Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T23:17:34.227767Z
Paper Citation Record · LEDGER
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning
As of 24 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 2 inbound Pith citation observations for arXiv:2505.05180.
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.
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Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T18:14:13.678118Z
A source-named dated measurement, never combined with another source.
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Reference resolution
60 of 60 outbound references displayed
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External citation measurements
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Outbound references
Observation ded358d7-21f6-40d5-981b-4733eae78033 · outbound
This paper cites write newline.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning write newline
Reference 1
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Observation daaa1870-b329-41b3-bdbd-e2d55858211b · outbound
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OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Unresolved cited work
Reference 2
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Observation eea6cf87-97cc-49ac-8ba8-542cac26c4d8 · outbound
This paper cites and Tzimiropoulos, G.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning and Tzimiropoulos, G
Reference 3
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Observation 22f9801c-9fe7-49b6-9a9e-2532c6fe2dc6 · outbound
This paper cites Describing textures in the wild.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Describing textures in the wild
Reference 4
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Observation f014b592-963d-439a-904c-afd9aa1d7750 · outbound
This paper cites Imagenet: A large-scale hierarchical image database.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Imagenet: A large-scale hierarchical image database
Reference 5
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Observation 91e13586-483b-4c4f-b9b0-1a025e6aec40 · outbound
This paper cites An introduction to ROC analysis.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning An introduction to ROC analysis
Reference 6
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Observation da42ae53-0e5d-43c1-b506-b79c179b6b15 · outbound
This paper cites Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Reference 7
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Observation 37882286-1113-4318-997c-26282bf2cd53 · outbound
This paper cites Shrec'22 track: Open-set 3d object retrieval.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Shrec'22 track: Open-set 3d object retrieval
Reference 8
- Resolution
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Observation 0ffcc87b-c00e-4df9-aeb2-b8a0029aadb8 · outbound
This paper cites and Zhou, Z.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning and Zhou, Z
Reference 9
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Observation 71e17c0b-a1fa-4901-ba26-9d0806b69247 · outbound
This paper cites Aucseg: Auc-oriented pixel-level long-tail semantic segmentation.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Aucseg: Auc-oriented pixel-level long-tail semantic segmentation
Reference 10
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Observation 0d3177a0-86dd-4859-8d8f-176dbd6bcf2f · outbound
This paper cites Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Reference 11
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Observation 390466a8-f0ef-4597-a07b-d383efa10f93 · outbound
This paper cites and Gimpel, K.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning and Gimpel, K
Reference 12
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Observation c4891a59-39d9-4ace-82b2-1f35f15369a9 · outbound
This paper cites The many faces of robustness: A critical analysis of out-of-distribution generalization.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning The many faces of robustness: A critical analysis of out-of-distribution generalization
Reference 13
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This paper cites Natural adversarial examples.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Natural adversarial examples
Reference 14
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Observation 6e1c5cb0-d939-40d8-ab73-cf00b10bc225 · outbound
This paper cites Reconboost: Boosting can achieve modality reconcilement.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Reconboost: Boosting can achieve modality reconcilement
Reference 15
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Observation c3e3c3f5-9111-4424-a18a-d512b2c28e1e · outbound
This paper cites U., Rasheed, H.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning U., Rasheed, H
Reference 16
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Observation 2dd14bfd-6403-4e35-9951-e46f2cd68231 · outbound
This paper cites U., Wasim, S.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning U., Wasim, S
Reference 17
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Observation aacc854d-b46a-4a08-99c7-cf3eb839c0cd · outbound
This paper cites Learning to Prompt with Text Only Supervision for Vision-Language Models.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Learning to Prompt with Text Only Supervision for Vision-Language Models
Reference 18
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Observation f03fac1c-2c13-4337-afc3-51162a395c92 · outbound
This paper cites and Ramanan, D.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning and Ramanan, D
Reference 19
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This paper cites 3d object representations for fine-grained categorization.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning 3d object representations for fine-grained categorization
Reference 20
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Observation eec0c71f-ccd2-4e4b-b790-2065936318c3 · outbound
This paper cites Gallop: Learning global and local prompts for vision-language models.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Gallop: Learning global and local prompts for vision-language models
Reference 21
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Observation a291974d-7526-42b3-b9e1-713f596584c3 · outbound
This paper cites The power of scale for parameter-efficient prompt tuning.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning The power of scale for parameter-efficient prompt tuning
Reference 22
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Observation dc7c1aee-6c9f-40f3-b1e6-e7d94e2345a3 · outbound
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OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Promptkd: Unsupervised prompt distillation for vision-language models
Reference 23
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OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Stochastic auc maximization with deep neural networks
Reference 24
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Observation c67860c3-3280-48c3-91be-18055e8d3845 · outbound
This paper cites Fine-Grained Visual Classification of Aircraft.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Fine-Grained Visual Classification of Aircraft
Reference 25
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OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Unresolved cited work
Reference 26
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OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Locoop: Few-shot out-of-distribution detection via prompt learning
Reference 27
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OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Out-of-distribution detection with negative prompts
Reference 28
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OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning and Zisserman, A
Reference 29
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OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning M., Vedaldi, A., Zisserman, A., and Jawahar, C
Reference 30
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OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Automatic differentiation in pytorch
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OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., and Sutskever, I
Reference 32
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This paper cites Do I mage N et classifiers generalize to I mage N et? In International Conference on Machine Learning, pp.\ 5389--5400, 2019.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Do I mage N et classifiers generalize to I mage N et? In International Conference on Machine Learning, pp.\ 5389--5400, 2019
