonsense Evaluation in Video Generation

Reference 9

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arxiv_id, observed 2026-06-29T18:23:50.403328Z

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:02cd24f12f0ef61dbcd923645394a75aafcaa9798a3483a5b86910f062e43dfd

Observation 38e11ad2-a35f-4070-b045-60fdc2c9e13d · outbound

This paper cites Revisiting Feature Prediction for Learning Visual Representations from Video.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Revisiting Feature Prediction for Learning Visual Representations from Video

Reference 10

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

source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:7f096246f8c52aba5cde068ce86b0c94645b582652c03b5709428ccd9030189a

Observation 46fb538a-0dc0-4ca4-a3ee-3eb9bcefe0d3 · outbound

This paper cites Using thematic analysis in psychology.Qualitative research in psychology, 3(2):77–101, 2006.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Using thematic analysis in psychology.Qualitative research in psychology, 3(2):77–101, 2006

Reference 11

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:1049b3ffc0583e1428f595f7e296371d2949c63e4413c52504736958d7ab2759

Observation 2f86de51-cc75-4292-82dc-5ceede2f945c · outbound

This paper cites an unresolved cited work.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Unresolved cited work

Reference 12

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:a413bbda69924a042212b1b305dec79b4532094e5943b60166f4284b985d5f05

Observation 91cfe143-d622-467a-a4ca-2d2f5448dc01 · outbound

This paper cites Genie: Generative interactive environments.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Genie: Generative interactive environments

Reference 13

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:4738681994b8e62ae2c37e9f41a49f5f416a668e5f2150da1e1267044a61d95e

Observation b9c39e98-a094-4114-bb39-cdbb2312030c · outbound

This paper cites nuscenes: A multimodal dataset for autonomous driving.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios nuscenes: A multimodal dataset for autonomous driving

Reference 14

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:9c992df264ebcaa1eeb5cf8ef008bccdb47b65bbd4652ca0c321a03b5b8624b6

Observation 70f85be1-409b-42f7-a307-60c01363c5cf · outbound

This paper cites VRAG: Learning World Models for Interactive Video Generation.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios VRAG: Learning World Models for Interactive Video Generation

Reference 15

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local_arxiv, observed 2026-06-29T18:23:50.443323Z

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=2026-06-29T18:23:22.987086Z digest=sha256:8d9e6584d874587e02e8f83f25ba406b10b5965f7ed7afb5457731daec474b72

Observation 2893582a-4971-4839-a496-b1445e394b71 · outbound

This paper cites Unveiling causal reasoning in large language models: Reality or mirage?Advances in Neural Information Processing Systems, 37:96640–96670, 2024.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Unveiling causal reasoning in large language models: Reality or mirage?Advances in Neural Information Processing Systems, 37:96640–96670, 2024

Reference 16

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:c31b8e5c0225bbb536b44161899deb1af0b076a5e466fecaf577c54fd660f0b4

Observation 94a86343-012c-469a-8188-95c222dcff54 · outbound

This paper cites Veo 3.1 technical report.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Veo 3.1 technical report

Reference 17

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:ab47f2a62b5a9c77fc82dacbad2802a5a7e62dd4d6b3f8dc775d290b6ff14f9c

Observation 39d58627-e050-4e19-bd4d-81297e23212e · outbound

This paper cites Worldscore: A unified evaluation benchmark for world generation.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Worldscore: A unified evaluation benchmark for world generation

Reference 18

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:bef22d6939822713f53e356f72dab2c1598a43cef1153c8a6a300dd80da598ed

Observation 5447d01b-d4c6-4827-988c-d999c60e73cf · outbound

This paper cites CausalVQA: A Physically Grounded Causal Reasoning Benchmark for Video Models.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios CausalVQA: A Physically Grounded Causal Reasoning Benchmark for Video Models

Reference 19

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arxiv_id, observed 2026-06-29T18:23:50.387373Z

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=2026-06-29T18:23:22.987086Z digest=sha256:8697014b2c148521da834713362aadaa4de0d1c0ef59ba590c3db4648c7d8531

