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Paper Citation Record · LEDGER

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models

As of 21 August 2026, this Paper Citation Record lists 100 of 183 outbound references and 0 inbound Pith citation observations for arXiv:2608.09696.

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

pith.paper-citation-record.v1
2608.09696 v3

Coverage vector

measured 100 of 183 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:21:09.184325Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

100 of 183 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved92
  • parse uncertain1
  • malformed identifier0
  • metadata mismatch7

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9113bdca-039e-43f1-b706-f49ed9b31b6c · outbound

This paper cites ATLAS: Active Theory Learning for Automated Science.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models ATLAS: Active Theory Learning for Automated Science

Reference 1

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Observation e93bf61e-5eda-40f6-87c3-12393c6772ae · outbound

This paper cites Action-BED: Task-Driven Bayesian Experimental Design with Singly Intractable Objectives.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Action-BED: Task-Driven Bayesian Experimental Design with Singly Intractable Objectives

Reference 2

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Observation d34b4edf-65df-4198-a8ec-225aa21e7034 · outbound

This paper cites Program Synthesis for Simulation-Based Inference: Joint Model Selection and Parameter Estimation.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Program Synthesis for Simulation-Based Inference: Joint Model Selection and Parameter Estimation

Reference 4

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Observation 89aa51fc-541f-40f5-9222-caafba7065b3 · outbound

This paper cites Murphy , title =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Murphy , title =

Reference 5

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Observation cd6f9592-88c5-42ac-b414-87b6dd4b3286 · outbound

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Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Unresolved cited work

Reference 6

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Observation 38b6a246-86a4-4df6-86f9-16614d12c6c9 · outbound

This paper cites Next-Latent Prediction Transformers Learn Compact World Models.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Next-Latent Prediction Transformers Learn Compact World Models

Reference 7

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Observation b5960375-9d01-460a-a369-e2f930224bb4 · outbound

This paper cites LaT-PFN: A Joint Embedding Predictive Architecture for In-context Time-series Forecasting.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models LaT-PFN: A Joint Embedding Predictive Architecture for In-context Time-series Forecasting

Reference 8

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Observation 3afd2d8e-a94c-46bf-9e00-54859f3269a0 · outbound

This paper cites Causal Reasoning and Large Language Models: Opening a New Frontier for Causality , journal =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Causal Reasoning and Large Language Models: Opening a New Frontier for Causality , journal =

Reference 9

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Observation e734dd71-00c2-4b42-85a3-11220222f00b · outbound

This paper cites IEEE Transactions on Artificial Intelligence , year =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models IEEE Transactions on Artificial Intelligence , year =

Reference 10

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Observation 440e81c3-d42e-4e9a-934b-d092a68e7b8c · outbound

This paper cites Lauffenburger and Garry P.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Lauffenburger and Garry P

Reference 11

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Observation 91d03c01-779f-4abd-94dc-02e497b85ab7 · outbound

This paper cites Roohani and Arash Mehrjou and Jure Leskovec and Patrick Schwab , title =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Roohani and Arash Mehrjou and Jure Leskovec and Patrick Schwab , title =

Reference 12

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Observation d9d4e58c-5982-44a2-86d1-236e37ba9300 · outbound

This paper cites Toward Causal Representation Learning , journal =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Toward Causal Representation Learning , journal =

Reference 13

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Observation 276c5aa6-477f-4238-91c6-d343df95b111 · outbound

This paper cites Halpern , title =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Halpern , title =

Reference 14

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Observation 771affcf-2655-4028-b6fd-a03519c929d6 · outbound

This paper cites Rubenstein and Sebastian Weichwald and Stephan Bongers and Joris M.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Rubenstein and Sebastian Weichwald and Stephan Bongers and Joris M

Reference 15

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Observation 3fea3339-c255-434e-9869-0816535946a3 · outbound

This paper cites Interventions, Where and How?.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Interventions, Where and How?

