Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-05T16:47:46.954911Z
Paper Citation Record · LEDGER
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning
As of 18 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 0 inbound Pith citation observations for arXiv:2508.17751.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
61 of 61 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 690b37ac-af80-40b6-b9ff-c0b4e5ff506d · outbound
This paper cites Phi-3 technical report: A highly capable language model locally on your phone.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Phi-3 technical report: A highly capable language model locally on your phone
Reference 1
- Resolution
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Observation 3e133c2f-9471-41de-8ef7-c629f71b1092 · outbound
This paper cites Evaluating CLIP: Towards characterization of broader capabilities and downstream implications, 2021.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Evaluating CLIP: Towards characterization of broader capabilities and downstream implications, 2021
Reference 2
- Resolution
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Observation a2c210af-ba09-4750-bf09-2ab72890aaa8 · outbound
This paper cites Flamingo: a visual language model for few-shot learning.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Flamingo: a visual language model for few-shot learning
Reference 3
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Observation 71740ef9-9217-4432-b804-eb1b0e82cddb · outbound
This paper cites VQA: Visual question answering.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning VQA: Visual question answering
Reference 4
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Observation 99639337-8d69-4ade-ab83-ec928676e03e · outbound
This paper cites Fair- ness and Machine Learning: Limitations and Opportunities.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Fair- ness and Machine Learning: Limitations and Opportunities
Reference 5
- Resolution
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Source-reported events for the cited work
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Observation e5b1bc34-8efe-474d-a690-890c8090f34f · outbound
This paper cites A prompt array keeps the bias away: Debiasing vision-language models with adversar- ial learning.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning A prompt array keeps the bias away: Debiasing vision-language models with adversar- ial learning
Reference 6
- Resolution
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Observation 71ceb0d1-9e11-4023-9308-15c6d605d378 · outbound
This paper cites Multimodal datasets: misogyny, pornography, and ma- lignant stereotypes, 2021.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Multimodal datasets: misogyny, pornography, and ma- lignant stereotypes, 2021
Reference 7
- Resolution
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Observation 8774dc7b-787b-4e18-b780-e7562cfc3856 · outbound
This paper cites In- structPix2Pix: Learning to follow image editing instructions.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning In- structPix2Pix: Learning to follow image editing instructions
Reference 8
- Resolution
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Source-reported events for the cited work
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Observation 0dedbf4e-4fa4-41f3-8671-b6b563d5c599 · outbound
This paper cites Evaluating bias and fairness in gender- neutral pretrained vision-and-language models.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Evaluating bias and fairness in gender- neutral pretrained vision-and-language models
Reference 9
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Observation 3f3b40a7-5d4e-4449-b0aa-2f19b3cd6b3f · outbound
This paper cites Microsoft COCO captions: Data collection and evaluation server, 2015.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Microsoft COCO captions: Data collection and evaluation server, 2015
Reference 10
- Resolution
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Source-reported events for the cited work
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Observation 0600c703-b863-4379-8415-84bef30e5f66 · outbound
This paper cites Reproducible scal- ing laws for contrastive language-image learning.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Reproducible scal- ing laws for contrastive language-image learning
Reference 11
- Resolution
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Source-reported events for the cited work
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Observation cea08688-2b89-4c14-9eda-2549f6339d92 · outbound
This paper cites Debiasing vision-language models via biased prompts, 2023.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Debiasing vision-language models via biased prompts, 2023
Reference 12
- Resolution
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Observation ddfb0b9b-8d1a-4eff-9ec7-9f8e37a89f69 · outbound
This paper cites Utility-fairness trade-offs and how to find them.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Utility-fairness trade-offs and how to find them
Reference 13
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Source-reported events for the cited work
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Observation 2d53ff5b-1b9a-4628-ac91-95de5a41db94 · outbound
This paper cites FairerCLIP: Debiasing clip’s zero-shot predictions us- ing functions in RKHSs.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning FairerCLIP: Debiasing clip’s zero-shot predictions us- ing functions in RKHSs
Reference 14
- Resolution
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Source-reported events for the cited work
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Observation f36202a8-e94f-4560-940e-d77386da8da0 · outbound
