Paper Citation Record · LEDGER

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning

As of 7 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2607.05911.

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

pith.paper-citation-record.v1
2607.05911 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-08T21:04:13.639595Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

42 of 42 outbound references displayed

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verified fuzzy36
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 978aaa66-1ab6-4815-9382-e7adcf22cdd6 · outbound

This paper cites Qwen2.5-VL Technical Report.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Qwen2.5-VL Technical Report

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-07-08T21:05:35.492748Z

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Observation 96db8bf3-89d3-4ce2-be18-51d69e818572 · outbound

This paper cites Prompting language-informed distribution for compositional zero-shot learning.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Prompting language-informed distribution for compositional zero-shot learning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.582578Z

Source-reported events for the cited work

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Observation 427fdc6c-a377-4eba-b1fa-4478fbd9d606 · outbound

This paper cites Event-centric multi-modal fusion method for dense video captioning.Neural Net- works, 146:120–129,.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Event-centric multi-modal fusion method for dense video captioning.Neural Net- works, 146:120–129,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.550786Z

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Observation dc01769f-0d7b-4890-88b6-53d1099cdb57 · outbound

This paper cites Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-07-08T21:05:35.489700Z

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Observation dcce6fe0-7316-4c78-a71b-745087202739 · outbound

This paper cites Deep reinforcement learning from human preferences.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Deep reinforcement learning from human preferences

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.570874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fd5aaad9-344e-468c-8459-6735d4f1b1f2 · outbound

This paper cites Instructblip: Towards general-purpose vision-language models with instruction tuning.NeurIPS, 36:49250–49267,.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Instructblip: Towards general-purpose vision-language models with instruction tuning.NeurIPS, 36:49250–49267,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.590925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e2c510c3-1be7-4e6d-a1d4-fa417aa8c028 · outbound

This paper cites Molmo and pixmo: Open weights and open data for state-of-the-art multimodal models.arXiv e- prints, pages arXiv–2409,.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Molmo and pixmo: Open weights and open data for state-of-the-art multimodal models.arXiv e- prints, pages arXiv–2409,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.565565Z

Source-reported events for the cited work

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Observation 0b470d76-70fc-4c78-b423-a04cb4e4fc3a · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-07-08T21:05:35.486072Z

Source-reported events for the cited work

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Observation 6e43f59c-8d8a-4bc3-af96-e5efca2bfc03 · outbound

This paper cites Visual programming: Compositional visual reasoning without training.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Visual programming: Compositional visual reasoning without training

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.588339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dfbd6fc1-93af-474b-a9f7-a92fe1e836e1 · outbound

This paper cites Learning attention as disentangler for composi- tional zero-shot learning.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Learning attention as disentangler for composi- tional zero-shot learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.562015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b70fa404-7677-42e0-9b76-820fa7bd414f · outbound

This paper cites Troika: Multi-path cross-modal traction for compositional zero- shot learning.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Troika: Multi-path cross-modal traction for compositional zero- shot learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.586948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 24fabee1-2af7-4eb8-9652-08fcabc247bb · outbound

This paper cites Gqa: A new dataset for real-world vi- sual reasoning and compositional question answering.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Gqa: A new dataset for real-world vi- sual reasoning and compositional question answering

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.576801Z

Source-reported events for the cited work

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Observation 3fe5d224-a988-42d3-b700-83bb38d8fd59 · outbound

This paper cites GPT-4o System Card.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning GPT-4o System Card

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-07-08T21:05:35.498654Z

Source-reported events for the cited work

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Observation f5cc3553-30f8-412b-a469-58e56b55ae29 · outbound

This paper cites Discovering states and transformations in image collections.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Discovering states and transformations in image collections

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.592678Z

Source-reported events for the cited work

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Observation 7fc2b712-04ce-480d-8f2e-9795e743f0d6 · outbound

This paper cites Mdetr-modulated detection for end-to-end multi- modal understanding.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Mdetr-modulated detection for end-to-end multi- modal understanding

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.581103Z

Source-reported events for the cited work

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Observation d841b680-9840-428d-aa85-833abe6a0bb1 · outbound

This paper cites Hierarchical visual primi- tive experts for compositional zero-shot learning.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Hierarchical visual primi- tive experts for compositional zero-shot learning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.602780Z

Source-reported events for the cited work

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Observation fda6d790-0b50-4cc6-b3d4-367a792b12ef · outbound

This paper cites Visual instruction tuning.NeurIPS, 36:34892–34916,.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Visual instruction tuning.NeurIPS, 36:34892–34916,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.548581Z

Source-reported events for the cited work

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Observation 0748d347-bd42-4aa7-9618-0da740e83097 · outbound

This paper cites Llavanext: Improved reasoning, ocr, and world knowl- edge,.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Llavanext: Improved reasoning, ocr, and world knowl- edge,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.544909Z

Source-reported events for the cited work

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Observation 96525a8d-c4ec-4eab-b3b6-1d449b9a832a · outbound

This paper cites From red wine to red tomato: Composition with context.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning From red wine to red tomato: Composition with context

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.542142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7279c981-925f-4d12-9b2f-8ba672b31b4a · outbound

This paper cites Learning graph embeddings for compositional zero-shot learning.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Learning graph embeddings for compositional zero-shot learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.574831Z

Source-reported events for the cited work

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Observation 2729735a-89a6-4b48-bbd4-fc91731c394e · outbound

This paper cites Attributes as operators: factorizing unseen attribute-object compositions.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Attributes as operators: factorizing unseen attribute-object compositions

