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Paper Citation Record · LEDGER

Large Models in Dialogue for Active Perception and Anomaly Detection

As of 11 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2501.16300.

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

pith.paper-citation-record.v1
2501.16300 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T13:36:47.038964Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

  • verified exact3
  • verified fuzzy13
  • unresolved19
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 57b3e36c-01d4-4c2f-9d4d-964a380f73df · outbound

This paper cites Drone-surveillance for search and rescue in natural disaster,.

Large Models in Dialogue for Active Perception and Anomaly Detection Drone-surveillance for search and rescue in natural disaster,

Reference 1

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Observation a074eb6b-2ec7-4dc7-9876-858f5084837d · outbound

This paper cites Uav-based surveillance sys- tem: an anomaly detection approach,.

Large Models in Dialogue for Active Perception and Anomaly Detection Uav-based surveillance sys- tem: an anomaly detection approach,

Reference 2

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Observation 277fe239-e82f-4781-848f-d604ceec5d7b · outbound

This paper cites Anomaly detection, localization and classification for railway inspection,.

Large Models in Dialogue for Active Perception and Anomaly Detection Anomaly detection, localization and classification for railway inspection,

Reference 3

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Observation 562fa8e3-5678-4b6a-8e7b-43c69ad1c712 · outbound

This paper cites Smart Autopilot Drone System for Surface Surveillance and Anomaly Detection via Customizable Deep Neural Network,.

Large Models in Dialogue for Active Perception and Anomaly Detection Smart Autopilot Drone System for Surface Surveillance and Anomaly Detection via Customizable Deep Neural Network,

Reference 4

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source=pdf_text observed=2026-08-10T13:36:46.925312Z digest=sha256:f429f91ed91b545a1e93926df45d1dc23131baffb8c5d32fb46802d08dd27d6b

Observation 1b39fa36-b1f5-4cb0-85aa-a096ed0140fd · outbound

This paper cites An Autonomous Drone Surveil- lance and Tracking Architecture,.

Large Models in Dialogue for Active Perception and Anomaly Detection An Autonomous Drone Surveil- lance and Tracking Architecture,

Reference 5

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T13:36:46.929565Z digest=sha256:be11db6fa91ecffb929d0539a11c2e320f661f85a7f4f218b110e2666945cd19

Observation 53107d5f-79dc-4e74-994b-decde82468ef · outbound

This paper cites Revisiting active perception,.

Large Models in Dialogue for Active Perception and Anomaly Detection Revisiting active perception,

Reference 6

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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.

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Observation 832c2cf6-c865-4a32-b1dd-b4c4d0b76d48 · outbound

This paper cites How to select and use tools?: Active perception of target objects using multimodal deep learning,.

Large Models in Dialogue for Active Perception and Anomaly Detection How to select and use tools?: Active perception of target objects using multimodal deep learning,

Reference 7

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 620c0bf9-d90a-49b4-a1d9-2c7d3969f640 · outbound

This paper cites Enabling high-resolution pose estimation in real time using active perception,.

Large Models in Dialogue for Active Perception and Anomaly Detection Enabling high-resolution pose estimation in real time using active perception,

Reference 8

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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.

source=pdf_text observed=2026-08-10T13:36:46.941320Z digest=sha256:1e6c00c03d0bb186d8014d001f16d9d52c7b6d9e4d4f26c57bd059886e6a28cb

Observation 6a24c768-beac-4a7d-bf61-1f5a1ee340cb · outbound

This paper cites VQA: Visual Question Answering.

Large Models in Dialogue for Active Perception and Anomaly Detection VQA: Visual Question Answering

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T13:36:46.944501Z digest=sha256:60193e60add574201c959b392f1740876b597847cb8882054eb8ec79a242fa88

Observation bd11874c-d85c-4564-94bb-0c22a67b3ece · outbound

This paper cites Mqa: Answering the question via robotic manipulation,.

Large Models in Dialogue for Active Perception and Anomaly Detection Mqa: Answering the question via robotic manipulation,

Reference 10

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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.

source=pdf_text observed=2026-08-10T13:36:46.948221Z digest=sha256:2f0a6350637387813acdc25d832e720778e6becc31d6bf517dc640643ac29960

Observation da900d3b-1e19-4d97-b39b-f5d63e0bb375 · outbound

This paper cites IQA: Visual Question Answering in Interactive Environments.