This paper cites BERT: Pre-training of deep bidirectional Trans- formers for language understanding.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning BERT: Pre-training of deep bidirectional Trans- formers for language understanding
Reference 15
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Source-reported events for the cited work
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Observation aac3223f-945d-41ff-b46f-561a1f84f088 · outbound
This paper cites From captions to vi- sual concepts and back.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning From captions to vi- sual concepts and back
Reference 16
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Source-reported events for the cited work
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Observation 0e99166c-7f2a-4ecf-8640-f7464b95db92 · outbound
This paper cites Certifying and removing disparate impact.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Certifying and removing disparate impact
Reference 17
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 52c0b791-776b-49c6-ba28-a5df8a7733de · outbound
This paper cites Interpreting CLIP’s image representation via text-based de- composition.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Interpreting CLIP’s image representation via text-based de- composition
Reference 18
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- verified fuzzy
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Source-reported events for the cited work
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Observation 336c171b-8de3-4792-91a8-e95e94d0f06e · outbound
This paper cites Uncurated image-text datasets: Shedding light on demographic bias.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Uncurated image-text datasets: Shedding light on demographic bias
Reference 19
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Source-reported events for the cited work
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Observation 4b586c46-57a0-417a-8e22-94a708f38413 · outbound
This paper cites Fairness-aware ranking in search & recommendation systems with application to linkedin talent search.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Fairness-aware ranking in search & recommendation systems with application to linkedin talent search
Reference 20
- Resolution
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Observation 0f3cbe6e-8336-40c3-898f-93164b88fbbe · outbound
This paper cites Biases propagate in encoder-based vision- language models: A systematic analysis from intrinsic mea- sures to zero-shot retrieval outcomes, 2025.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Biases propagate in encoder-based vision- language models: A systematic analysis from intrinsic mea- sures to zero-shot retrieval outcomes, 2025
Reference 21
- Resolution
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Source-reported events for the cited work
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Observation ce3913d7-0f22-4120-880e-089db377952d · outbound
This paper cites Making the V in VQA matter: El- evating the role of image understanding in visual question answering.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Making the V in VQA matter: El- evating the role of image understanding in visual question answering
Reference 22
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Observation daa16c0f-fa37-4cf6-8984-3c8c977a40c0 · outbound
This paper cites Equality of op- portunity in supervised learning.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Equality of op- portunity in supervised learning
Reference 23
- Resolution
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Source-reported events for the cited work
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Observation 393fc66a-3ae0-420e-a420-afdad91f334d · outbound
This paper cites The bias of harmful label associations in vision- language models.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning The bias of harmful label associations in vision- language models
Reference 24
- Resolution
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Observation df98b082-6df4-4bc0-ac08-32ef05885133 · outbound
This paper cites Gaussian Error Linear Units (GELUs), 2023.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Gaussian Error Linear Units (GELUs), 2023
Reference 25
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 482dd66d-5cb9-4eac-b5d9-f9fec20e3609 · outbound
This paper cites Quantify- ing societal bias amplification in image captioning.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Quantify- ing societal bias amplification in image captioning
Reference 26
- Resolution
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Observation 8158f9e1-7767-4a10-8518-9f64f770051f · outbound
This paper cites GQA: A new dataset for real-world visual reasoning and compositional question answering.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning GQA: A new dataset for real-world visual reasoning and compositional question answering
Reference 27
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Observation 6e0df9da-d74c-44d6-8c59-1d983f5762c3 · outbound
This paper cites FairFace: Face at- tribute dataset for balanced race, gender, and age for bias measurement and mitigation.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning FairFace: Face at- tribute dataset for balanced race, gender, and age for bias measurement and mitigation
Reference 28
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Observation d29e842d-70e6-4bd1-a766-68b945fda4a0 · outbound
This paper cites Deep visual-semantic align- ments for generating image descriptions.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Deep visual-semantic align- ments for generating image descriptions
