Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-10T13:53:13.255412Z
Paper Citation Record · LEDGER
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing
As of 11 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2604.13565.
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 2026-08-11T06:34:44.6726+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
29 of 29 outbound references displayed
- verified exact20
- verified fuzzy4
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch5
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 10d5bb70-009c-487f-98bd-1d4a92bbec88 · outbound
This paper cites Phi-4 Technical Report.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing Phi-4 Technical Report
Reference 1
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T06:34:04.627981Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation b743bf03-f52b-4f71-af2f-94a6c4b40e93 · outbound
This paper cites Qwen2.5-VL Technical Report.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing Qwen2.5-VL Technical Report
Reference 2
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-10T13:55:28.943074Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation e9eb6281-4bf4-4163-942a-6f70a5b2385d · outbound
This paper cites PaLI: A Jointly-Scaled Multilingual Language-Image Model.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing PaLI: A Jointly-Scaled Multilingual Language-Image Model
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T09:29:06.753688Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation be95497e-1c19-49f7-984d-9c1a7e3db8a4 · outbound
This paper cites Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
Reference 4
- Resolution
- verified exact
- local_arxiv, observed 2026-05-10T13:55:28.882325Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 66deedc9-2dc7-486d-9b70-61735715618a · outbound
This paper cites MobileVLM V2: Faster and Stronger Baseline for Vision Language Model.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing MobileVLM V2: Faster and Stronger Baseline for Vision Language Model
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T15:27:52.297815Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation e6bf72af-f900-4464-94c8-2f652b3890ac · outbound
This paper cites Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Reference 6
- Resolution
- verified exact
- local_arxiv, observed 2026-05-10T13:55:28.886711Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 9ada7a52-d94c-42ab-8348-381645d93efc · outbound
This paper cites Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T13:55:28.864017Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 41dffd84-efdf-410f-a7cf-d3cda9244948 · outbound
This paper cites Fuse-rsvlm: Feature fusion vision-language model for remote sensing.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing Fuse-rsvlm: Feature fusion vision-language model for remote sensing
Reference 8
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-10T13:55:28.859406Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation e02b6a43-6fbf-4be8-9971-81e4cdcd2f23 · outbound
This paper cites GPT-4o System Card.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing GPT-4o System Card
Reference 9
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-10T13:55:28.868449Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 43793d89-f142-4146-9280-b7ba2545f853 · outbound
This paper cites Perceiver IO: A General Architecture for Structured Inputs & Outputs.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing Perceiver IO: A General Architecture for Structured Inputs & Outputs
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T19:47:14.368858Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 803264ab-af06-49ee-bafc-52a41ccd21ae · outbound
This paper cites LLaVA-OneVision: Easy Visual Task Transfer.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing LLaVA-OneVision: Easy Visual Task Transfer
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T13:55:28.841281Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 758ae707-d055-446e-a5f7-fef6647e10f9 · outbound
This paper cites Zoomearth: Active perception for ultra-high-resolution geospatial vision-language tasks.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing Zoomearth: Active perception for ultra-high-resolution geospatial vision-language tasks
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T13:55:28.877832Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 4694d5be-3c81-497e-80d9-5804e5b594f3 · outbound
This paper cites DeepSeek-VL: Towards Real-World Vision-Language Understanding.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing DeepSeek-VL: Towards Real-World Vision-Language Understanding
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T17:58:54.896552Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation d7e78842-bf47-44bf-b04b-89513cee8a1e · outbound
This paper cites Feast Your Eyes: Mixture-of-Resolution Adaptation for Multimodal Large Language Models.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing Feast Your Eyes: Mixture-of-Resolution Adaptation for Multimodal Large Language Models
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T13:55:28.929452Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 05ac4714-57d8-430b-8b4c-7af0fb851665 · outbound
This paper cites Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignment.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignment
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T13:55:28.947914Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 1fe73360-2323-4e6d-b566-5b72059d795d · outbound
This paper cites Pang, C., Wu, J., Li, J., Liu, Y ., Sun, J., Li, W., Weng, X., Wang, S., Feng, L., Xia, G.-S., et al.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing Pang, C., Wu, J., Li, J., Liu, Y ., Sun, J., Li, W., Weng, X., Wang, S., Feng, L., Xia, G.-S., et al
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T20:32:51.638803Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation c67f5314-4cc2-4240-a3f8-dcd1b3d58f48 · outbound
This paper cites Gemini: A Family of Highly Capable Multimodal Models.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing Gemini: A Family of Highly Capable Multimodal Models
Reference 17
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-10T13:55:28.895324Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation e514c6c9-a609-403d-9c4c-39e1164a92fe · outbound
This paper cites Geollava-8k: Scaling remote-sensing multimodal large language models to 8k resolution.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing Geollava-8k: Scaling remote-sensing multimodal large language models to 8k resolution
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T13:55:28.919701Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 3d400f2f-eff3-4772-a8dd-bb6c7efc96e3 · outbound
This paper cites InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
Reference 19
- Resolution
- verified exact
- local_arxiv, observed 2026-05-10T13:55:28.900179Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation b093fa26-d4ce-4b8a-97aa-daa1ec2ec902 · outbound
This paper cites HART: High-Resolution Annotation-Free Reasoning Technique through a Closed-loop Framework.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing HART: High-Resolution Annotation-Free Reasoning Technique through a Closed-loop Framework
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-07-09T02:19:51.127350Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 1e1f7aad-727b-41f6-a4f9-25ea935e6e7f · outbound
This paper cites MiniCPM-V: A GPT-4V Level MLLM on Your Phone.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing MiniCPM-V: A GPT-4V Level MLLM on Your Phone
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T21:07:32.698795Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation f7842195-dade-46fb-95ff-89cf4b2f0476 · outbound
This paper cites TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T13:55:28.939019Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 9317b03e-b415-4d34-9062-8c89946060ef · outbound
This paper cites InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-17T13:48:49.136768Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 3ff5b6d1-3237-43c4-840f-74ce213dd73e · outbound
This paper cites InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-05-17T10:46:29.265418Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation f4ffe41d-642d-4a2d-98f9-0c39c02f2819 · outbound
This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
Reference 25
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T20:37:02.028216Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 813d64ae-fc82-4dce-abdf-2ebc52c07bc6 · outbound
This paper cites InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
Reference 26
- Resolution
- verified exact
- local_arxiv, observed 2026-05-10T13:55:28.951954Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation f085f98e-51fb-4643-ae48-a07375c0a9be · outbound
This paper cites Background.
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T20:32:51.644519Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 6cc63cf4-b307-4574-afd0-0f5c3cc17ba4 · outbound
This paper cites Regarding our model, in the case of XLRS-Bench, the budget Bs is assigned as 180,1320,1600 , and 8000 corresponding to the four resolutions.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing Regarding our model, in the case of XLRS-Bench, the budget Bs is assigned as 180,1320,1600 , and 8000 corresponding to the four resolutions
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T20:32:51.641982Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 94aa12ee-4d6a-4e80-ae1f-c0c9e6d44b19 · outbound
This paper cites By integrating spatial distance, the clustering process effectively groups tokens that are both semantically similar and geographically close.
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing By integrating spatial distance, the clustering process effectively groups tokens that are both semantically similar and geographically close
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T20:32:51.647864Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Pith citing papers
No inbound Pith citation observations are available.