Observation bfc4ef56-7b17-4eb3-895a-5e20d9219925 · outbound

This paper cites Physically grounded vision-language models for robotic manipulation.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Physically grounded vision-language models for robotic manipulation

Reference 20

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:e36d1bb44affb00b001cd0c607986c864533eee7cf1aec90c171990d8753d46c

Observation e07c8478-39c6-4e0f-b2fa-be1aff78a821 · outbound

This paper cites Vista: A generalizable driving world model with high fidelity and versatile controllability.Advances in Neural Information Processing Systems, 37:91560–91596, 2024.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Vista: A generalizable driving world model with high fidelity and versatile controllability.Advances in Neural Information Processing Systems, 37:91560–91596, 2024

Reference 21

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:c21c577268f9d3d96da9067ca6ce2d677d18c984a285e97d3ceba978da1fc193

Observation a14a540c-bb22-4d19-960b-51e86d01760c · outbound

This paper cites Shortcut learning in deep neural networks.Nature Machine Intelligence, 2(11):665–673, 2020.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Shortcut learning in deep neural networks.Nature Machine Intelligence, 2(11):665–673, 2020

Reference 22

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:b96e11360ee9e9df734776ea0be405932fa4fe1e89670bdd127ee002463b1deb

Observation 9758491e-52e3-419f-92c0-c140056475ef · outbound

This paper cites CATER: A diagnostic dataset for Compositional Actions and TEmporal Reasoning.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios CATER: A diagnostic dataset for Compositional Actions and TEmporal Reasoning

Reference 23

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arxiv_id, observed 2026-06-29T18:23:50.381067Z

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=2026-06-29T18:23:22.987086Z digest=sha256:24829d6cba531f545ef3cc3b303e3a2a97d86929531b16cf477d0663b2eef3c8

Observation e8feec49-3b22-47ea-869e-112fb719c402 · outbound

This paper cites Genie 3: A new frontier for world models.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Genie 3: A new frontier for world models

Reference 24

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:bb480d8a37ef802fed3d3efa83b29d8bfc4309acfbc1ffa76f7a6eca11955b88

Observation bb8ee50c-75e4-4aa2-9537-016f91649e4a · outbound

This paper cites Agqa: A benchmark for compositional spatio-temporal reasoning.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Agqa: A benchmark for compositional spatio-temporal reasoning

Reference 25

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:a98426b43549befe62707a78a6cd0f1c72e7587497abd2654424b0b7548e5d89

Observation 9b918a7f-00bf-4a7a-a66c-bf48fd0a7dd7 · outbound

This paper cites The Essential Role of Causality in Foundation World Models for Embodied AI.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios The Essential Role of Causality in Foundation World Models for Embodied AI

Reference 26

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arxiv_id, observed 2026-06-29T18:23:50.400419Z

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=2026-06-29T18:23:22.987086Z digest=sha256:2da407b7e6ed94a3eadbe7e87ade69bb14aa9eff50d4ff1939cb0dfd5bcbe729

Observation 7b87d8bf-ad92-4d59-9fae-8665b2feabdd · outbound

This paper cites Mastering Diverse Domains through World Models.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Mastering Diverse Domains through World Models

Reference 27

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local_arxiv, observed 2026-06-29T18:23:50.411638Z

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=2026-06-29T18:23:22.987086Z digest=sha256:376531610cbc7d39e4925a935b151783caf855b3b98088b26d46b1fe7544d25d

Observation c30405d9-a706-4b6d-baa1-2b79e4a6fbc7 · outbound

This paper cites GAIA-1: A Generative World Model for Autonomous Driving.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios GAIA-1: A Generative World Model for Autonomous Driving

Reference 28

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Source-reported events for the cited work

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:574ab7a22bbdf0a65d29cad58476e8fe50d3c5a5ecf85b3c9188951fab5a8a73

Observation 6d2227b6-a2d7-40b1-aa9c-10a6e24ebbf5 · outbound

This paper cites Vbench: Comprehensive benchmark suite for video generative models.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Vbench: Comprehensive benchmark suite for video generative models

