Paper Citation Record · LEDGER

Measuring Weak-to-Strong Legibility of Reasoning Models

As of 23 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 2 inbound Pith citation observations for arXiv:2603.20508.

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

pith.paper-citation-record.v1
2603.20508 v2

Coverage vector

measured 62 of 62 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-07-13T21:27:25.757351Z

measured 64 of 64 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T15:22:55.664897Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T05:51:08.265461Z

Reference resolution

62 of 62 outbound references displayed

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External citation measurements

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Outbound references

Observation 6b382bfc-c27a-44b2-9049-e2b1973b49e2 · outbound

This paper cites an unresolved cited work.

Measuring Weak-to-Strong Legibility of Reasoning Models Unresolved cited work

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation f7a3c8af-0d98-4df9-a460-8e844eb8520a · outbound

This paper cites Gradient based sample selection for online continual learning.Advances in neural information processing sys- tems, 32, 2019.

Measuring Weak-to-Strong Legibility of Reasoning Models Gradient based sample selection for online continual learning.Advances in neural information processing sys- tems, 32, 2019

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation d928332c-6f55-4737-8a5c-0afae09b7a46 · outbound

This paper cites Monitoring the mammalian fauna of ur- ban areas using remote cameras and citizen science.Journal of Urban Ecology, 4(1):juy002, 2018.

Measuring Weak-to-Strong Legibility of Reasoning Models Monitoring the mammalian fauna of ur- ban areas using remote cameras and citizen science.Journal of Urban Ecology, 4(1):juy002, 2018

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation f883d107-5597-44e4-9b59-01853e4ec7d7 · outbound

This paper cites The MegaDetector: Large-scale deployment of computer vision for conservation and biodiversity monitor- ing.

Measuring Weak-to-Strong Legibility of Reasoning Models The MegaDetector: Large-scale deployment of computer vision for conservation and biodiversity monitor- ing

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 0e6ee8ae-cf4d-4de6-99e0-d60a5da99058 · outbound

This paper cites Recognition in terra incognita.

Measuring Weak-to-Strong Legibility of Reasoning Models Recognition in terra incognita

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 8ebd5ff6-236f-4e2f-aa96-f635b458239d · outbound

This paper cites Recognition in terra incognita.

Measuring Weak-to-Strong Legibility of Reasoning Models Recognition in terra incognita

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation c2c1e84c-af88-47b4-9bd4-07e809ffb951 · outbound

This paper cites The iWildCam 2018 Challenge Dataset.

Measuring Weak-to-Strong Legibility of Reasoning Models The iWildCam 2018 Challenge Dataset

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 92e32500-717d-4423-93db-83cb643ae9c7 · outbound

This paper cites The iWildCam 2021 Competition Dataset.

Measuring Weak-to-Strong Legibility of Reasoning Models The iWildCam 2021 Competition Dataset

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation bbd7e06d-f42f-40aa-a23c-9d3f8f993a17 · outbound

This paper cites Deep learning-based ecological analysis of camera trap images is impacted by training data quality and quantity.

Measuring Weak-to-Strong Legibility of Reasoning Models Deep learning-based ecological analysis of camera trap images is impacted by training data quality and quantity

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 6f0dc4e2-bff9-4e7f-9c4e-2b935747a5b0 · outbound

This paper cites Pelagic Publishing Ltd, 2016.

Measuring Weak-to-Strong Legibility of Reasoning Models Pelagic Publishing Ltd, 2016

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation f677d566-2910-4f7c-9208-0d90d50b0cbf · outbound

This paper cites Automated wildlife image classification: An active learning tool for ecological applica- tions.Ecological Informatics, 77:102231, 2023.

Measuring Weak-to-Strong Legibility of Reasoning Models Automated wildlife image classification: An active learning tool for ecological applica- tions.Ecological Informatics, 77:102231, 2023

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 0ef351b1-0919-493b-a423-92293dc37fc2 · outbound

This paper cites Class-balanced loss based on effective number of samples, 2019.

Measuring Weak-to-Strong Legibility of Reasoning Models Class-balanced loss based on effective number of samples, 2019

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 39271814-7e61-4969-ace6-b8ccf425f6bc · outbound

This paper cites Class-balanced loss based on effective number of samples.

