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Paper Citation Record · LEDGER

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG

As of 8 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2607.24313.

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

pith.paper-citation-record.v1
2607.24313 v1

Coverage vector

measured 34 of 34 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-07-31T18:28:55.470070Z

measured 34 of 34 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

34 of 34 outbound references displayed

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  • verified fuzzy0
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External citation measurements

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Outbound references

Observation 192a5117-5a6f-4199-86d3-355f77215c45 · outbound

This paper cites FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains

Reference 1

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Observation 3f1cd8d7-5200-4278-aef5-7452f7ab6fd0 · outbound

This paper cites AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis

Reference 2

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Observation 0d81d951-2646-4c4a-b023-f429c2977421 · outbound

This paper cites MAX78002: Artificial Intelligence Micro- controller with Low-Power Convolutional Neural Network Accel- erator.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG MAX78002: Artificial Intelligence Micro- controller with Low-Power Convolutional Neural Network Accel- erator

Reference 3

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Observation 5b2ae750-1319-4b5c-9dcc-f58bd625ecc9 · outbound

This paper cites MAX78002 Artificial Intelligence Micro- controller with Low-Power Convolutional Neural Network Acceler- ator Data Sheet.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG MAX78002 Artificial Intelligence Micro- controller with Low-Power Convolutional Neural Network Acceler- ator Data Sheet

Reference 4

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Observation 1f4b7fac-6515-49af-ac46-d4d8a13c6d6d · outbound

This paper cites OzFish Dataset - Machine Learning Dataset for Baited Remote Underwater Video Stations.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG OzFish Dataset - Machine Learning Dataset for Baited Remote Underwater Video Stations

Reference 5

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Observation 5e955668-c5e8-4edf-83e2-7d147733060a · outbound

This paper cites Real-time reporting of baleen whale passive acoustic detections from ocean gliders.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG Real-time reporting of baleen whale passive acoustic detections from ocean gliders

Reference 6

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Observation ded13481-7b2c-4e53-89f9-a5dc2e8e4897 · outbound

This paper cites YOLO-World: Real-Time Open-Vocabulary Object Detection.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG YOLO-World: Real-Time Open-Vocabulary Object Detection

Reference 7

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Observation 744b948c-d4e5-4308-bcaa-fbbda02785f8 · outbound

This paper cites FishBase.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG FishBase

Reference 8

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Observation ddad4f8f-4ff1-4bcd-aba5-8bcee6724568 · outbound

This paper cites CLIBD: Bridging vision and genomics forbiodiversitymonitoringatscale,in:ProceedingsoftheThirteenth InternationalConferenceonLearningRepresentations.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG CLIBD: Bridging vision and genomics forbiodiversitymonitoringatscale,in:ProceedingsoftheThirteenth InternationalConferenceonLearningRepresentations

Reference 9

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Observation 151efb63-9a79-4f5c-bb31-467406bdbe0c · outbound

This paper cites Radio-triggered wake-up for wireless sensor networks.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG Radio-triggered wake-up for wireless sensor networks

Reference 10

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Observation 9fbcb9f9-6a4a-45c0-8dce-37139d02bef8 · outbound

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Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG Unresolved cited work

Reference 11

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Observation 16af1c62-deb2-4c75-b573-660141380375 · outbound

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Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG Unresolved cited work

Reference 12

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Observation a5c0bea9-96a9-46e7-83d2-95015b000089 · outbound

This paper cites Syrene:Anunderwater embeddedartificialintelligencecameraforinvasivefaunamonitoring.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG Syrene:Anunderwater embeddedartificialintelligencecameraforinvasivefaunamonitoring

Reference 13

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Observation c0c806e7-2855-429b-bab1-8190059c9081 · outbound

This paper cites MCUNet: Tiny Deep Learning on IoT Devices.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG MCUNet: Tiny Deep Learning on IoT Devices

Reference 14

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Observation 0e776203-2cc3-4683-a660-c42c921a009e · outbound

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Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG Unresolved cited work

Reference 15

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Observation a4d2ed94-4f85-4c0c-89c4-90616ffe01e7 · outbound

This paper cites Fish-vista: A multi-purpose dataset for understanding & identification of traits from images, in: 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG Fish-vista: A multi-purpose dataset for understanding & identification of traits from images, in: 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp

Reference 16

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Observation 4d097cbc-ab4f-4dc0-95d1-64588b9a7a08 · outbound

This paper cites sleepy- cam: Power management mechanism for wireless video-surveillance cameras,in:2017IEEEInternationalConferenceonCommunications Workshops (ICC Workshops), pp.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG sleepy- cam: Power management mechanism for wireless video-surveillance cameras,in:2017IEEEInternationalConferenceonCommunications Workshops (ICC Workshops), pp

