Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T04:21:09.184325Z
Paper Citation Record · LEDGER
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.
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Source: paper_references, paper_reference_links, observed 2026-08-14T04:21:09.184325Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
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A source-named dated measurement, never combined with another source.
Source: cited_works
100 of 183 outbound references displayed
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Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models ATLAS: Active Theory Learning for Automated Science
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Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Murphy , title =
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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
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Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Lauffenburger and Garry P
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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 =
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Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Halpern , title =
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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
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Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Interventions, Where and How?
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Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Ivanova and Ilyas Malik and Tom Rainforth , title =
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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 =
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