Measuring Weak-to-Strong Legibility of Reasoning Models Class-balanced loss based on effective number of samples

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 091daa16-a9e6-44d8-ad9c-b35a0a0d185c · outbound

This paper cites A continual learning survey: Defying for- getting in classification tasks.IEEE transactions on pattern analysis and machine intelligence, 44(7):3366–3385, 2021.

Measuring Weak-to-Strong Legibility of Reasoning Models A continual learning survey: Defying for- getting in classification tasks.IEEE transactions on pattern analysis and machine intelligence, 44(7):3366–3385, 2021

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 54f5a203-bae5-43c0-bccc-11b213267009 · outbound

This paper cites Multimodal Foundation Models for Zero-shot Animal Species Recognition in Camera Trap Images.

Measuring Weak-to-Strong Legibility of Reasoning Models Multimodal Foundation Models for Zero-shot Animal Species Recognition in Camera Trap Images

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 3b121c01-963a-4459-88f5-21037137c4da · outbound

This paper cites A brief review of domain adaptation.

Measuring Weak-to-Strong Legibility of Reasoning Models A brief review of domain adaptation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation e84ad5bc-fb64-4865-b263-f961dbb02494 · outbound

This paper cites Wildclip: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models.International Journal of Computer Vision, 132(9): 3770–3786, 2024.

Measuring Weak-to-Strong Legibility of Reasoning Models Wildclip: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models.International Journal of Computer Vision, 132(9): 3770–3786, 2024

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 9ed068d9-37db-4e28-8b12-35f469e876d9 · outbound

This paper cites Geodesic flow kernel for unsupervised domain adaptation.

Measuring Weak-to-Strong Legibility of Reasoning Models Geodesic flow kernel for unsupervised domain adaptation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 8361d737-2f48-4fdc-b965-43c569cee17a · outbound

This paper cites Bioclip 2: Emergent properties from scaling hierarchi- cal contrastive learning.arXiv preprint arXiv:2505.23883,.

Measuring Weak-to-Strong Legibility of Reasoning Models Bioclip 2: Emergent properties from scaling hierarchi- cal contrastive learning.arXiv preprint arXiv:2505.23883,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 19109b91-8462-4107-ac7c-b93705cebec4 · outbound

This paper cites A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.

Measuring Weak-to-Strong Legibility of Reasoning Models A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 5ae65cda-5fa4-4878-99f9-31c403d79f0c · outbound

This paper cites Parameter-efficient transfer learning for nlp, 2019.

Measuring Weak-to-Strong Legibility of Reasoning Models Parameter-efficient transfer learning for nlp, 2019

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 1fb0ec56-b424-4648-8e58-cbd5400a86a7 · outbound

This paper cites Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022.

Measuring Weak-to-Strong Legibility of Reasoning Models Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022

Reference 22

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Observation 4a92c720-8da8-4529-9bf0-7d21ee1d266b · outbound

This paper cites Idaho camera traps.https://lila.science/datasets/idaho- camera-traps/.

Measuring Weak-to-Strong Legibility of Reasoning Models Idaho camera traps.https://lila.science/datasets/idaho- camera-traps/

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 9b6929e2-cf59-43a0-a9de-c8f38f520ecf · outbound

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Measuring Weak-to-Strong Legibility of Reasoning Models Northern and central annamites camera traps 2.0

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 69044240-6b5a-40cc-bc23-f30bf1638dba · outbound

This paper cites Vi- sual prompt tuning, 2022.

Measuring Weak-to-Strong Legibility of Reasoning Models Vi- sual prompt tuning, 2022

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation a5e8a3bf-b003-4ff4-aedf-b78e1b7e4c3e · outbound

This paper cites Wilds: A benchmark of in-the- wild distribution shifts.

Measuring Weak-to-Strong Legibility of Reasoning Models Wilds: A benchmark of in-the- wild distribution shifts

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 124719a8-8276-4dca-bc31-303342ee00cd · outbound

This paper cites Microsoft coco: Common objects in context.

Measuring Weak-to-Strong Legibility of Reasoning Models Microsoft coco: Common objects in context

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation d26c9eb6-25c5-45a1-9e66-6f42a92b7eb0 · outbound

This paper cites Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning.

