mlsanityData to 14 Oct 2026

CamAgent: An LLM-Agent Framework for Multi-Species Camera-Trap Workflows

arXiv 2609.39112 ↗First posted 30 Sep 2026cs.CVcited by 0

On the map: LLM agents and tool use → Multimodal and embodied agents

Abstract (arXiv)

Camera traps accumulated vast, multidimensional data for wildlife monitoring, yet translating raw media archives into meaningful ecological insights remains highly fragmented. Current research workflows require laboriously stitching together disparate analysis tools and scripts, creating steep programming hurdles and complicating end-to-end spatiotemporal analyses. To overcome this fragmentation, we present CamAgent, an autonomous Large Language Model (LLM) agent framework that integrates camera-trap analytical workflows into a unified intelligent ecosystem. CamAgent interprets natural-language ecological intent, schedules computational routing, and executes specialized tools spanning computer-vision perception (e.g., SpeciesNet), CamtrapDP-compatible data management, detection-corrected occupancy modeling, temporal activity analysis, and species co-occurrence networks. The framework automates multi-stage analytical pipelines while maintaining essential data-quality controls and analytical conventions. Consequently, CamAgent significantly reduces manual programming overhead for conservationists, establishing a transparent, scalable, and fully integrated paradigm for camera-trap ecology. Our project is available at https://anonymous.4open.science/r/artifact72c6f4.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 8 of its 41 references that are in the map. First posted on arXiv 30 Sep 2026.

Builds on

8 of 41 references.

References · 8

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  3. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,242
  4. 2018Recognition in Terra Incognitacited by 658
  5. 2026Towards End-to-End Automation of AI Researchcited by 159
  6. 2020PP-OCR: A Practical Ultra Lightweight OCR Systemcited by 151
  7. 2021Seeing biodiversity: perspectives in machine learning for wildlife conservationcited by 130
  8. 2019Efficient Pipeline for Camera Trap Image Reviewcited by 79

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  1. 2024Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data
  2. 2026Automated Species Identification in Camera Trap Images for Wildlife Conservation
  3. 2023Multimodal Foundation Models for Zero-shot Animal Species Recognition in Camera Trap Images
  4. 2026Can Edge-Deployable Vision-Language Models Identify Species?
  5. 2026Scout: Open-World Species Recognition on the Edge
  6. 2026WildIng: A Wildlife Image Invariant Representation Model for Geographical Domain Shift
  7. 2025MetaWild: A Multimodal Dataset for Animal Re-Identification with Environmental Metadata
  8. 2026Advancing Wildlife Conservation through Multimodal Animal Re-Identification with Environmental Metadata