Reference 29

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:e0cc07c818206e9f1b5b57e13cdf22c1b11ef57ed6227ded2dae82d509d43ff5

Observation e3012064-5443-4880-b537-77fdb92113e3 · outbound

This paper cites Vbench++: Comprehensive and versatile benchmark suite for video generative models.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Vbench++: Comprehensive and versatile benchmark suite for video generative models.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025

Reference 30

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:276064b2ec2a6bc97e2b78ee6c35150b444a59a3d52c7cf189c7e9c9417ac5a3

Observation 7fea6898-1e36-4fd8-9607-5c96b0cb4923 · outbound

This paper cites How far is video generation from world model: A physical law perspective.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios How far is video generation from world model: A physical law perspective

Reference 31

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:eb7a3012f1e2da8f9d927df2c1c394af2b54555c2edc478d9837ee8f2970cd0c

Observation 05aa365d-cd2d-4a2b-9462-d5e0a366c37d · outbound

This paper cites DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

Reference 32

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local_arxiv, observed 2026-06-29T18:23:50.397554Z

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:97c9ae1e8b7a205ee9e9ae602c4a6cea39132435d68e2651f34cd9476f2024b1

Observation 99c92f26-4afe-4495-9a74-dd4f30599f77 · outbound

This paper cites Kie.ai ai model api platform, 2026.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Kie.ai ai model api platform, 2026

Reference 33

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:296b288655c01587b48bc5cfa5de40081ce9e49986a8390e3d6d048c09dcdfe6

Observation fb302278-bdc5-4002-9573-9452b2770e9c · outbound

This paper cites OpenVLA: An Open-Source Vision-Language-Action Model.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios OpenVLA: An Open-Source Vision-Language-Action Model

Reference 34

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local_arxiv, observed 2026-06-29T18:23:50.409119Z

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=2026-06-29T18:23:22.987086Z digest=sha256:d487fb3904da24c067947affb95edebffbc3b316590de28765afc2f92500bbfa

Observation 84de40a7-289b-4d2d-a6e3-308ea4873384 · outbound

This paper cites Causalvlbench: Benchmarking visual causal reasoning in large vision-language models.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Causalvlbench: Benchmarking visual causal reasoning in large vision-language models

Reference 35

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:ba77c33c9a86db76ba961938b1fd3146451f425285cbabf4b3fed96d90211448

Observation c95dfd25-f6ac-4c47-9198-e9458392e99a · outbound

This paper cites WorldModelBench: Judging Video Generation Models As World Models.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios WorldModelBench: Judging Video Generation Models As World Models

Reference 36

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arxiv_id, observed 2026-06-29T18:23:50.390311Z

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

source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:76815e7d71b43809ff1572c1cb625ccd60582bda57abc825c36594e00e6c1cda

Observation 21c1f094-bd9f-4ef8-9dcf-6764cd417f65 · outbound

This paper cites Multimodal Causal Reasoning Benchmark: Challenging Vision Large Language Models to Discern Causal Links Across Modalities.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Multimodal Causal Reasoning Benchmark: Challenging Vision Large Language Models to Discern Causal Links Across Modalities

Reference 37

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arxiv_id, observed 2026-06-29T18:23:50.361767Z

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

source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:070eceed7200d48d231c817fa3da69adab1f2a3d26c3debbb4525d48c2cbdada

Observation 80c2dc4c-7e88-4606-a8c5-1a03c995ee08 · outbound

This paper cites Physgen: Rigid-body physics-grounded image-to-video generation.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Physgen: Rigid-body physics-grounded image-to-video generation

Reference 38

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:2282f10559b3572d5f5f06d9e7b1010fdf5a4461966c09d7f816f6e57f45409d

Observation ed1683d1-5acf-4be1-a8c4-91ac7b0806a6 · outbound

This paper cites G-eval: Nlg evaluation using gpt-4 with better human alignment.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios G-eval: Nlg evaluation using gpt-4 with better human alignment