Measuring Weak-to-Strong Legibility of Reasoning Models Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 724bfdb0-8c5b-43c5-91a9-d85bbfa7e3c2 · outbound

This paper cites Online continual learning in image classification: An empirical survey.Neurocomputing, 469:28–51, 2022.

Measuring Weak-to-Strong Legibility of Reasoning Models Online continual learning in image classification: An empirical survey.Neurocomputing, 469:28–51, 2022

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation fb108663-5028-464e-91fc-fdd42e3bacb3 · outbound

This paper cites Fine-tuning is fine, if cali- brated.Advances in Neural Information Processing Systems, 37:136084–136119, 2024.

Measuring Weak-to-Strong Legibility of Reasoning Models Fine-tuning is fine, if cali- brated.Advances in Neural Information Processing Systems, 37:136084–136119, 2024

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation b08828e8-fdd0-4c68-8122-91989891ca4a · outbound

This paper cites Lessons and insights from a unifying study of parameter-efficient fine-tuning (peft) in visual recognition.

Measuring Weak-to-Strong Legibility of Reasoning Models Lessons and insights from a unifying study of parameter-efficient fine-tuning (peft) in visual recognition

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 4f106b69-e83b-4ab4-a6bb-4f1fd0dee1d8 · outbound

This paper cites Two-phase training mitigates class imbalance for camera trap image classification with CNNs.

Measuring Weak-to-Strong Legibility of Reasoning Models Two-phase training mitigates class imbalance for camera trap image classification with CNNs

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 2be7085d-d56c-4cc4-900a-4d2c5c6f01a9 · outbound

This paper cites Trail camera images of new zealand animals.https://lila.science/datasets/nz- trailcams.

Measuring Weak-to-Strong Legibility of Reasoning Models Trail camera images of new zealand animals.https://lila.science/datasets/nz- trailcams

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Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 9500d4b4-adfd-434b-9c4b-b7cc3652747e · outbound

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Measuring Weak-to-Strong Legibility of Reasoning Models Unresolved cited work

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Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 6a26b9a4-9a75-41e2-b703-49ff82e7f943 · outbound

This paper cites Snap- shot safari: A large-scale collaborative to monitor africa’s remarkable biodiversity.South African Journal of Science, 117(1-2):1–4, 2021.

Measuring Weak-to-Strong Legibility of Reasoning Models Snap- shot safari: A large-scale collaborative to monitor africa’s remarkable biodiversity.South African Journal of Science, 117(1-2):1–4, 2021

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Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 32e51f80-bb91-426d-bba5-f22a98e78666 · outbound

This paper cites Har- nessing artificial intelligence to fill global shortfalls in biodi- versity knowledge.Nature Reviews Biodiversity, pages 1–17,.

Measuring Weak-to-Strong Legibility of Reasoning Models Har- nessing artificial intelligence to fill global shortfalls in biodi- versity knowledge.Nature Reviews Biodiversity, pages 1–17,

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Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 9d46db28-8e34-40bb-b2a9-3f586678f1ed · outbound

This paper cites Learning transferable visual models from natural language supervision, 2021.

Measuring Weak-to-Strong Legibility of Reasoning Models Learning transferable visual models from natural language supervision, 2021

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Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation effc5770-228a-4645-859c-53c4b55dfc9c · outbound

This paper cites Balanced meta-softmax for long-tailed visual recog- nition.Advances in neural information processing systems, 33:4175–4186, 2020.

Measuring Weak-to-Strong Legibility of Reasoning Models Balanced meta-softmax for long-tailed visual recog- nition.Advances in neural information processing systems, 33:4175–4186, 2020

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Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation de5faf30-5719-465c-a468-fba0f843d7e8 · outbound

This paper cites Balanced meta-softmax for long-tailed visual recognition, 2020.

Measuring Weak-to-Strong Legibility of Reasoning Models Balanced meta-softmax for long-tailed visual recognition, 2020

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Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation c89b0ec0-3c27-4420-b992-0e89525ad570 · outbound

This paper cites A broad review on class imbalance learning techniques.Applied Soft Computing, 143:110415, 2023.

