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Paper Citation Record · LEDGER

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning

As of 18 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2606.06100.

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

pith.paper-citation-record.v1
2606.06100 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-28T01:57:27.102124Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T23:46:56.165087Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

46 of 46 outbound references displayed

  • verified exact2
  • verified fuzzy0
  • unresolved43
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 07c62ba4-1701-4215-b74d-f27e0c60b21b · outbound

This paper cites Making the V in VQA matter: Elevating the role of image understanding in visual question answering.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Making the V in VQA matter: Elevating the role of image understanding in visual question answering

Reference 1

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Observation fcf8ff4c-5534-4f96-b02a-baf55e9d8bd6 · outbound

This paper cites GQA: A new dataset for real-world visual reasoning and composi- tional question answering.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning GQA: A new dataset for real-world visual reasoning and composi- tional question answering

Reference 2

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:5d5ffb7dc168cef0d784460c49fa4ebde7417738356a040dca2f6e73feff26e4

Observation 09e6c4ef-03d7-42fc-a66c-4e025d3df952 · outbound

This paper cites Winoground: Probing vision and language models for visio- linguistic compositionality.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Winoground: Probing vision and language models for visio- linguistic compositionality

Reference 3

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:52e6acbdb9140a099717cb208d3a5d8fa4824798ae6846f8d3ca676f556908af

Observation 8960315b-6866-4486-9801-8c688d9830d5 · outbound

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

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Learning transferable visual models from natural language supervision

Reference 4

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:fa62e303390f90d7fca85b98beb7ca7676ecd2d8ed5d192e3e7519225f2f6564

Observation 3485940c-e055-499d-8b5f-0f9c8b3e60ff · outbound

This paper cites BLIP-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning BLIP-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 5

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:749202e3a3f4d86094d4d7525092736de7a10732739ede097b6d989dda03f647

Observation 24a26e5e-44f1-4c5e-93e4-f04155fb2b87 · outbound

This paper cites Visual instruction tuning.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Visual instruction tuning

Reference 6

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:6ffe041a0b4d581382fc50d39daa32108deff7360ad5889f17b6e446e1f71bd9

Observation 0a635b35-9e5c-48ec-8a4f-c6a15f0c3c4d · outbound

This paper cites Improved baselines with visual instruction tuning.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Improved baselines with visual instruction tuning

Reference 7

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:17b257b6c36210f013b2e3e58ae2886ae60cda40f97b9f96ce8190d173579a0b

Observation 9acad1c0-2f50-42ab-9fb8-b38fb023e6e4 · outbound

This paper cites When and why vision- language models behave like bags-of-words, and what to do about it? InICLR, 2023.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning When and why vision- language models behave like bags-of-words, and what to do about it? InICLR, 2023

Reference 8

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:eafc957867311f7930e7d5b96a368b4277f6dc04ce99cb1e6f85915cdff0a79b

Observation d2136ad3-7a22-4ddc-84af-bd5af036f840 · outbound

This paper cites Sugarcrepe: Fixing hackable benchmarks for vision-language compositionality.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Sugarcrepe: Fixing hackable benchmarks for vision-language compositionality

Reference 9

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:17b65836bb4dec54a2529f85bd2d9106be1386957f2fdf4cf35e953045c4dcfe

Observation e4406b2a-b1ba-4197-9207-a8e57172398e · outbound

This paper cites SA-VQA: Structured Alignment of Visual and Semantic Representations for Visual Question Answering.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning SA-VQA: Structured Alignment of Visual and Semantic Representations for Visual Question Answering

Reference 10

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arxiv_id, observed 2026-07-02T12:36:57.224868Z

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:0a8d8e15f2c18139aebe25b5241c51042ad0413615c5fb2a8ad0f17ce7e4d486

Observation 83024b4e-076c-49a6-8b37-465fb045c06c · outbound

This paper cites LLaV A-SG: Leveraging scene graphs as visual semantic expression in vision-language models.ICASSP, 2025.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning LLaV A-SG: Leveraging scene graphs as visual semantic expression in vision-language models.ICASSP, 2025

Reference 11

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:74d453c3e0fd03978c75d1a5b151d4ccb34c1bf0c73db5a74332b15bf7184262

Observation b36acf81-fae8-4bba-a1b4-bf1cc6203cec · outbound

This paper cites Compositional chain-of-thought prompting for large multimodal models.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Compositional chain-of-thought prompting for large multimodal models

Reference 12

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:74577cc6f7ee16933577c2d9f596336a14b33d8a5ea5dfa0b947405a340347af

Observation 0f09f6c1-71b5-40c7-9283-21658b2dc70c · outbound

This paper cites Hyperbolic image-text representations.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Hyperbolic image-text representations

Reference 13

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:91894f193d04364c95419cb421c21b31d5a2de0e14413396eaeda4fe6391299c

Observation 33145b73-fd21-4f48-86c8-d9d5d48c19dc · outbound

This paper cites Accept the modality gap: An exploration in the hyperbolic space.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Accept the modality gap: An exploration in the hyperbolic space

