Paper Citation Record · LEDGER

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery

As of 23 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2502.10060.

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

pith.paper-citation-record.v1
2502.10060 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

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measured 45 of 45 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

45 of 45 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 938925cf-5fd4-44c8-a20a-1b50c8526965 · outbound

This paper cites Evaluation of output embeddings for fine- grained image classification.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Evaluation of output embeddings for fine- grained image classification

Reference 1

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Observation 1b3ffa03-50b7-4b55-aefb-6505b590ebfb · outbound

This paper cites Bootstrapping variational information pursuit with large language and vision models for interpretable image classifi- cation.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Bootstrapping variational information pursuit with large language and vision models for interpretable image classifi- cation

Reference 2

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Observation 1281f613-706e-4257-9860-e8cfda8416ee · outbound

This paper cites In- formation maximization perspective of orthogonal matching pursuit with applications to explainable ai.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery In- formation maximization perspective of orthogonal matching pursuit with applications to explainable ai

Reference 3

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Observation e295a511-7ca1-41df-a580-912146eff841 · outbound

This paper cites Evolving interpretable visual classifiers with large language models.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Evolving interpretable visual classifiers with large language models

Reference 4

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Observation 32f493ed-b053-4f50-b246-68e0190e2b85 · outbound

This paper cites Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl

Reference 5

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Observation 4827d74f-ba51-4e87-9f69-c03da1065063 · outbound

This paper cites Neural logic machines.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Neural logic machines

Reference 6

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Observation 57d98368-7b94-4cc5-bf69-2062cbe0bfdc · outbound

This paper cites Sentinel-2: Esa’s optical high-resolution mission for gmes operational services.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Sentinel-2: Esa’s optical high-resolution mission for gmes operational services

Reference 7

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Observation 8577c0b6-6c87-43e5-8bec-2fb94b9b3529 · outbound

This paper cites The global ecosystem dynamics investigation: High-resolution laser ranging of the earth’s forests and topography.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery The global ecosystem dynamics investigation: High-resolution laser ranging of the earth’s forests and topography

Reference 8

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Observation 9f8dea01-1c28-4ae6-898a-b0d4cf6f0d47 · outbound

This paper cites The Llama 3 Herd of Models.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery The Llama 3 Herd of Models

Reference 9

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Observation 2d364445-a0be-492f-bac6-510a6a575eb3 · outbound

This paper cites Dreamcoder: Bootstrapping inductive program synthesis with wake-sleep library learning.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Dreamcoder: Bootstrapping inductive program synthesis with wake-sleep library learning

Reference 10

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Observation dfc594fb-b9b6-4f2b-bc1d-c3ae89b397d3 · outbound

This paper cites Learning visual at- tributes.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Learning visual at- tributes

Reference 11

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Observation 5035b650-f9ed-48e8-af39-13c5c9385d45 · outbound

This paper cites Symbolic Regression with a Learned Concept Library.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Symbolic Regression with a Learned Concept Library

Reference 12

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Observation 95a75849-88b5-4b72-acaf-830751a4a74b · outbound

This paper cites Visual pro- gramming: Compositional visual reasoning without training.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Visual pro- gramming: Compositional visual reasoning without training

Reference 13

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Observation 2df81906-a5b1-402b-9efb-525cf958e1ab · outbound

This paper cites Visual program- ming: Compositional visual reasoning without training.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Visual program- ming: Compositional visual reasoning without training

Reference 14

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Observation c9a95512-8f40-40ca-b292-f72148bef616 · outbound

This paper cites LLMs as Visual Explainers: Advancing Image Classification with Evolving Visual Descriptions.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery LLMs as Visual Explainers: Advancing Image Classification with Evolving Visual Descriptions

Reference 15

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Observation 116a3f71-a816-4d43-8f7a-4c3c88a4a9fc · outbound

This paper cites Deep residual learning for image recognition.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Deep residual learning for image recognition

Reference 16

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Observation f29371c8-bf0d-4773-bb29-f1218734b44e · outbound

This paper cites Part- stacked cnn for fine-grained visual categorization.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Part- stacked cnn for fine-grained visual categorization

Reference 17

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Observation 59de58a4-b81b-4f1f-8d7e-94aefb1801ec · outbound

This paper cites Inferring and executing programs for visual reasoning.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Inferring and executing programs for visual reasoning

Reference 18

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Observation 2b5cedd3-af7d-4c02-8934-b0cbd34cfd3d · outbound

This paper cites Semantic autoencoder for zero-shot learning.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Semantic autoencoder for zero-shot learning

Reference 19

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Observation da9ef2f5-ecc4-4567-9e89-67b65b60e345 · outbound

This paper cites Concept bottleneck models.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Concept bottleneck models

Reference 20

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Observation 16f9ac4f-f624-42d0-8f09-a0e2fc28f631 · outbound

This paper cites Efficient memory management for large language model serving with pagedattention.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Efficient memory management for large language model serving with pagedattention

Reference 21

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Observation cd775304-7475-4c8a-95ae-048d80701f77 · outbound

This paper cites Attribute-based classification for zero-shot visual object categorization.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Attribute-based classification for zero-shot visual object categorization

Reference 22

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Observation b77fe49a-8c1f-4ed3-b66c-e971179a9c92 · outbound