Reference 39

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:c44633b44dd539e23747c37bae03ae6efac2049975ecff51f424a8911ac7d1c1

Observation 6639408d-a99a-46e7-a02d-f4e8fd88a0f6 · outbound

This paper cites Evalcrafter: Benchmarking and evaluating large video generation models.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Evalcrafter: Benchmarking and evaluating large video generation models

Reference 40

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:8d2e3ea568e34a4b52df4ffc04974cc41b0f8a679c969dfecc193eb295d8bb30

Observation a45a101d-2b4d-4e0b-bc7c-ec70f494a13a · outbound

This paper cites Compositional Causal Reasoning Evaluation in Language Models.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Compositional Causal Reasoning Evaluation in Language Models

Reference 41

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

source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:be881324a43e6fdbcf6df599543dcd3156aa93b18ae4f14f49923ca9c21ca14f

Observation 6b3c9c2e-891f-4cee-bac8-5e222da552d2 · outbound

This paper cites Towards world simulator: Crafting physical commonsense-based benchmark for video generation.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Towards world simulator: Crafting physical commonsense-based benchmark for video generation

Reference 42

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:3b4c68a7925e61daba41cf4fe5bce945f0e97fde4e214e18eb71ca23ab4c6137

Observation f7fc9a5d-dec4-4ff6-b463-4303b3154eb5 · outbound

This paper cites Do gener- ative video models understand physical principles? InProceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 948–958, 2026.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Do gener- ative video models understand physical principles? InProceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 948–958, 2026

Reference 43

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:6a3c5b6860f1e9e38219b07a67bb0d5dd4e4e885b6740d681d37db10160aeeb4

Observation 811846ca-6884-4de8-a148-cd0805913723 · outbound

This paper cites Counterfactual vqa: A cause-effect look at language bias.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Counterfactual vqa: A cause-effect look at language bias

Reference 44

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:ddfbc7f4d34547d10d930cb258bc8c09db98ed6bf554c9c92e231461681b815a

Observation 748d0ae3-bed2-4e53-88da-35f9d93547b3 · outbound

This paper cites Video generation models as world simulators.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Video generation models as world simulators

Reference 45

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:89814520515c1c830d31161326a98baa5438173bfe55fd40eb94129086d4ea24

Observation 2828a563-fde3-41dd-8e4a-8caa2234857d · outbound

This paper cites Open x- embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration 0.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Open x- embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration 0

Reference 46

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:97805446c1150b60de68083fa164bc80e4c049ed9c014ee8ecf32dedd8bf05cf

Observation 4fd0ee5a-5488-43f6-afb2-33dede048484 · outbound

This paper cites an unresolved cited work.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Unresolved cited work

Reference 47

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:d10267fb894e4b3a5e4ef10f6ce96cccbc573c70af69ca4fac7a13339630060d

Observation 00d18cf0-d906-4304-82af-8ce383394ba2 · outbound

This paper cites Cripp-vqa: Counterfactual reasoning about implicit physical properties via video question answering.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Cripp-vqa: Counterfactual reasoning about implicit physical properties via video question answering

Reference 48

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:da25370a99f79bb910b31a2818e8b66504ba6ef434d79c8c938e11b019a2d4ba

Observation 4746c20f-93ba-45d0-8f59-4bb2c4411309 · outbound

This paper cites Cambridge university press, 2009.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Cambridge university press, 2009

Reference 49

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:816edb7dc5730069be7aea894807fdc0ef008125cc46c7ea8c045fd0180fd945

Observation e7fc84b7-fcc1-4d79-b2ce-08f08665d270 · outbound

This paper cites WorldSimBench: Towards Video Generation Models as World Simulators.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios WorldSimBench: Towards Video Generation Models as World Simulators

Reference 50

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arxiv_id, observed 2026-06-29T18:23:50.414383Z

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

source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:3f215b30a696f5d0b958c1afa2841e6a4ece4231e7585e91ee9492ef29fbaeff