Reference 16

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Observation b6863238-4ef2-46dd-b0b9-c9f2b465b65f · outbound

This paper cites an unresolved cited work.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Unresolved cited work

Reference 17

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Observation 84e091ab-446f-4a97-8407-101408cccbb3 · outbound

This paper cites Ivanova and Ilyas Malik and Tom Rainforth , title =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Ivanova and Ilyas Malik and Tom Rainforth , title =

Reference 18

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Observation 1f414162-b69c-4bef-a1e0-de0537936bd7 · outbound

This paper cites Amortized Inference for Causal Structure Learning , booktitle =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Amortized Inference for Causal Structure Learning , booktitle =

Reference 19

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Observation 002a4c5a-8415-4a21-8222-3862de0646dc · outbound

This paper cites Wang and J.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Wang and J

Reference 20

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Observation c31308e4-6fb5-4d89-9757-5822dd06bb0a · outbound

This paper cites Transformers Can Do.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Transformers Can Do

Reference 21

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Observation 9ae06d70-c1c4-4c22-91ab-293f352930a4 · outbound

This paper cites The Value Equivalence Principle for Model-Based Reinforcement Learning , booktitle =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models The Value Equivalence Principle for Model-Based Reinforcement Learning , booktitle =

Reference 22

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Observation 7b9d7546-64cb-4d40-9435-1ec98a4757b7 · outbound

This paper cites International Conference on Learning Representations (ICLR) , year =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models International Conference on Learning Representations (ICLR) , year =

Reference 23

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Observation 781e16eb-a7f0-4a75-9822-cf576799ad3f · outbound

This paper cites Recurrent Independent Mechanisms , booktitle =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Recurrent Independent Mechanisms , booktitle =

Reference 24

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Observation ca45fc53-d766-4ef0-b141-b182fc8c6513 · outbound

This paper cites Proceedings of the 39th International Conference on Machine Learning (ICML) , series =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Proceedings of the 39th International Conference on Machine Learning (ICML) , series =

Reference 25

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Observation f86b4755-332b-40c1-b52d-ae5a78b32b95 · outbound

This paper cites Journal of Artificial Intelligence Research , volume =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Journal of Artificial Intelligence Research , volume =

Reference 26

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Observation 4977ba55-c682-4b86-a830-438071186c43 · outbound

This paper cites Journal of Artificial Intelligence Research , volume =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Journal of Artificial Intelligence Research , volume =

Reference 27

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Observation 0d17799f-bde0-40aa-9a55-a86ef3e04eb7 · outbound

This paper cites Science , volume =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Science , volume =

Reference 28

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Observation d3a9935c-3121-4f3c-b273-486408689445 · outbound

This paper cites Nature , volume =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Nature , volume =

Reference 29

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Observation eb75d464-0f1e-427d-9a04-61c26380d779 · outbound

This paper cites Devon Hjelm and Aaron Courville and Philip Bachman , title =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Devon Hjelm and Aaron Courville and Philip Bachman , title =

Reference 30

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This paper cites International Conference on Learning Representations (ICLR) , year =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models International Conference on Learning Representations (ICLR) , year =

Reference 31

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Observation c93f5d80-469b-4e51-840c-c7c5fe6833a4 · outbound

This paper cites Proceedings of the 42nd International Conference on Machine Learning (ICML) , year =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Proceedings of the 42nd International Conference on Machine Learning (ICML) , year =

Reference 32

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Observation ee0a5081-dd2f-49db-9dad-97919fd72ae0 · outbound

This paper cites Learning POMDP World Models from Observations with Language-Model Priors.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Learning POMDP World Models from Observations with Language-Model Priors

Reference 33

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Observation 48233d54-62c0-4a37-96e5-7e079c853615 · outbound

This paper cites Raftery , title =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Raftery , title =

Reference 34

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Observation 1a2564d6-6cb0-47aa-a886-f15870b83812 · outbound

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Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Brier , title =

Reference 35

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Observation b45f356f-a57a-4cc3-b208-778e6043e349 · outbound

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Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Unresolved cited work

Reference 36

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Observation f9fe4560-4d25-41d2-9baa-7c615d760b41 · outbound

This paper cites Is Capability a Liability? More Capable Language Models Make Worse Forecasts When It Matters Most.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Is Capability a Liability? More Capable Language Models Make Worse Forecasts When It Matters Most

Reference 38

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local_arxiv, observed 2026-08-14T04:21:10.836997Z

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Observation da6a80e8-ab7c-47b7-8c41-37224ce97651 · outbound

This paper cites LLM-Guided ODE Discovery and Parameter Inference from Small-Cohort Aggregate Data.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models LLM-Guided ODE Discovery and Parameter Inference from Small-Cohort Aggregate Data