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.586517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 14c2011b-d000-4421-bfa5-6446533d6210 · outbound

This paper cites Learning to compose soft prompts for composi- tional zero-shot learning.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Learning to compose soft prompts for composi- tional zero-shot learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.540170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1a028341-beab-49a5-9585-46bc9035b656 · outbound

This paper cites Training language models to follow instruc- tions with human feedback.NeurIPS, 35:27730–27744,.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Training language models to follow instruc- tions with human feedback.NeurIPS, 35:27730–27744,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.600503Z

Source-reported events for the cited work

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Observation 29436495-b00e-4cc4-9f61-855b3c8ce38c · outbound

This paper cites Learning to predict visual attributes in the wild.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Learning to predict visual attributes in the wild

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.579200Z

Source-reported events for the cited work

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Observation 5107e784-8e21-4c17-9db3-0be7ac23cbf7 · outbound

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

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning What does a platypus look like? generating customized prompts for zero-shot image classification

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.554760Z

Source-reported events for the cited work

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Observation 28f79568-9dc4-4606-976b-666bbf1bc7b9 · outbound

This paper cites Task-driven modular networks for zero-shot compositional learning.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Task-driven modular networks for zero-shot compositional learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.583144Z

Source-reported events for the cited work

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Observation 3bc3b357-3141-44b7-b12f-8f5f535c9fd3 · outbound

This paper cites Learning clustering-based prototypes for compositional zero-shot learning.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Learning clustering-based prototypes for compositional zero-shot learning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.585084Z

Source-reported events for the cited work

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Observation 370e1cc8-cf1f-457f-b18d-060fbf55c328 · outbound

This paper cites Learning transferable visual models from nat- ural language supervision.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Learning transferable visual models from nat- ural language supervision

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.573230Z

Source-reported events for the cited work

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Observation 4ccdd083-8f24-4238-be7a-aa4763744e48 · outbound

This paper cites Independent prototype propagation for zero- shot compositionality.NeurIPS, 34:10641–10653,.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Independent prototype propagation for zero- shot compositionality.NeurIPS, 34:10641–10653,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.571392Z

Source-reported events for the cited work

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Observation 6a1ebdb1-4c7d-4d56-a192-7003fbe719e4 · outbound

This paper cites Disentangling visual embeddings for at- tributes and objects.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Disentangling visual embeddings for at- tributes and objects

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.563738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7d53c6f0-ceae-4d83-beb6-bf61ab7f615a · outbound

This paper cites Beyond seen primitive concepts and attribute- object compositional learning.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Beyond seen primitive concepts and attribute- object compositional learning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.573017Z

Source-reported events for the cited work

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Observation 71f99d74-b3a6-4d28-95f5-e25bc0b8edd2 · outbound

This paper cites Aligning large multimodal models with factually aug- mented rlhf.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Aligning large multimodal models with factually aug- mented rlhf

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.577137Z

Source-reported events for the cited work

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Observation 8136a24d-b14e-4d72-b8a5-425f94c78739 · outbound

This paper cites Vipergpt: Visual inference via python execu- tion for reasoning.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Vipergpt: Visual inference via python execu- tion for reasoning

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.548814Z

Source-reported events for the cited work

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Observation 77330df8-a4cd-4b56-b04d-893709a94d31 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.NeurIPS, 35:24824– 24837,.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Chain-of-thought prompting elicits reasoning in large language models.NeurIPS, 35:24824– 24837,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.565389Z

Source-reported events for the cited work

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Observation d1743e07-cb71-4cc2-974a-d11d697dfab6 · outbound

This paper cites DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-07-08T21:05:35.486340Z

Source-reported events for the cited work

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Observation 4148e1c1-e288-4ad4-a3bf-240402ef48f4 · outbound

This paper cites Relation-aware compo- sitional zero-shot learning for attribute-object pair recog- nition.IEEE TMM, 24:3652–3664,.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Relation-aware compo- sitional zero-shot learning for attribute-object pair recog- nition.IEEE TMM, 24:3652–3664,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.567491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7e25653c-1c9a-4bd2-83b6-1e1e80d07a12 · outbound

This paper cites Vigor: Im- proving visual grounding of large vision language models with fine-grained reward modeling.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Vigor: Im- proving visual grounding of large vision language models with fine-grained reward modeling

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.598283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9e88f811-9ff0-4689-ab37-946dffad5aba · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models.NeurIPS, 36:11809–11822,.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Tree of thoughts: Deliberate problem solving with large language models.NeurIPS, 36:11809–11822,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.588823Z

Source-reported events for the cited work

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Observation cbccba40-5095-47d0-a0dc-a0d9ba679b77 · outbound

This paper cites Rlhf-v: To- wards trustworthy mllms via behavior alignment from fine-grained correctional human feedback.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Rlhf-v: To- wards trustworthy mllms via behavior alignment from fine-grained correctional human feedback

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.594942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b9a58659-e22b-4918-99b1-9dc81bd9e73d · outbound

This paper cites Least-to-most prompting enables complex reasoning in large language models.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Least-to-most prompting enables complex reasoning in large language models

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.604670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bfa17325-a0f6-4192-81f6-7af57be4247c · outbound

This paper cites Diccr: Double-gated intervention and confounder causal reasoning for vision-language nav- igation.Neural Networks, 184:107078,.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning Diccr: Double-gated intervention and confounder causal reasoning for vision-language nav- igation.Neural Networks, 184:107078,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T21:05:35.590742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 375654c8-3a61-476b-abec-bc1586d42c0d · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-07-08T21:05:35.495740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.