Measuring Weak-to-Strong Legibility of Reasoning Models A broad review on class imbalance learning techniques.Applied Soft Computing, 143:110415, 2023

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Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 307450fb-991d-4c8a-9754-c1f33cec41c7 · outbound

This paper cites Extending the WILDS Benchmark for Unsupervised Adaptation.

Measuring Weak-to-Strong Legibility of Reasoning Models Extending the WILDS Benchmark for Unsupervised Adaptation

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Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation fd7ece1f-8963-442a-a720-51dd83b7efd2 · outbound

This paper cites Catalog: A camera trap language-guided contrastive learning model.

Measuring Weak-to-Strong Legibility of Reasoning Models Catalog: A camera trap language-guided contrastive learning model

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Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 424b2a28-4813-491f-8161-6fa082f80950 · outbound

This paper cites Online class- incremental continual learning with adversarial shapley value.

Measuring Weak-to-Strong Legibility of Reasoning Models Online class- incremental continual learning with adversarial shapley value

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Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 63a47c11-c66a-4560-881f-ffc885162f46 · outbound

This paper cites Domain adaptation: challenges, methods, datasets, and applications.IEEE access, 11:6973–7020,.

Measuring Weak-to-Strong Legibility of Reasoning Models Domain adaptation: challenges, methods, datasets, and applications.IEEE access, 11:6973–7020,

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Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 6b56dbb9-3f05-427a-aa51-3387e9161199 · outbound

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

Measuring Weak-to-Strong Legibility of Reasoning Models Bioclip: A vision foundation model for the tree of life

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Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 80d5c421-dfae-424d-89a8-0fbfd4812da1 · outbound

This paper cites Snapshot serengeti, high-frequency annotated camera trap images of 40 mammalian species in an african savanna.Scientific data, 2(1):1–14, 2015.

Measuring Weak-to-Strong Legibility of Reasoning Models Snapshot serengeti, high-frequency annotated camera trap images of 40 mammalian species in an african savanna.Scientific data, 2(1):1–14, 2015

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Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 327a2fb6-2f69-40f7-baaf-77c3d7e3095a · outbound

This paper cites Machine learning to classify ani- mal species in camera trap images: Applications in ecology.

Measuring Weak-to-Strong Legibility of Reasoning Models Machine learning to classify ani- mal species in camera trap images: Applications in ecology

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Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 033d77ed-4c81-4c2b-adf6-9c7fa3dbef63 · outbound

This paper cites Use of camera traps for wildlife studies: a review.Biotechnologie, Agronomie, Soci ´et´e et En- vironnement, 18(3), 2014.

Measuring Weak-to-Strong Legibility of Reasoning Models Use of camera traps for wildlife studies: a review.Biotechnologie, Agronomie, Soci ´et´e et En- vironnement, 18(3), 2014

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Observation f0d6b01f-8c25-448c-89c4-6395017378a5 · outbound

This paper cites Holistic trans- fer: towards non-disruptive fine-tuning with partial target data.Advances in Neural Information Processing Systems, 36:29149–29173, 2023.

Measuring Weak-to-Strong Legibility of Reasoning Models Holistic trans- fer: towards non-disruptive fine-tuning with partial target data.Advances in Neural Information Processing Systems, 36:29149–29173, 2023

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Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00a06f6f-6c55-4c10-88bf-4a032da31134 · outbound

This paper cites Perspectives in machine learning for wildlife conservation.Nature communications, 13(1):792,.

Measuring Weak-to-Strong Legibility of Reasoning Models Perspectives in machine learning for wildlife conservation.Nature communications, 13(1):792,

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Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation a0f1bad4-f852-4e5f-bcc8-f8eda0f88268 · outbound

This paper cites The inaturalist species classification and de- tection dataset.

Measuring Weak-to-Strong Legibility of Reasoning Models The inaturalist species classification and de- tection dataset

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Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation c0ea4cd5-e9a9-43f0-b852-578f7a56ebee · outbound

This paper cites Reliable and efficient integration of ai into camera traps for smart wildlife monitoring based on continual learning.Eco- logical Informatics, 83:102815, 2024.