Observation 96d1a665-1de3-4f34-88c1-97d690c24b7d · outbound

This paper cites Toward causal representation learning.Proceedings of the IEEE, 109(5):612–634, 2021.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Toward causal representation learning.Proceedings of the IEEE, 109(5):612–634, 2021

Reference 51

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:d956bf1b26bcd87917f29a6f99275ea814807d4eb8a14b996931e51eba1c7689

Observation f8a645cc-70f2-4ce5-9cf5-c13afee3d265 · outbound

This paper cites Seedance 1.5 pro: A Native Audio-Visual Joint Generation Foundation Model.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Seedance 1.5 pro: A Native Audio-Visual Joint Generation Foundation Model

Reference 52

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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=2026-06-29T18:23:22.987086Z digest=sha256:afe48cc7c3d384b57ccf216da633a620a6825767fbada702b2617b8a998c722e

Observation 1e34c705-1103-45cc-9cbc-bf50363e3408 · outbound

This paper cites Seedance 2.0: Advancing Video Generation for World Complexity.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Seedance 2.0: Advancing Video Generation for World Complexity

Reference 53

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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=2026-06-29T18:23:22.987086Z digest=sha256:4650419fd18e3331d71f118b57495c4556706323da302a776fc12ccf4db47c5f

Observation c5020912-8b41-4953-a293-15d2da57521f · outbound

This paper cites Worldarena: A unified benchmark for evaluating perception and functional utility of embodied world models.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Worldarena: A unified benchmark for evaluating perception and functional utility of embodied world models

Reference 54

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

source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:aaee5f9c731516d515a06c839d8d78d3f491fe65ff4593758fd4e12e81c4f6f8

Observation 23897052-9e9c-4964-b660-826ca03b00f2 · outbound

This paper cites T2v- compbench: A comprehensive benchmark for compositional text-to-video generation.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios T2v- compbench: A comprehensive benchmark for compositional text-to-video generation

Reference 55

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:873b58bcd1b6ec74465e01f6470d77b8cdc0f0c028511d5b912ce8419dbd1973

Observation bdd7aa5c-58cb-4ac9-a7f7-48fddab4f1ad · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Gemini: A Family of Highly Capable Multimodal Models

Reference 56

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

source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:cac53506cbdd25cc95be3cf42eeadb288ec3a7b457a514fc7a912a81365cdb6d

Observation ee9f6de7-f860-4d0c-8c85-b069dd08ac54 · outbound

This paper cites Kling-Omni Technical Report.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Kling-Omni Technical Report

Reference 57

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

source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:81454022fb7afe04ab2f2a4fb4af2aeb1e64bd3aefcd8c57e3c6f6f82ef019da

Observation 64ae3862-2a3f-435f-a749-0f0684852523 · outbound

This paper cites Winoground: Probing vision and language models for visio-linguistic compositionality.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Winoground: Probing vision and language models for visio-linguistic compositionality

Reference 58

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:ecbf445dd796aed78bf03d110a69ab37fe436866492d25fa3f4c6c5662c1acfe

Observation 1d868501-14df-46fd-ba99-96a22601e8e2 · outbound

This paper cites an unresolved cited work.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Unresolved cited work

Reference 59

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:894cb2b90a23ec8a6256b92415cae50e3af8f4411c95ad1493f6e881b2e04394

Observation d0b7ad6b-b23b-4243-b012-d86f03c1d864 · outbound

This paper cites Wan: Open and Advanced Large-Scale Video Generative Models.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Wan: Open and Advanced Large-Scale Video Generative Models

Reference 60

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local_arxiv, observed 2026-06-29T18:23:50.437754Z

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

source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:e90ae9ebb6b39d24ea2e08da175108b6166fa244800f3cc7548ae75158751331

Observation 01b56f1f-cef5-4c33-b99d-f034051f4bfe · outbound

This paper cites GenSim: Generating Robotic Simulation Tasks via Large Language Models.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios GenSim: Generating Robotic Simulation Tasks via Large Language Models