Reference 33
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OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning and Etemad, A
Reference 34
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OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Unresolved cited work
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OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
Reference 36
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OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Kill two birds with one stone: Rethinking data augmentation for deep long-tailed learning
Reference 37
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This paper cites C., and Xing, E.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning C., and Xing, E
Reference 38
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OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Llm-autoda: Large language model-driven automatic data augmentation for long-tailed problems
Reference 39
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This paper cites Openauc: Towards auc-oriented open-set recognition.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Openauc: Towards auc-oriented open-set recognition
Reference 40
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This paper cites Cascade prompt learning for vision-language model adaptation.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Cascade prompt learning for vision-language model adaptation
Reference 41
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This paper cites Zero-shot learning — the good, the bad and the ugly.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Zero-shot learning — the good, the bad and the ugly
Reference 42
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OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning A., Oliva, A., and Torralba, A
Reference 43
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Observation 5f5e587f-fab9-4cb8-aa44-747fa50a9755 · outbound
This paper cites and Ying, Y.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning and Ying, Y
Reference 44
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Observation a7f56374-f354-46ec-9158-f4f9122ce87b · outbound
This paper cites Learning with multiclass AUC: theory and algorithms, 2022.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Learning with multiclass AUC: theory and algorithms, 2022
Reference 45
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Observation 255fb5e1-ea9e-4be9-81bc-670e1867cffa · outbound
This paper cites Optimizing two-way partial AUC with an end-to-end framework.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Optimizing two-way partial AUC with an end-to-end framework
Reference 46
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Observation 40bda5bf-5418-49dc-a0f1-6c846172c3c2 · outbound
This paper cites Harnessing hierarchical label distribution variations in test agnostic long-tail recognition.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Harnessing hierarchical label distribution variations in test agnostic long-tail recognition
Reference 47
- Resolution
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Observation 3943a415-4ed9-4b4a-8801-46ef1ae5a4ca · outbound
This paper cites Visual-language prompt tuning with knowledge-guided context optimization.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Visual-language prompt tuning with knowledge-guided context optimization
Reference 48
- Resolution
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- raw_fallback, observed 2026-08-15T23:17:34.429143Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 15ec1a37-6d2a-4a7b-a652-7a2e699f66f0 · outbound
This paper cites Tcp: Textual-based class-aware prompt tuning for visual-language model.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Tcp: Textual-based class-aware prompt tuning for visual-language model
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:17:34.416656Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 1f41cb34-f335-429b-b527-13dc221d0b19 · outbound
This paper cites Compositional training for end-to-end deep AUC maximization.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Compositional training for end-to-end deep AUC maximization
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:17:34.403934Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 162b53f2-2ccb-42ee-a81a-74ac78c66898 · outbound
This paper cites Counterfactual zero-shot and open-set visual recognition.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Counterfactual zero-shot and open-set visual recognition
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:17:34.393083Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation debaec28-68e4-4f76-87ca-f6ec7ed2d1e4 · outbound
This paper cites Local-Prompt: Extensible Local Prompts for Few-Shot Out-of-Distribution Detection.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Local-Prompt: Extensible Local Prompts for Few-Shot Out-of-Distribution Detection
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:17:34.199553Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2aa6eda4-6558-4537-9f25-2cf495f9021a · outbound
This paper cites T., and Song, J.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning T., and Song, J
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:17:34.379869Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation bc3ece8c-7fce-4f90-b257-f985cf450982 · outbound
This paper cites Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:17:34.368399Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation c9aab9d4-5389-4d70-bcae-df55a0287558 · outbound
This paper cites Two fists, one heart: Multi-objective optimization based strategy fusion for long-tailed learning.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Two fists, one heart: Multi-objective optimization based strategy fusion for long-tailed learning
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:17:34.356558Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 89afa806-1156-44c6-8a02-078792b52355 · outbound
This paper cites Breaking long-tailed learning bottlenecks: A controllable paradigm with hypernetwork-generated diverse experts.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Breaking long-tailed learning bottlenecks: A controllable paradigm with hypernetwork-generated diverse experts
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:17:34.343589Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 54bb1eb4-1837-46e4-aac8-3ccd3e1c29b2 · outbound
This paper cites C., and Liu, Z.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning C., and Liu, Z
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:17:34.330925Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 49946655-a1de-4f2e-afc0-b7e1ea8bef26 · outbound
This paper cites C., and Liu, Z.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning C., and Liu, Z
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:17:34.319563Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 5323b0a9-ae8c-4a46-b1dc-2d7ad42a8b20 · outbound
This paper cites Decoop: Robust prompt tuning with out-of-distribution detection.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Decoop: Robust prompt tuning with out-of-distribution detection
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:17:34.306790Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 1178c5f8-03e6-4fd2-a9be-6dfe7e2d9e4b · outbound
This paper cites Prompt-aligned gradient for prompt tuning.
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning Prompt-aligned gradient for prompt tuning
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:17:34.294899Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Pith citing papers
Observation ff1058bd-8dfa-4be5-acdc-304eb984508d · inbound
SpecPL: Disentangling Spectral Granularity for Prompt Learning cites this paper.
SpecPL: Disentangling Spectral Granularity for Prompt Learning OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T17:11:10.386147Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 02c287f4-8791-40ca-bf43-11e6e6cde60d · inbound
UniTraffic-Agent: Unified Traffic Video Reasoning for AI City Challenge 2026 Track 3 with Two Out-of-Domain Evaluations cites this paper.
UniTraffic-Agent: Unified Traffic Video Reasoning for AI City Challenge 2026 Track 3 with Two Out-of-Domain Evaluations OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:14:13.678118Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.