Reference 14

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:c1dec0ab6589dc92c54c7d7645a244de0abdaa7f1fe05368100c3d1d620e8111

Observation bd619135-e1c7-4e84-adbd-321087a20421 · outbound

This paper cites Show and tell: A neural image caption generator.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Show and tell: A neural image caption generator

Reference 15

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:bf7aac5df9ddd4fe04f6f1688a294f72ea0fe7dc09f1a2f433298765037ce94c

Observation 96d82925-245b-4a1b-b120-bc91dfaf6dea · outbound

This paper cites Multimodal compact bilinear pooling for visual question answering and visual grounding.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Multimodal compact bilinear pooling for visual question answering and visual grounding

Reference 16

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:c985ac38981f9889c3f764d6293cf8b5e57a853045c90493690c8ea762280d35

Observation 4585fa6e-b3b1-4ac2-a7f0-6a21862aadab · outbound

This paper cites VL-BERT: Pre-training of generic visual-linguistic representations.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning VL-BERT: Pre-training of generic visual-linguistic representations

Reference 17

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:8c43f82936d3514f12eb80ae677e6f14937a51a793f6df6a8bac5f157e5aa66c

Observation 27d360c0-8d48-4a38-bc4b-d3313eab24d7 · outbound

This paper cites ViLBERT: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning ViLBERT: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks

Reference 18

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:162a2741ac38f3ce8fb7f5d3707921166ae1581bcc195a8dd69fbefcd7c3257c

Observation f4be454e-ff86-429d-86b5-edbebdc7cf59 · outbound

This paper cites UNITER: Universal image-text representation learning.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning UNITER: Universal image-text representation learning

Reference 19

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:1c27bb724e8b29f09beb3bf466bf064253af670f9af9a6debf99c09ed86d85ef

Observation 36162ab0-8a56-4bf3-8c08-c1985a5f5688 · outbound

This paper cites DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control

Reference 20

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arxiv_id, observed 2026-07-02T12:36:57.221781Z

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:c2772e9f6f5c11b4ff2aa634d38c0a3b0a7256c630f9bb02f88e550d04654929

Observation 6352a24d-fc67-492b-8ecc-6de07e164012 · outbound

This paper cites InternVL: Scaling up vision foundation models and aligning for generic visual-linguistic tasks.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning InternVL: Scaling up vision foundation models and aligning for generic visual-linguistic tasks

Reference 21

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:7ce9874148d11b2c8be586b2f8f617264193523eb0117a106159de1639d92acc

Observation b86ee07e-3dfe-4c17-aa37-95a2bfdb4484 · outbound

This paper cites Scene graph generation by iterative message passing.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Scene graph generation by iterative message passing

Reference 22

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:69b6a08052021d3c54ceda5df4b9232c19c37a41356e23c6135d0507c93be548

Observation 06f5b66e-1e1f-4819-b1dc-e727544551af · outbound

This paper cites Graphical contrastive losses for scene graph generation.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Graphical contrastive losses for scene graph generation

Reference 23

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:ab82494597ad7eac772a42c3b51a4f5fd50437463e3c2a797dac7283e1e3cade

Observation 91b7a0c9-6454-4cea-a005-fd71ac6271b8 · outbound

This paper cites Panoptic scene graph generation.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Panoptic scene graph generation

Reference 24

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:47a64f876ff05d15d6e982be0e38b6ef3765eca551f8078be38e150bc57a6914

Observation 22af9e34-1a99-4aca-a685-1d3a8eefee41 · outbound

This paper cites From pixels to graphs: Open-vocabulary scene graph generation with vision-language models.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning From pixels to graphs: Open-vocabulary scene graph generation with vision-language models

Reference 25

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:28d3a52c27a03c9d23a579deeb26f4f1d6e16b8a5b25368f8150dd185e5fa9ca

Observation 91d927ac-7633-4572-a611-6eb0577313d8 · outbound

This paper cites Incorporating structured representations into pretrained vision and language models using scene graphs.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Incorporating structured representations into pretrained vision and language models using scene graphs

Reference 26

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:e153c6deb361edc624fd63d12ac7e25bae9fdc010beb03d3ba50855136faf562

Observation 4d9c2341-7e34-4590-bc7c-a6b0f5b5deca · outbound

This paper cites EGTR: Extracting graph from transformer for scene graph generation.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning EGTR: Extracting graph from transformer for scene graph generation

Reference 27

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:5801e06d586317c17a7a5d0aa8da733542cd8edc77bf86cfb231ed1f57956710

Observation 3bad75db-e5cf-4096-9dad-e1638a28c645 · outbound

This paper cites Leveraging predicate and triplet learning for scene graph generation.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Leveraging predicate and triplet learning for scene graph generation

Reference 28

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:6cfc5fe8d4f939fab2e32c9faf410fd16ff672a13060b31cdf7aad890abbdc6c