Reference 61

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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=2026-06-29T18:23:22.987086Z digest=sha256:ad762857e8e94aa6dfef5edd8f959560ce1ba457e8f6b1b4ab913096b5563de1

Observation 849fa882-b67a-4c3d-9470-37825e937304 · outbound

This paper cites Large language models are not fair evaluators.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Large language models are not fair evaluators

Reference 62

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:0eaa166ada2bccd10662d8ebcae890bf66eb02432d83f4d5f96820225eb70c83

Observation ccc1480c-ff55-4f80-96ce-0059d1fada48 · outbound

This paper cites Omnidrive: A holistic vision-language dataset for autonomous driving with counterfactual reasoning.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Omnidrive: A holistic vision-language dataset for autonomous driving with counterfactual reasoning

Reference 63

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:892a4b890065c8abc5b8be979f9bf511868e13b06a7bb2419ef82af60df633e0

Observation 3859a7cf-1f9f-40a8-bdc7-44fe4e28fd05 · outbound

This paper cites Drive- dreamer: Towards real-world-drive world models for autonomous driving.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Drive- dreamer: Towards real-world-drive world models for autonomous driving

Reference 64

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:3b796b6cc1eb4ba840570dfc051994d454ae1b718259822bb1e991099e6e178d

Observation b6f9ff10-1ac0-4d90-8520-dcf54c305edf · outbound

This paper cites Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail

Reference 65

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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=2026-06-29T18:23:22.987086Z digest=sha256:3e95f26b391f12882196ce8ecb2b05c01f4c908897ca051bdc52e180a09b3c39

Observation 3a454eaf-2584-4e77-8d5c-74bd66c6e61b · outbound

This paper cites Causalbench: A comprehensive benchmark for evaluating causal reasoning capabilities of large language models.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Causalbench: A comprehensive benchmark for evaluating causal reasoning capabilities of large language models

Reference 66

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:638de19ef55ca4684a0898cba3f13a43d2184c11b3d96966c0e1f1299b046de7

Observation 02e07769-2bae-43ab-9b93-72549519475a · outbound

This paper cites HunyuanVideo 1.5 Technical Report.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios HunyuanVideo 1.5 Technical Report

Reference 67

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local_arxiv, observed 2026-06-29T18:23:50.370134Z

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

source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:49b33ec7d21a6ead0f317db4c18007de3247429e938112c650175947cac178b3

Observation 0e77163e-16be-4107-86bd-f14845461c73 · outbound

This paper cites STAR: A Benchmark for Situated Reasoning in Real-World Videos.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios STAR: A Benchmark for Situated Reasoning in Real-World Videos

Reference 68

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arxiv_id, observed 2026-06-29T18:23:50.384160Z

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

source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:abf67998ab762b3366af2d4af861aae41646e427041dc919aeffbb1139de0654

Observation 52cfe877-7da2-4a7f-9825-16c53dc2f7f2 · outbound

This paper cites Video World Models with Long-term Spatial Memory.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Video World Models with Long-term Spatial Memory

Reference 69

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arxiv_id, observed 2026-06-29T18:23:50.422893Z

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=2026-06-29T18:23:22.987086Z digest=sha256:def2fe44943460da48ebfcb01ebea43583b193f4e9880be1aba9d6b2c9c64fec

Observation b92568bb-c81a-411e-8341-c5a1aa543c37 · outbound

This paper cites Grok imagine: Multimodal image and video generation model, 2025.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Grok imagine: Multimodal image and video generation model, 2025

Reference 70

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:8e0efec3738743430821030b32c5494b12c72095edc98593da6af8a5a5ff2d9c

Observation 71909a19-4e14-4a2d-89fc-7428a5bbcc85 · outbound

This paper cites Next-qa: Next phase of question- answering to explaining temporal actions.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Next-qa: Next phase of question- answering to explaining temporal actions

Reference 71

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:b6538c73313e4cdc2dc22faed3fd3c466eca5a535f6602567306961a6b111493