Reference 17

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Observation 3dc206a4-94f4-423a-a940-75dd6846ca60 · outbound

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Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG Unresolved cited work

Reference 18

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Observation 2b5f6d3b-a557-4e82-a116-a5a9a4c73ecc · outbound

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Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG Unresolved cited work

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Observation 5c4da1ad-0e0a-4aab-9c6c-57cbc34efa4c · outbound

This paper cites Flexible and fully quantized lightweight TinyissimoYOLO for ultra-low-power edge systems.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG Flexible and fully quantized lightweight TinyissimoYOLO for ultra-low-power edge systems

Reference 21

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Observation 338d8df9-ee76-4d59-a6ac-165c45b6ede7 · outbound

This paper cites Fishnet: Deep neural networks for low-cost fish stock estimation, in: 2024 IEEE International Conference on Omni- layer Intelligent Systems (COINS), IEEE.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG Fishnet: Deep neural networks for low-cost fish stock estimation, in: 2024 IEEE International Conference on Omni- layer Intelligent Systems (COINS), IEEE

Reference 22

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Observation 73403b7a-2b14-47b2-aee4-c02be690aa6b · outbound

This paper cites Automated species classification and counting by deep-sea mobile crawler platforms using YOLO.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG Automated species classification and counting by deep-sea mobile crawler platforms using YOLO

Reference 23

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Observation 19f06eab-737a-43ef-9151-0d55ede936e0 · outbound

This paper cites AntPi: A raspberry pi based edge-cloud system for real-time ant species detection using YOLO.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG AntPi: A raspberry pi based edge-cloud system for real-time ant species detection using YOLO

Reference 24

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Observation 7a61c9d3-f4c3-4f52-96e5-0d23b9673fa5 · outbound

This paper cites SeaLifeBase.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG SeaLifeBase

Reference 25

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Observation 2f5baed2-e607-4090-a876-28713e8c8423 · outbound

This paper cites Learning transferable visual models from nat- ural language supervision, in: Proceedings of the 38th International Conference on Machine Learning, PMLR.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG Learning transferable visual models from nat- ural language supervision, in: Proceedings of the 38th International Conference on Machine Learning, PMLR

Reference 26

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Observation 21d4d1a8-7e76-428f-8586-8b160919cf60 · outbound

This paper cites A review on tinyml: State-of-the-art and prospects.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG A review on tinyml: State-of-the-art and prospects

Reference 27

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Observation 60b7b6d8-bc7b-488f-b94a-3e53a39cb416 · outbound

This paper cites FMRAG: Retrieval-augmented multimodallargelanguagemodelsforfisheriesintelligence.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG FMRAG: Retrieval-augmented multimodallargelanguagemodelsforfisheriesintelligence

Reference 28

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Observation 3b6b28ac-5d6f-444b-9db6-38c41e68bc0a · outbound

This paper cites BioCLIP: A Vision Foundation Model for the Tree of Life.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG BioCLIP: A Vision Foundation Model for the Tree of Life

Reference 29

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Observation 1198d8c8-2b9a-4804-b041-676f48fce68e · outbound

This paper cites Reliable and efficient integration of AI into camera traps for smart wildlife monitoring based on continual learning.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG Reliable and efficient integration of AI into camera traps for smart wildlife monitoring based on continual learning

Reference 30

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Observation a53513cc-10ce-4fa6-b915-d2d1722b8e82 · outbound

This paper cites SOUND Database of Marine Animal Vocalizations: Structure and Operations.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG SOUND Database of Marine Animal Vocalizations: Structure and Operations

Reference 31

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Observation 929c8f3b-0b49-4e63-b64f-2a88c418a147 · outbound

This paper cites BioTrove: A Large Curated Image Dataset Enabling AI for Biodiversity.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG BioTrove: A Large Curated Image Dataset Enabling AI for Biodiversity

Reference 32

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Observation 18a2da27-22a5-4765-a775-6fac581ceded · outbound

This paper cites Wildfish++:Acomprehensive fish benchmark for multimedia research.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG Wildfish++:Acomprehensive fish benchmark for multimedia research

Reference 33

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Observation 50004be9-2dae-49e0-a17b-53d2f9dfdf86 · outbound

This paper cites Expert Systems with Applications 183, 115270.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG Expert Systems with Applications 183, 115270

Reference 2021

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Observation 2b00f0ce-b534-43d9-bbbf-be4ed1dd5ff6 · outbound

This paper cites doi:10.48550/arXiv.

Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG doi:10.48550/arXiv

Reference 2025

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Pith citing papers

No inbound Pith citation observations are available.