Observation fa8e8846-e6e9-4c55-924a-a19bfc79114a · outbound

This paper cites Visual genome: Connecting language and vision using crowdsourced dense image annotations.IJCV, 123:32–73, 2017.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Visual genome: Connecting language and vision using crowdsourced dense image annotations.IJCV, 123:32–73, 2017

Reference 29

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Observation 201c65c5-acaf-4dc6-b5af-5be0c109d040 · outbound

This paper cites Neural motifs: Scene graph parsing with global context.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Neural motifs: Scene graph parsing with global context

Reference 30

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:b7dda86861b135c77e12d685ca09b458d66f87e48c5ecad229350105804ef683

Observation bfb15670-8500-4940-9572-c5081426baca · outbound

This paper cites Poincaré embeddings for learning hierarchical representations.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Poincaré embeddings for learning hierarchical representations

Reference 31

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:217d87677ee0e897e614b957400e4a856002f1ef3a5cfe58d784d18fe703a540

Observation 12d51005-9c6e-41ad-be21-e564de485126 · outbound

This paper cites Hyperbolic image embeddings.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Hyperbolic image embeddings

Reference 32

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:67eab3ffa88cf836ef33b3fd3a0c98715454c18f4272fe8de8b3916a4f3340ea

Observation 77f31dd9-e721-433a-81a4-7787413128f8 · outbound

This paper cites Hyperbolic neural networks.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Hyperbolic neural networks

Reference 33

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:4556d4791de7a2bc99f93269c0f4bc539cb9b6a5d1499cfdd88f01b4e5aae61c

Observation 264a7ecd-a0e1-4ba1-8e76-821893ed0b42 · outbound

This paper cites Inferring concept hierarchies from text corpora via hyperbolic embeddings.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Inferring concept hierarchies from text corpora via hyperbolic embeddings

Reference 34

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:2dff8c09183841e1d8d8f1854823eea47dc8d844e3469789c6a31b7b73acd80c

Observation f882b5f3-1f98-497d-ab36-61e3d5d4bd9d · outbound

This paper cites Order-embeddings of images and language.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Order-embeddings of images and language

Reference 35

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source=pdf_text observed=2026-06-28T01:57:27.102124Z digest=sha256:c94d88338574891e8517721a1975b3cdbff167220679e5f01b61d4bb6fd53d75

Observation 593ce2ea-2e64-4933-84c4-727307e9411c · outbound

This paper cites Compositional entailment learning for hyperbolic vision-language models.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Compositional entailment learning for hyperbolic vision-language models

Reference 36

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Observation 1a819be8-4fbc-45a3-aefc-abe7519efd8b · outbound

This paper cites Hyperbolic safety-aware vision-language models.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Hyperbolic safety-aware vision-language models

Reference 37

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Observation 853399c7-a4bd-4378-a989-68a181975f64 · outbound

This paper cites HyperET: Efficient training in hyperbolic space for multi-modal large language models.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning HyperET: Efficient training in hyperbolic space for multi-modal large language models

Reference 38

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Observation 01fc9781-498d-4822-86be-31be488d9ba7 · outbound

This paper cites Multi-relational Poincaré graph embeddings.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Multi-relational Poincaré graph embeddings

Reference 39

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Observation be83c4dc-377c-446c-a773-039901e9e67c · outbound

This paper cites Low-dimensional hyperbolic knowledge graph embeddings.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Low-dimensional hyperbolic knowledge graph embeddings

Reference 40

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Observation 828db24c-69ed-4d77-9687-0f8d09e700b9 · outbound

This paper cites RotatE: Knowledge graph embedding by relational rotation in complex space.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning RotatE: Knowledge graph embedding by relational rotation in complex space

Reference 41

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Observation a78ad690-0474-47d6-8667-7cbf0b10ce45 · outbound

This paper cites Mind the gap: Under- standing the modality gap in multi-modal contrastive representation learning.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Mind the gap: Under- standing the modality gap in multi-modal contrastive representation learning

Reference 42

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Observation bbd07f1c-20aa-478c-93e0-535b82172ea1 · outbound

This paper cites Visual prompt tuning.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Visual prompt tuning

Reference 43

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Observation 3d2dff28-f770-446a-93bf-a42acf51f923 · outbound

This paper cites LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model

Reference 44

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation d3a6b439-5ebb-4a79-80b7-db36c80f404c · outbound

This paper cites World Scientific, 2005.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning World Scientific, 2005

Reference 45

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Observation 4369d9a3-05fe-4faa-be85-d51bd38988c8 · outbound

This paper cites Why is winoground hard? investigating failures in visuolinguistic compositionality.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning Why is winoground hard? investigating failures in visuolinguistic compositionality

Reference 46

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Pith citing papers

Observation a78ab6f9-dcc7-4280-bbd8-a5abc17f1a8e · inbound

The Gate Always Closes: On Injecting Auxiliary Signals into Frozen Vision-Language Models cites this paper.

The Gate Always Closes: On Injecting Auxiliary Signals into Frozen Vision-Language Models HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning

Reference 4

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