Reference 39

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local_arxiv, observed 2026-08-14T04:21:10.804300Z

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Observation bfcaf486-0391-4732-b5ba-c934b9c6f44f · outbound

This paper cites , title =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models , title =

Reference 40

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Observation e46d6e0e-4e55-4628-88fa-558fdadf55f5 · outbound

This paper cites , title =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models , title =

Reference 41

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source=arxiv_source observed=2026-08-14T04:21:08.874574Z digest=sha256:3ed1fa78f80c41ab2d3619be9cf11c8ecbf48e1a83ebe45ecf287774e6e00ebf

Observation d579ccdb-6235-4030-af3d-abb7c3f3f780 · outbound

This paper cites Pawan and Dupont, Emilien and Ruiz, Francisco J.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Pawan and Dupont, Emilien and Ruiz, Francisco J

Reference 42

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source=arxiv_source observed=2026-08-14T04:21:08.879321Z digest=sha256:17d8e74e35217f5df5647625f9399e5b9b170bb2f05fe9ae3090882a65033be3

Observation af1a55c0-eac6-4fc5-98e8-f8148d0f1d55 · outbound

This paper cites Advances in Neural Information Processing Systems (NeurIPS) , year =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Advances in Neural Information Processing Systems (NeurIPS) , year =

Reference 44

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source=arxiv_source observed=2026-08-14T04:21:08.888239Z digest=sha256:a23bd143138020af63a2f1a5186fcc59a1acd42fa054d03770a25d38a67e8ba2

Observation e870296e-d1e8-4b44-8ca8-e0ddcb100a51 · outbound

This paper cites Automatic Ordinary Differential Equations Discovery For Biological Systems Using Large Language Model Powered Agentic System.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Automatic Ordinary Differential Equations Discovery For Biological Systems Using Large Language Model Powered Agentic System

Reference 45

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local_arxiv, observed 2026-08-14T04:21:10.748947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-14T04:21:08.892435Z digest=sha256:3fd5b29ec84e6421f31acf5d4c9668e68d3c4a962f820b7049948792ddaea628

Observation b1a8fe90-913a-4170-afbe-fb1c83c0f119 · outbound

This paper cites Nature Methods , year=.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Nature Methods , year=

Reference 46

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source=arxiv_source observed=2026-08-14T04:21:08.897829Z digest=sha256:056abf91e11b04facd272b16e3f8a93cc218e77ba649aaaccc50c53625cf5a63

Observation c58c6d73-3659-4769-bc9c-9d1b70709374 · outbound

This paper cites Research in Computational Molecular Biology (RECOMB) , year=.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Research in Computational Molecular Biology (RECOMB) , year=

Reference 47

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source=arxiv_source observed=2026-08-14T04:21:08.902472Z digest=sha256:707e8a97a7bab147d93892f997352bd5f3a703ee007d8758db7325007981be0b

Observation 1cfb0672-bd08-4d37-a315-fee992df51f2 · outbound

This paper cites Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space

Reference 48

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local_arxiv, observed 2026-08-14T04:21:10.724986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-14T04:21:08.907629Z digest=sha256:b91e2dd3adae8be225d12af25a3015f0af711667d1f4e9e164db5304cdd02fd0

Observation 40fd7347-9cf5-4631-a233-ca17bc7be6e7 · outbound

This paper cites Cell Systems , volume=.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Cell Systems , volume=

Reference 49

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source=arxiv_source observed=2026-08-14T04:21:08.912152Z digest=sha256:0eeb45ea49ced2eb6f37f007398518073afd01b25900868cc9f360ccd5b8580e

Observation 5953d32b-d06a-4926-a119-d700adb80dcb · outbound

This paper cites Nature Methods , volume=.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Nature Methods , volume=

Reference 50

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source=arxiv_source observed=2026-08-14T04:21:08.916754Z digest=sha256:6f70131523542f3667e2a54010f735e46d7dff0cb0cefc55f15cb3d1546c96af

Observation 53a5224d-aeff-4549-a29d-8db6a3c56a99 · outbound

This paper cites Nature Biotechnology , volume=.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Nature Biotechnology , volume=

Reference 51

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source=arxiv_source observed=2026-08-14T04:21:08.921235Z digest=sha256:76a7c9fbd22e044d7e7db10e45a39d89ca53b9532eee40f1e3fb613e5f5fcab3