This paper cites Automated Statistical Model Discovery with Language Models.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Automated Statistical Model Discovery with Language Models

Reference 23

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Observation 196980bf-41ac-46f5-84bd-8cf4b96426aa · outbound

This paper cites Code as policies: Language model programs for embodied control.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Code as policies: Language model programs for embodied control

Reference 24

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Observation d9593c4a-3337-4b4a-b807-5fa339f51913 · outbound

This paper cites Interpretable scientific dis- covery with symbolic regression: a review.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Interpretable scientific dis- covery with symbolic regression: a review

Reference 25

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Observation b9393286-ebb1-4bd0-9069-90957ee88f39 · outbound

This paper cites Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignment.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignment

Reference 26

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Observation c7519d57-b966-4091-a4b4-8b6421c32b09 · outbound

This paper cites The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision

Reference 27

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Observation e999fed8-68b8-4000-8592-b48cd54fe6cd · outbound

This paper cites Visual classification via description from large language models.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Visual classification via description from large language models

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:36:04.456079Z

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Observation ead1e705-7b00-46d3-8d1c-4132726bd16a · outbound

This paper cites In-context symbolic regression: Lever- aging large language models for function discovery.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery In-context symbolic regression: Lever- aging large language models for function discovery

Reference 29

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Observation ac1b50ba-0723-401a-a5bf-d9eae7bc0a39 · outbound

This paper cites Fine- grained population mapping from coarse census counts and open geodata.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Fine- grained population mapping from coarse census counts and open geodata

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:36:04.437681Z

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Observation 299c08b4-49d3-44dc-b2de-376d6be57ec5 · outbound

This paper cites High-resolution population maps derived from sentinel-1 and sentinel-2.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery High-resolution population maps derived from sentinel-1 and sentinel-2

Reference 31

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Observation acd22e4c-2abb-4e6c-9072-5a1ef3290d3e · outbound

This paper cites Above ground car- bon biomass estimate with physics-informed deep network.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Above ground car- bon biomass estimate with physics-informed deep network

Reference 32

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Observation 30ba98af-548c-4960-9802-2917cfb7887a · outbound

This paper cites Label-Free Concept Bottleneck Models.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Label-Free Concept Bottleneck Models

Reference 33

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Observation eff5c468-d1d3-4614-a2d9-d30867ff4ee3 · outbound

This paper cites What does a platypus look like? generating customized prompts for zero-shot image classification.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery What does a platypus look like? generating customized prompts for zero-shot image classification

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:36:04.413038Z

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Observation ef58340c-f95b-462c-b5f1-89e9c00a031d · outbound

This paper cites LLM-SR: Scientific Equation Discovery via Programming with Large Language Models.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery LLM-SR: Scientific Equation Discovery via Programming with Large Language Models

Reference 35

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Observation bdbe134c-657e-4060-a465-79056fe97a0f · outbound

This paper cites Bioclip: A vision foundation model for the tree of life.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Bioclip: A vision foundation model for the tree of life

Reference 36

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Observation f42f93f6-cf78-4a82-b809-f57de1b3cf45 · outbound

This paper cites Vipergpt: Visual inference via python execution for reasoning.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Vipergpt: Visual inference via python execution for reasoning

Reference 37

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Observation 3acbd599-b7c0-499f-bae9-a8e0a4efbee7 · outbound

This paper cites Revisiting pose-normalization for fine-grained few-shot recognition.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Revisiting pose-normalization for fine-grained few-shot recognition

Reference 38

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Observation 761f6de1-53d0-49a5-b2e0-76e2e1579f48 · outbound

This paper cites American community sur- vey 5-year estimates.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery American community sur- vey 5-year estimates

Reference 39

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Observation ca4ca852-40a1-4441-b1b7-c4ce1fd90202 · outbound

This paper cites Openstreetmap: Challenges and oppor- tunities in machine learning and remote sensing.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Openstreetmap: Challenges and oppor- tunities in machine learning and remote sensing

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:36:04.372189Z

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Observation a901e69a-fc9f-40e8-b0d6-e0aa91272e2c · outbound

This paper cites MAPPING POVERTY WITH SATELLITE IM- AGERY.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery MAPPING POVERTY WITH SATELLITE IM- AGERY

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:36:04.363055Z

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Observation cf62f392-6a6a-41cb-90ca-2be8aeac981a · outbound

This paper cites Language in a bottle: Language model guided concept bottlenecks for in- terpretable image classification.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Language in a bottle: Language model guided concept bottlenecks for in- terpretable image classification

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:36:04.354282Z

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Observation 998411d0-846e-47bb-bccc-3841ecddc3df · outbound

This paper cites SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning

Reference 43

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Observation 09c47092-f16e-45b9-8e9b-4e1097079255 · outbound

This paper cites MuseCL: Predicting urban so- cioeconomic indicators via multi-semantic contrastive learn- ing.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery MuseCL: Predicting urban so- cioeconomic indicators via multi-semantic contrastive learn- ing

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:36:04.346424Z

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Observation ed60d06b-c5f4-4dfd-a7f9-9e92a4940977 · outbound

This paper cites Interpretable basis decomposition for visual explanation.

DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery Interpretable basis decomposition for visual explanation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:36:04.338264Z

Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.