Measuring Weak-to-Strong Legibility of Reasoning Models Reliable and efficient integration of ai into camera traps for smart wildlife monitoring based on continual learning.Eco- logical Informatics, 83:102815, 2024

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Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 7b545981-edbb-4e7c-a404-58cae6248175 · outbound

This paper cites An evaluation of platforms for processing camera-trap data using artificial intelligence.

Measuring Weak-to-Strong Legibility of Reasoning Models An evaluation of platforms for processing camera-trap data using artificial intelligence

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Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 9c22bffe-c8c7-44c2-b187-e772342368bc · outbound

This paper cites Robust fine-tuning of zero-shot models.

Measuring Weak-to-Strong Legibility of Reasoning Models Robust fine-tuning of zero-shot models

Reference 54

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation ce2f2c02-772f-4ddb-a214-3f135baff7b7 · outbound

This paper cites Generalized out-of-distribution detection: A survey.Inter- national Journal of Computer Vision, 132(12):5635–5662,.

Measuring Weak-to-Strong Legibility of Reasoning Models Generalized out-of-distribution detection: A survey.Inter- national Journal of Computer Vision, 132(12):5635–5662,

Reference 55

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Observation d853b6a5-a23c-4f50-8211-f4287a8d41c6 · outbound

This paper cites Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning.

Measuring Weak-to-Strong Legibility of Reasoning Models Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning

Reference 56

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Observation bb3dfd3b-78bb-4ac2-9d0f-374ae0b309f7 · outbound

This paper cites Pro- crustean training for imbalanced deep learning.

Measuring Weak-to-Strong Legibility of Reasoning Models Pro- crustean training for imbalanced deep learning

Reference 57

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Observation b12ba672-3413-47d9-b723-ffe039f8fe33 · outbound

This paper cites Identifying and compensating for feature deviation in imbalanced deep learning, 2022.

Measuring Weak-to-Strong Legibility of Reasoning Models Identifying and compensating for feature deviation in imbalanced deep learning, 2022

Reference 58

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 455b9b79-5444-4dd8-84cb-efa8d91f99ac · outbound

This paper cites Automated identification of animal species in camera trap images.

Measuring Weak-to-Strong Legibility of Reasoning Models Automated identification of animal species in camera trap images

Reference 59

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Observation d073de87-4fb1-424d-94b1-0ebc43c9dd62 · outbound

This paper cites Deep long-tailed learning: A survey.IEEE transactions on pattern analysis and machine intelligence, 45(9):10795–10816, 2023.

Measuring Weak-to-Strong Legibility of Reasoning Models Deep long-tailed learning: A survey.IEEE transactions on pattern analysis and machine intelligence, 45(9):10795–10816, 2023

Reference 60

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Observation 775e97b2-bb63-4cd5-bb5b-9999f1e4fdb3 · outbound

This paper cites Domain generalization: A survey.IEEE transactions on pattern analysis and machine intelligence, 45(4):4396–4415, 2022.

Measuring Weak-to-Strong Legibility of Reasoning Models Domain generalization: A survey.IEEE transactions on pattern analysis and machine intelligence, 45(4):4396–4415, 2022

Reference 61

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Observation 3d4e4d39-cf29-4244-be7a-76d128aebda4 · outbound

This paper cites Class incremental learning for wildlife biodiversity monitoring in camera trap images.Ecological Informatics, 71:101760, 2022.

Measuring Weak-to-Strong Legibility of Reasoning Models Class incremental learning for wildlife biodiversity monitoring in camera trap images.Ecological Informatics, 71:101760, 2022

Reference 62

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

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Pith citing papers

Observation b69ac798-fe59-4f60-8192-591d2afd54a0 · inbound

CLORE: Content-Level Optimization for Reasoning Efficiency cites this paper.

CLORE: Content-Level Optimization for Reasoning Efficiency Measuring Weak-to-Strong Legibility of Reasoning Models

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-06-03T02:05:14.472947Z

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Observation 4a502dba-f086-44ab-a5fb-40a1dea5224f · inbound

How Much Does a Reasoning Summary Reveal? An Observability Ladder for Large Language Models cites this paper.

How Much Does a Reasoning Summary Reveal? An Observability Ladder for Large Language Models Measuring Weak-to-Strong Legibility of Reasoning Models

Reference 2026

Resolution: unresolved; no resolver link, observed 2026-08-04T15:22:55.664897Z

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