Observation 1b54f919-1a77-400c-a9f4-bef4247a6949 · outbound

This paper cites Cell Reports , volume=.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Cell Reports , volume=

Reference 52

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source=arxiv_source observed=2026-08-14T04:21:08.925614Z digest=sha256:b036ffd79ab9ef9459796fde3601908392dade5305b562c685eed249d5bec359

Observation 220f4201-8454-466b-8672-a9071be794ea · outbound

This paper cites Cell Systems , volume=.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Cell Systems , volume=

Reference 53

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source=arxiv_source observed=2026-08-14T04:21:08.930327Z digest=sha256:d1da9b75bec3a009a2b4b6f34f0d9cc88690e0302bac8e8148fe53bed78edbab

Observation 2712e245-04ae-45e7-a171-900debb66272 · outbound

This paper cites Science , volume=.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Science , volume=

Reference 54

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source=arxiv_source observed=2026-08-14T04:21:08.936582Z digest=sha256:81dcdbb444bb57863bc40a4c2ff499c21a1d7d1940efd22b0f62f659885c1d9d

Observation a81e8c54-dafd-42f4-b89c-a4b5501aa835 · outbound

This paper cites Science , volume=.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Science , volume=

Reference 55

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source=arxiv_source observed=2026-08-14T04:21:08.941810Z digest=sha256:f47b3f1ab03b81f7b12ba7aeb22f1c793ec5dceb2e3970da26a75951e0748838

Observation f1c102e4-26d2-4bbe-952f-d02ba9ba1299 · outbound

This paper cites Proceedings of the National Academy of Sciences , volume =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Proceedings of the National Academy of Sciences , volume =

Reference 56

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source=arxiv_source observed=2026-08-14T04:21:08.947703Z digest=sha256:933b211c580d553682419331df8c74e5ae40ca421a18b34b8d3d841369d95ab7

Observation 158bc8e0-b1e2-4758-8886-7f0241c11c4e · outbound

This paper cites Journal of the Royal Statistical Society: Series B , volume =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Journal of the Royal Statistical Society: Series B , volume =

Reference 57

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source=arxiv_source observed=2026-08-14T04:21:08.953459Z digest=sha256:18caef82ae6ea4203d1d9c39a307be1adbcd9d19e6a8151b2e8b19866e5d33f5

Observation 44c2d508-1f71-4f2f-8e15-b6ab81bd476a · outbound

This paper cites Wong , title =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Wong , title =

Reference 58

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source=arxiv_source observed=2026-08-14T04:21:08.958194Z digest=sha256:269941bec8371cfddab50c8eca4fb3e83fc83c48fa7b723fa3ac374e9f1b8fba

Observation 1ff2ced9-06c7-4f3a-8f25-7cbdb0e655d5 · outbound

This paper cites Gutmann and Aaron Courville and Zhanxing Zhu , title =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Gutmann and Aaron Courville and Zhanxing Zhu , title =

Reference 59

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source=arxiv_source observed=2026-08-14T04:21:08.962406Z digest=sha256:39435bd2fc942575bc5c29f18fd46d6b5b30f411502d1a01126e536880160fa8

Observation f75349a3-8945-453a-ac17-8cb667eddeae · outbound

This paper cites Radev and Ulf K.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Radev and Ulf K

Reference 60

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source=arxiv_source observed=2026-08-14T04:21:08.966688Z digest=sha256:7789cea57893169c662f4118485c7df7a656ca8c447e6a81feb218257323fcf1

Observation 92466ace-06c7-4d21-84cc-2f871fb3d892 · outbound

This paper cites Lindley , title =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Lindley , title =

Reference 61

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source=arxiv_source observed=2026-08-14T04:21:08.970751Z digest=sha256:8465ee2ba9e007ec55d147a6841299284fe82ca2ed8fb91e776a369649aff225

Observation 74a3187c-aed9-4f26-a5b9-d751800ce046 · outbound

This paper cites McCulloch and Walter Pitts , title =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models McCulloch and Walter Pitts , title =

Reference 62

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source=arxiv_source observed=2026-08-14T04:21:08.976071Z digest=sha256:6af20e46e5c6b61176886feb6197438e7ee2803fd7bd1f884ad2bbec9c45aa40