Observation 42b1f6a7-10fa-4aae-b144-9add24c930a7 · outbound

This paper cites Unisim: A neural closed-loop sensor simulator.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Unisim: A neural closed-loop sensor simulator

Reference 72

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:5c59522c198f1ae0c2355b913dfe9c7cbc1ceb2289defbe34e264726ec6007cc

Observation 39a1d28c-5111-4589-a2b1-1d18d4d3ccec · outbound

This paper cites CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Reference 73

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local_arxiv, observed 2026-06-29T18:23:50.446357Z

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=2026-06-29T18:23:22.987086Z digest=sha256:afb1c3976ac038b4298d856871dfcbac04dac0427e4052d50fac780bc15fdf0b

Observation 51c52bc1-dd3e-4674-a0fe-25c1540fdc1a · outbound

This paper cites Tenenbaum.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Tenenbaum

Reference 74

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:668549c69f80631fffdde8c4e5f74f34dfb09e6de1325052b916bbcc0001b530

Observation fe3173a2-4724-4ca7-aaed-e942764f59a5 · outbound

This paper cites Chronomagic-bench: A benchmark for metamorphic evaluation of text-to-time-lapse video generation.Advances in Neural Information Processing Systems, 37:21236–21270, 2024.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Chronomagic-bench: A benchmark for metamorphic evaluation of text-to-time-lapse video generation.Advances in Neural Information Processing Systems, 37:21236–21270, 2024

Reference 75

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:e29e39cc931904f2faecf2f2bcd67bcc10cec75c333d73934a08698e36a0c1f8

Observation c2a4d166-a776-4cf3-a84c-013c05566a01 · outbound

This paper cites Magicbrush: A manually annotated dataset for instruction-guided image editing.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Magicbrush: A manually annotated dataset for instruction-guided image editing

Reference 76

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:48f1830e972601248a860b6ec67d122ed8d3e7d5bf2278ccad695fce07d66650

Observation 94fa7883-bdb0-4bd1-a817-7a5bdcaf72ca · outbound

This paper cites TesserAct: Learning 4D Embodied World Models.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios TesserAct: Learning 4D Embodied World Models

Reference 77

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arxiv_id, observed 2026-06-29T18:23:50.417573Z

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=2026-06-29T18:23:22.987086Z digest=sha256:2c957f2d4ce8255e203aa6dced0c375f41f31b3db25f837c62f2e619f0d6bf36

Observation aaddc02a-7f16-4cdc-bf6f-94455700735c · outbound

This paper cites VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness

Reference 78

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local_arxiv, observed 2026-06-29T18:23:50.420197Z

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=2026-06-29T18:23:22.987086Z digest=sha256:8917a9ef4f1ff88e1f76280ae6648019fed18d5be77886b57799076f31d6cfdb

Observation 074d939f-06d0-4a1e-af2a-f9bbfe7bae00 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.Advances in neural information processing systems, 36:46595–46623, 2023.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Judging llm-as-a-judge with mt-bench and chatbot arena.Advances in neural information processing systems, 36:46595–46623, 2023

Reference 79

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:29d9189f1dee437c5b8cdadafc22d18a9c90ea88ec43c37e1f6511a8e62143ee

Observation 8b69a24a-8e10-4c6e-abd5-7d4fd5927f73 · outbound

This paper cites ContPhy: Continuum Physical Concept Learning and Reasoning from Videos.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios ContPhy: Continuum Physical Concept Learning and Reasoning from Videos

Reference 80

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arxiv_id, observed 2026-06-29T18:23:50.378133Z

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=2026-06-29T18:23:22.987086Z digest=sha256:efa7b01a64805a03c060899d9a2be33cfbc1d5c06781e959594b154e786fba6e

Observation cf347445-125c-4eb9-803a-b4dbe4dadd3f · outbound

This paper cites Drivinggen: A comprehensive benchmark for generative video world models in autonomous driving.arXiv preprint arXiv:2601.01528.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Drivinggen: A comprehensive benchmark for generative video world models in autonomous driving.arXiv preprint arXiv:2601.01528