Reference 29
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Observation cdda01af-37d2-40ef-8495-51839d8db0c2 · outbound
This paper cites Microsoft COCO: Common objects in context.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Microsoft COCO: Common objects in context
Reference 30
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Observation c9839df6-374b-438f-9b2b-de07249f2f5c · outbound
This paper cites Visual instruction tuning.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Visual instruction tuning
Reference 31
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Observation 14e920ba-30a8-4728-a1c0-049ea330a4c6 · outbound
This paper cites Learning adversarially fair and transferable represen- tations.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Learning adversarially fair and transferable represen- tations
Reference 32
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Observation 380d6538-3060-47b0-92bc-0c455d7399bd · outbound
This paper cites Gen- der bias in multimodal models: A transnational feminist ap- proach considering geographical region and culture, 2023.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Gen- der bias in multimodal models: A transnational feminist ap- proach considering geographical region and culture, 2023
Reference 33
- Resolution
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Observation 9e7c0e71-6e73-4753-993d-3d0679d6c8a0 · outbound
This paper cites OK-VQA: A visual question answering benchmark requiring external knowledge.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning OK-VQA: A visual question answering benchmark requiring external knowledge
Reference 34
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This paper cites Provably fair representations, 2017.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Provably fair representations, 2017
Reference 35
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Observation ce6a9334-581a-49cb-81d5-59fd2178cc37 · outbound
This paper cites Gender arti- facts in visual datasets.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Gender arti- facts in visual datasets
Reference 36
- Resolution
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Observation d349c7cc-550f-4c3a-8598-2ffbe60609df · outbound
This paper cites Null-text inversion for editing real images using guided diffusion models.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Null-text inversion for editing real images using guided diffusion models
Reference 37
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 4376dec1-1e26-4df4-a12b-d461059a0845 · outbound
This paper cites Im2Text: Describing images using 1 million captioned pho- tographs.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Im2Text: Describing images using 1 million captioned pho- tographs
Reference 38
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 30f972d7-441e-4b9e-b97d-e7db6d63313f · outbound
This paper cites Learn- ing transferable visual models from natural language super- vision.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Learn- ing transferable visual models from natural language super- vision
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:47:47.153445Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation de70d587-d9c1-4313-87f4-33f1a512f1f1 · outbound
This paper cites Zero-shot text-to-image generation.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Zero-shot text-to-image generation
Reference 40
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 401ec320-c01c-4229-b811-142ffee67daf · outbound
This paper cites High-resolution image syn- thesis with latent diffusion models.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning High-resolution image syn- thesis with latent diffusion models
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:47:47.138567Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 4e22c348-e71e-491f-90c1-9b61e1567584 · outbound
This paper cites Distributionally robust neural networks for group shifts: On the importance of regularization for worst- case generalization.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Distributionally robust neural networks for group shifts: On the importance of regularization for worst- case generalization
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:47:47.130829Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 1f1ddd3a-2cb9-41d2-9398-1bcc328a8c9f · outbound
This paper cites LAION-5B: An open large-scale dataset for train- ing next generation image-text models.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning LAION-5B: An open large-scale dataset for train- ing next generation image-text models
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:47:47.123157Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 476c0974-b09c-4dbf-9e4e-e4ca76d27417 · outbound
This paper cites A-OKVQA: A benchmark for visual question answering using world knowl- edge.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning A-OKVQA: A benchmark for visual question answering using world knowl- edge
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:47:47.115648Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 7003d940-ccb7-418a-8d26-6f2050e967fc · outbound
This paper cites Conceptual Captions: A cleaned, hypernymed, im- age alt-text dataset for automatic image captioning.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Conceptual Captions: A cleaned, hypernymed, im- age alt-text dataset for automatic image captioning
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:47:47.108235Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e8b0c10c-6118-474f-af8c-cfae591df915 · outbound