Observation dd9a6812-86be-4655-9208-4629637faebf · outbound

This paper cites On the Measure of Intelligence , journal =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models On the Measure of Intelligence , journal =

Reference 63

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source=arxiv_source observed=2026-08-14T04:21:08.979953Z digest=sha256:283f01931cdfae19e9a865e8c83046d05f47240cf189fad1d6efb44c4ff67604

Observation 85356d74-567d-4a75-a49c-79e1043d7520 · outbound

This paper cites an unresolved cited work.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Unresolved cited work

Reference 65

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source=arxiv_source observed=2026-08-14T04:21:08.989442Z digest=sha256:67736193bc674fc5bcb665c6033572534730a2baafdbcaf917b76f11632c4ae4

Observation db73f937-3139-407b-b97a-95c724af07bf · outbound

This paper cites Statistical Science , volume=.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Statistical Science , volume=

Reference 66

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source=arxiv_source observed=2026-08-14T04:21:08.993967Z digest=sha256:19e27ad8fd7b81f9b506dba4e7412c5b2876e7278b6c29be66f97194efdd554f

Observation 4712789d-62e1-4de3-9483-f946315719a9 · outbound

This paper cites Ivanova and Freddie Bickford Smith , title=.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Ivanova and Freddie Bickford Smith , title=

Reference 67

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source=arxiv_source observed=2026-08-14T04:21:08.999682Z digest=sha256:c7dd98aab14a34a093eb10cae23cba4e2ffaf57ee7b75e555ab73eeeb05d050c

Observation 4f4efea8-86d5-4ce3-b040-9bfca9ea1383 · outbound

This paper cites Bernardo and Adrian F.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Bernardo and Adrian F

Reference 68

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source=arxiv_source observed=2026-08-14T04:21:09.005612Z digest=sha256:fbb1718b2b6482fb7989c5e7db8dba1a532af348d153bbad7ec334517dbc03f4

Observation d3d64dc0-c946-492c-b611-9a6188baa355 · outbound

This paper cites an unresolved cited work.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Unresolved cited work

Reference 69

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source=arxiv_source observed=2026-08-14T04:21:09.010495Z digest=sha256:2c1eb376bf25e7ce82013fa7896510264e8a905a141d61cbf75c5bd007b62193

Observation ce0be79d-e84e-4255-9bca-aaca1b8e85da · outbound

This paper cites an unresolved cited work.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Unresolved cited work

Reference 70

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source=arxiv_source observed=2026-08-14T04:21:09.015523Z digest=sha256:a61797b663bf1b18c85b77788e07eb77933a15e766cc703d1b76658d2f7451ff

Observation eee88213-6518-427d-8543-296db663d2e5 · outbound

This paper cites Statistical Causality from a Decision-Theoretic Perspective.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Statistical Causality from a Decision-Theoretic Perspective

Reference 71

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source=arxiv_source observed=2026-08-14T04:21:09.020125Z digest=sha256:b878a0380851c1dd7b9fdb1a6b8e5dd75be50b6899cef2ce7fa71f5f2510c7f8

Observation 1b91990d-18d1-4655-834d-17ba79cb53b3 · outbound

This paper cites On Pearl’s Hierarchy and the Foundations of Causal Inference.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models On Pearl’s Hierarchy and the Foundations of Causal Inference

Reference 72

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source=arxiv_source observed=2026-08-14T04:21:09.024780Z digest=sha256:39f3920b4f2feab30e5c86f9c97be3fde014be6d0cbf324a3e75525582ab7b01

Observation 68524293-1ba5-417c-9590-3f81c75d2164 · outbound

This paper cites On scientific understanding with artificial intelligence.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models On scientific understanding with artificial intelligence

Reference 73

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source=arxiv_source observed=2026-08-14T04:21:09.030854Z digest=sha256:d1ed9f9defad80740488b3b8578ac0b9a478f95cca7c12afc611e65a3a1df13b

Observation 88ccd35e-5f68-4690-beca-ec0b75b1253e · outbound

This paper cites Artificial intelligence and illusions of understanding in scientific research.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Artificial intelligence and illusions of understanding in scientific research

Reference 74

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source=arxiv_source observed=2026-08-14T04:21:09.036094Z digest=sha256:4bd0b7758c1877f9fb0adddefdb98afb1a9f2a85d9e87095356064b02b2f42ef