Reference 81

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arxiv_id, observed 2026-06-29T18:23:50.364767Z

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=2026-06-29T18:23:22.987086Z digest=sha256:6111b5cc035c98d1bc2a36660a32e1e6287caee6b1cac8791277bfa38a88307f

Observation 5b1f372b-2e01-4a1c-a441-2392f4315b05 · outbound

This paper cites icy surface.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios icy surface

Reference 82

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Source-reported events for the cited work

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:79bb5eaf019ed0bd8ef7d42281be0ce569e4c76bd9eb55dfaf41240e17d88968

Observation 81f7950d-d953-4fb8-b690-3376ce91050a · outbound

This paper cites an unresolved cited work.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Unresolved cited work

Reference 83

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:100177cd052dbb04f473a7490b66202c47174b2f4df4fe911afd806714b3d737

Observation 76dcdd03-15f0-4915-aaea-5b071e1a4a3f · outbound

This paper cites Video B, and (d)causal constraintsdictating which entities may and may not change.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Video B, and (d)causal constraintsdictating which entities may and may not change

Reference 84

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:b5f1e035557ffac2e2d6b2ced46e83a02a2abac627ee1e693d66835c4cb62919

Observation ed8f2436-848e-4f89-8d21-32970d8d222d · outbound

This paper cites an unresolved cited work.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Unresolved cited work

Reference 85

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:15962332f1e5635d0a1c51919995f99d4e175a7a86b3218c6b7ef6856c795fb1

Observation bb17f0ee-b5c6-43b7-9593-0a84eb7015c0 · outbound

This paper cites an unresolved cited work.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios Unresolved cited work

Reference 86

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:babc23e491c417857985135f5e23b80c07c26dae0063e19e1f18bdf7fa2e12f8

Observation 19ab91b7-922a-4d27-a71c-f6e0d97e11ec · outbound

This paper cites A_Adherence_Single.

What-If World: A Causal Benchmark for General World Models in Embodied Scenarios A_Adherence_Single

Reference 87

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source=pdf_text observed=2026-06-29T18:23:22.987086Z digest=sha256:9ce984379c0d578ec145297da47ee3052c114f34ece6b262f427b71bd5b6d1e0

Pith citing papers

Observation 1f520f89-7eaa-4ecb-8227-ad1780631dc8 · inbound

Why Does the Future Branch? Identifiable Closure Tests for Stochastic Physical World Models cites this paper.

Why Does the Future Branch? Identifiable Closure Tests for Stochastic Physical World Models What-If World: A Causal Benchmark for General World Models in Embodied Scenarios

Reference 27

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local_arxiv, observed 2026-08-05T00:42:28.649422Z

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=2026-08-05T00:42:28.416916Z digest=sha256:ad65671a95a8847bd4e1f5696fe6127f476db7e3d965ddde3a58116b6a0a59de

Observation 178f7739-2537-4975-b7bf-4b84f80cc2f0 · inbound

Twin Rollouts: Noise-Coupled Counterfactual Branching in Interactive Video World Models cites this paper.

Twin Rollouts: Noise-Coupled Counterfactual Branching in Interactive Video World Models What-If World: A Causal Benchmark for General World Models in Embodied Scenarios

Reference 2

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source=pdf_text observed=2026-08-14T04:23:57.855823Z digest=sha256:def02bce79777bfec4f66b9d22221ecbdf9fe79977c3487537a7e2e99ac3ada7

Observation 94cdc256-45ce-4789-bb49-1612e101b291 · inbound

WorldSimProbe: Diagnosing Simulator Faithfulness in Action-Conditioned World Models for Embodied Manipulation cites this paper.

WorldSimProbe: Diagnosing Simulator Faithfulness in Action-Conditioned World Models for Embodied Manipulation What-If World: A Causal Benchmark for General World Models in Embodied Scenarios

Reference 36

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source=arxiv_source observed=2026-08-11T19:59:33.585056Z digest=sha256:704be470c7facf6052576fa6faba7c8b3d7d19146ef14809a73b1c2a215fe8fe