This paper cites Fair representation: guaranteeing approximate multiple group fairness for unknown tasks.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Fair representation: guaranteeing approximate multiple group fairness for unknown tasks
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:47:47.100653Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 433bf8be-ce38-485a-b633-c22b7ef8edcb · outbound
This paper cites Towards VQA models that can read.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Towards VQA models that can read
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:47:47.092979Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 31131a89-1534-44a8-93f5-d70e320ee32e · outbound
This paper cites Upstream mitigation is not all you need: Testing the bias transfer hypothesis in pre-trained language models.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Upstream mitigation is not all you need: Testing the bias transfer hypothesis in pre-trained language models
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:47:47.085237Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation add17087-f930-49bc-b009-e464596feacf · outbound
This paper cites CIDEr: Consensus-based image description evalu- ation.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning CIDEr: Consensus-based image description evalu- ation
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:47:47.077721Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 9f6bb619-536f-49f5-acb9-0025e53a0139 · outbound
This paper cites Show and tell: A neural image caption gen- erator.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Show and tell: A neural image caption gen- erator
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:47:47.070599Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2f5bec65-c783-4f24-9d43-11e7364657c7 · outbound
This paper cites Directional bias am- plification.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Directional bias am- plification
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:47:47.062905Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 04e7bd17-2829-4a1f-bced-f6c423e54be1 · outbound
This paper cites Overwriting pre- trained bias with finetuning data.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Overwriting pre- trained bias with finetuning data
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:47:47.055078Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 56a2476c-0dd4-405b-b9e4-80dae31b9f20 · outbound
This paper cites Are gender-neutral queries really gender-neutral? Mitigating gender bias in im- age search.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Are gender-neutral queries really gender-neutral? Mitigating gender bias in im- age search
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:47:47.047346Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 014ec216-63e4-4bbe-9ba5-347dbbcae806 · outbound
This paper cites American == white in multimodal language-and-image AI.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning American == white in multimodal language-and-image AI
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:47:47.039433Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 1598465a-63b3-486c-a4a2-c99fbecc4a14 · outbound
This paper cites Markedness in visual se- mantic AI.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Markedness in visual se- mantic AI
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:47:47.031744Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 90080e4b-ed35-4d91-8bc0-b72190c83a88 · outbound
This paper cites Contrastive language-vision AI models pretrained on web- scraped multimodal data exhibit sexual objectification bias.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Contrastive language-vision AI models pretrained on web- scraped multimodal data exhibit sexual objectification bias
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:47:47.023160Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation a2211749-b27b-421e-bffd-0f69625e3673 · outbound
This paper cites mPLUG-Owl: Modularization empowers large language models with multimodality, 2024.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning mPLUG-Owl: Modularization empowers large language models with multimodality, 2024
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:47:47.014520Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation f06629ae-b66b-4b42-b391-db17f95c6172 · outbound
This paper cites Yi: Open foundation models by 01.ai,.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Yi: Open foundation models by 01.ai,
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:47:47.006707Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ac4ebf09-1c89-42a5-9531-014b6f306bd7 · outbound
This paper cites Under- standing and evaluating racial biases in image captioning.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Under- standing and evaluating racial biases in image captioning
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:47:46.998789Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ccb5eeb6-6347-405d-903c-de2480ce0250 · outbound
This paper cites Inherent tradeoffs in learning fair representations.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning Inherent tradeoffs in learning fair representations
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:47:46.990715Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2cb315d0-520e-4bf3-b7b8-30879cf7acf5 · outbound
This paper cites TinyLLaV A: A framework of small-scale large multimodal models, 2024.
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning TinyLLaV A: A framework of small-scale large multimodal models, 2024
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:47:46.982341Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Pith citing papers
No inbound Pith citation observations are available.