Observation 57c67878-6603-486c-8d9f-4a828df1618d · outbound

This paper cites From scientific theory to duality of predictive artificial intelligence models.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models From scientific theory to duality of predictive artificial intelligence models

Reference 75

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source=arxiv_source observed=2026-08-14T04:21:09.041669Z digest=sha256:a365c669b64bcb2434e476efaa751748e7b00d2acbcccd8a12a746efe2190e94

Observation 99252dd5-bb55-4974-9a9f-75aa88ae2512 · outbound

This paper cites From prediction to understanding: Will AI foundation models transform brain science?.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models From prediction to understanding: Will AI foundation models transform brain science?

Reference 76

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source=arxiv_source observed=2026-08-14T04:21:09.046760Z digest=sha256:3f9d6ac16eaec595bd5c9ec38702adb36482f7e78634a4da9d61825dc12af047

Observation e42667bc-e458-4b57-8684-3f6b44337ba0 · outbound

This paper cites Scientific explanation and the causal structure of the world.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Scientific explanation and the causal structure of the world

Reference 77

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source=arxiv_source observed=2026-08-14T04:21:09.051258Z digest=sha256:00525ef2443dfc699ed84426fd4aef87f691fce34eaa5eb78f0f0fa4e7edbdc8

Observation cdf52a32-54bf-403e-99c4-a2e6f5ffe30d · outbound

This paper cites Automatic ordinary differential equations discovery for biological systems using large language model powered agentic system.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Automatic ordinary differential equations discovery for biological systems using large language model powered agentic system

Reference 78

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source=arxiv_source observed=2026-08-14T04:21:09.056080Z digest=sha256:34751f3beffe6f85d225dd018616fded9fb4206e0d06436baf5c9980168ee71c

Observation c3fe08eb-a525-48e7-8d0f-984057480c96 · outbound

This paper cites Bayesian prediction for artificial intelligence.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Bayesian prediction for artificial intelligence

Reference 79

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source=arxiv_source observed=2026-08-14T04:21:09.060339Z digest=sha256:5e34c10532ec7021c2b56b0cc30510d53e66e016b6951014ead73fe104c5b10a

Observation b6156099-0dd0-42c5-89ec-6732ea6a0ba5 · outbound

This paper cites An Introduction to Sequential Monte Carlo.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models An Introduction to Sequential Monte Carlo

Reference 80

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source=arxiv_source observed=2026-08-14T04:21:09.064662Z digest=sha256:5614821de5a58a15c6fc4934aa329ff6c5fbbb056790df9da6f941f21092a7a3

Observation 08f89f45-0601-4a47-a5e0-c40d958665a0 · outbound

This paper cites Elements of Sequential Monte Carlo.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Elements of Sequential Monte Carlo

Reference 81

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source=arxiv_source observed=2026-08-14T04:21:09.069644Z digest=sha256:669a414e18dd34671a10bfe8231d811debf7dbc04c6f1e9ea56d1bedfc699218

Observation 2eb0ddce-896f-4c59-84f8-f2de5fa70498 · outbound

This paper cites Bayesian model comparison and backprop nets.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Bayesian model comparison and backprop nets

Reference 82

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source=arxiv_source observed=2026-08-14T04:21:09.078869Z digest=sha256:bcda4062490e6f09ea33dcd3775fe9fa946e7fe2447c2d69799be0cb6e234746

Observation 4bd18b5f-ab29-4b6f-8379-c83b56f69da0 · outbound

This paper cites Simulation-based inference: A practical guide.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Simulation-based inference: A practical guide

Reference 83

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source=arxiv_source observed=2026-08-14T04:21:09.083992Z digest=sha256:16301a3135e74e67e287d41394520ddcf12819d3f089fb4ef1d40f88674ac664

Observation 8ab2b721-ca2c-4700-a499-98086cbe7a48 · outbound

This paper cites and Xu, Baixuan and Wang, Zhaowei and Cheng, Jiayang and Tsang, Hong Ting and Wang, Weiqi and Bai, Jiaxin and Fang, Tianqing and Song, Yangqiu and Wong, Ginny Y.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models and Xu, Baixuan and Wang, Zhaowei and Cheng, Jiayang and Tsang, Hong Ting and Wang, Weiqi and Bai, Jiaxin and Fang, Tianqing and Song, Yangqiu and Wong, Ginny Y

Reference 84

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source=arxiv_source observed=2026-08-14T04:21:09.088634Z digest=sha256:9505e3f42eb8aa2748436bdcf4036e577f609e54ff99be3a8017e48ab467ba4d

Observation e0194bbf-bbf2-4279-8ba2-8d29eb668cee · outbound

This paper cites , journal =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models , journal =

Reference 85

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source=arxiv_source observed=2026-08-14T04:21:09.093428Z digest=sha256:9d5f8dbed4c99916754841fb64189d9a3e69415a7f4a5f7b50c573da5c1631d0

Observation 0e451368-7ed6-4c3a-af13-1181f8ae4d26 · outbound

This paper cites Understanding world or predicting future? A comprehensive survey of world models.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Understanding world or predicting future? A comprehensive survey of world models

Reference 86

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source=arxiv_source observed=2026-08-14T04:21:09.098513Z digest=sha256:28a3b1543690a91a6fb570328ce30ff795bb88264ea8992174b6870318d530e1

Observation e8ec0b3b-4f0d-478a-acbd-aeb1d72a166d · outbound

This paper cites Why we must break the world.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Why we must break the world

Reference 87

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source=arxiv_source observed=2026-08-14T04:21:09.103860Z digest=sha256:c8971fbb602bb39321f4bb3b34edf8fcef8b4dfce58436499d086a34f35a8d85

Observation 37582673-3072-40c6-8406-51bdcbf1deca · outbound

This paper cites arXiv preprint arXiv:2602.04492 , year =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models arXiv preprint arXiv:2602.04492 , year =

Reference 88

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source=arxiv_source observed=2026-08-14T04:21:09.108392Z digest=sha256:587327a2d0b7bd41d81cb78670d91aea66e780dc21b98281efaf319ba396e4fc

Observation caf51d05-f9f3-42cb-a0c5-a92f475fac43 · outbound

This paper cites Science Robotics , year =.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Science Robotics , year =

Reference 89

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source=arxiv_source observed=2026-08-14T04:21:09.113084Z digest=sha256:6c98e780fb21478b7b3f237c1079f1679683737a327dafe3ae74be3458767aaf

Observation d80d67f1-a4b7-4b5a-b6ff-0ae62df9acd7 · outbound

This paper cites an unresolved cited work.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Unresolved cited work

Reference 90

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source=arxiv_source observed=2026-08-14T04:21:09.117473Z digest=sha256:26760226dfa787b3b7141602a2cf0b27177ca416f2693f13d9d0ba1d9793ba21

Observation a61398be-b4f4-4701-9127-ae1db40cec62 · outbound

This paper cites Automated scientific discovery: From equation discovery to autonomous discovery systems.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Automated scientific discovery: From equation discovery to autonomous discovery systems

Reference 91

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source=arxiv_source observed=2026-08-14T04:21:09.121857Z digest=sha256:5666840d08d454bda916f511c53bb81b435dca4897d12e4d3e34270ee6f21cba

Observation 8383202c-77b0-4cad-a312-09c361ce71da · outbound

This paper cites LLMs for Experiment Design in Scientific Domains: Are We There Yet?.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models LLMs for Experiment Design in Scientific Domains: Are We There Yet?

Reference 92

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source=arxiv_source observed=2026-08-14T04:21:09.126521Z digest=sha256:571902877c88b07693b2175aad2fb2f2533d3dd934b55021d1a8aa65da673a23

Observation 9c5dcce0-0370-4fb3-b4d0-b437b6d51bba · outbound

This paper cites Halstead complexity metric.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Halstead complexity metric

Reference 94

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source=arxiv_source observed=2026-08-14T04:21:09.136448Z digest=sha256:a4bc164a37d677acb2bc9f20f92bf1dd2286c3ccc5cc79e004c946166e0f5055

Observation 4c41e841-8884-4042-b6ef-6cbae4d6da6b · outbound

This paper cites Discrimination among mechanistic models.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Discrimination among mechanistic models

Reference 95

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source=arxiv_source observed=2026-08-14T04:21:09.140742Z digest=sha256:f540d0c05d4ffe4ffafd2aaed708022a1f33bc720fd00d8dcb37d5899baf00f9

Observation 66b6b560-903c-43a1-aff4-e72b271a4aa1 · outbound

This paper cites an unresolved cited work.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Unresolved cited work

Reference 96

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source=arxiv_source observed=2026-08-14T04:21:09.145316Z digest=sha256:138cb1ae0f236d4c5d453f7c26f93d4697da7f44c053f221e3699c8393d2c967

Observation 4e96ff51-a238-4b57-85c8-7093d44d0741 · outbound

This paper cites ATLAS : Active Theory Learning for Automated Science.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models ATLAS : Active Theory Learning for Automated Science

Reference 97

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source=arxiv_source observed=2026-08-14T04:21:09.149402Z digest=sha256:6f2f8aab34b8ef1885c53c7f7e7eeb0c21f76ca5c2313dc64c529b8ea27d73d4

Observation 2cee9fe6-8bc8-4c32-a8b7-4a59d0b4d305 · outbound

This paper cites AI -discovered cognitive models reveal novel insights into human and animal learning.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models AI -discovered cognitive models reveal novel insights into human and animal learning

Reference 98

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source=arxiv_source observed=2026-08-14T04:21:09.153461Z digest=sha256:ae10a2f0ed7b1998fa8ecd654966c1fc0ca9255aa1329e5bc38fa213bc8bf525

Observation 20fc3ef3-dbf7-4929-9d2a-6ed6fb72cda6 · outbound

This paper cites Interpreting emergent planning in model-free reinforcement learning.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Interpreting emergent planning in model-free reinforcement learning

Reference 99

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source=arxiv_source observed=2026-08-14T04:21:09.157597Z digest=sha256:0863d366abcd95e089b5c468ecf0919350f188ece1453ce45ebae2e2a52e0c51

Observation 3a3e5d99-7dde-41ca-b8a4-0c4fd2db2e27 · outbound

This paper cites auto-psych: Automating the science of mind using agent-driven theory discovery and experimentation.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models auto-psych: Automating the science of mind using agent-driven theory discovery and experimentation

Reference 100

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source=arxiv_source observed=2026-08-14T04:21:09.161501Z digest=sha256:611157c863c2e3feee342e38d0e1d1cfa50cc33c92875d523eec1f1f7e1327f4

Observation 7b95effe-908f-4903-b8a7-cb1987db07cd · outbound

This paper cites Closing the loop to discover psychological theories with an Automated Cognitive Scientist.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Closing the loop to discover psychological theories with an Automated Cognitive Scientist

Reference 101

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source=arxiv_source observed=2026-08-14T04:21:09.165446Z digest=sha256:3474c9aa914a9f3def11d7eec5be1de087641cf7e5754cf907de4c5f46dac60f

Observation 2b099fd4-7f81-4040-9eef-069d3e7401ef · outbound

This paper cites Successful automatic model discovery can produce false mechanisms.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Successful automatic model discovery can produce false mechanisms

Reference 102

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source=arxiv_source observed=2026-08-14T04:21:09.170610Z digest=sha256:c027adb3c5b52466c9af6d55b467369023d9a9205793baf8b5c9ec800c14b40e

Observation 1618138f-ab6d-440a-94a3-976d33b92551 · outbound

This paper cites An AI system to help scientists write expert-level empirical software.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models An AI system to help scientists write expert-level empirical software

Reference 103

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source=arxiv_source observed=2026-08-14T04:21:09.175300Z digest=sha256:0c25f71ed3ce92af11c96d3571b6b59b61a21ead9b9029db8bd6ae0f660e3aa1

Observation 54607410-ac05-41cd-8b19-33bfb035a807 · outbound

This paper cites Physical Review E , volume=.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Physical Review E , volume=

Reference 104

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source=arxiv_source observed=2026-08-14T04:21:09.179892Z digest=sha256:6f4cac01aa2a9d1d8bacc4d5dc003e620b443610f53d60ca651e79c627da481e

Observation 64bc4038-3fae-418f-96bf-0621ed3c26c2 · outbound

This paper cites PLoS Computational Biology , volume=.

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models PLoS Computational Biology , volume=

Reference 105

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source=arxiv_source observed=2026-08-14T04:21:09.184325Z digest=sha256:b3d39e315bd55fa377376a7ec5eb3961454cdadc437213fc7d46f0677b323704

Pith citing papers

No inbound Pith citation observations are available.