Large Models in Dialogue for Active Perception and Anomaly Detection IQA: Visual Question Answering in Interactive Environments

Reference 11

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source=pdf_text observed=2026-08-10T13:36:46.952315Z digest=sha256:a3ec1c5a1f0385a467dfd48f320e939156a1262269fca40cba9a8cc8ba31f2de

Observation 13afe0da-296d-4e28-a43b-af366314ef4b · outbound

This paper cites Embodied Question Answering.

Large Models in Dialogue for Active Perception and Anomaly Detection Embodied Question Answering

Reference 12

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source=pdf_text observed=2026-08-10T13:36:46.957015Z digest=sha256:cd9ad7e83f1265bf83de0b1d78d263e1f6046b5ad9f285738a595c44b951acb1

Observation cbe56b3e-4bca-4619-8965-da5ec86f36c3 · outbound

This paper cites ChatGPT for Robotics: Design Principles and Model Abilities.

Large Models in Dialogue for Active Perception and Anomaly Detection ChatGPT for Robotics: Design Principles and Model Abilities

Reference 13

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source=pdf_text observed=2026-08-10T13:36:46.960733Z digest=sha256:8bbaece4ff71dc09ce805ae55dfc59ea06c4d183e683987cd1dc883570574288

Observation 1be16067-693c-42cf-8be6-e2497f774153 · outbound

This paper cites From words to flight: Integrating openai chatgpt with px4/gazebo for natural language-based drone control,.

Large Models in Dialogue for Active Perception and Anomaly Detection From words to flight: Integrating openai chatgpt with px4/gazebo for natural language-based drone control,

Reference 14

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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.

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Observation 7f599fab-b503-46dc-875c-d68c812bd158 · outbound

This paper cites Improved Trust in Human-Robot Collaboration with ChatGPT.

Large Models in Dialogue for Active Perception and Anomaly Detection Improved Trust in Human-Robot Collaboration with ChatGPT

Reference 15

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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.

source=pdf_text observed=2026-08-10T13:36:46.967076Z digest=sha256:4f1b63b3121a27c6fa7a864bf11c941479d268ba8b9804316d243f6302ef01c7

Observation c527dd44-b5a7-4888-b178-4e6a5d642e5f · outbound

This paper cites Code as Policies: Language Model Programs for Embodied Control.

Large Models in Dialogue for Active Perception and Anomaly Detection Code as Policies: Language Model Programs for Embodied Control

Reference 16

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Observation 173042e0-8a50-4801-bf65-0e12f766c645 · outbound

This paper cites Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models.

Large Models in Dialogue for Active Perception and Anomaly Detection Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models

Reference 17

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source=pdf_text observed=2026-08-10T13:36:46.974893Z digest=sha256:b0fa88c6958473743ffc5f0b929fe042b699ca5c074f5230d05596d8ae950d08

Observation daf1eb64-5784-4abf-be05-02e01b005862 · outbound

This paper cites HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face.

Large Models in Dialogue for Active Perception and Anomaly Detection HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face

Reference 18

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Observation d322d50f-c322-4a2d-b23c-5cf795ace02f · outbound

This paper cites NExT-GPT: Any-to-Any Multimodal LLM.

Large Models in Dialogue for Active Perception and Anomaly Detection NExT-GPT: Any-to-Any Multimodal LLM

Reference 19

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Observation 2344987a-d7e5-43f0-abe9-cc1f789d65fc · outbound

This paper cites CLIPort: What and Where Pathways for Robotic Manipulation.

Large Models in Dialogue for Active Perception and Anomaly Detection CLIPort: What and Where Pathways for Robotic Manipulation

Reference 20

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source=pdf_text observed=2026-08-10T13:36:46.986323Z digest=sha256:71632a4c9bdc1a990960e2f1652353e693e827987a354c578b6a0d82d3707e99

Observation bff4d29b-e310-4ea5-a42d-8c335460711c · outbound

This paper cites Reshaping Robot Trajectories Using Natural Language Commands: A Study of Multi-Modal Data Alignment Using Transformers.

Large Models in Dialogue for Active Perception and Anomaly Detection Reshaping Robot Trajectories Using Natural Language Commands: A Study of Multi-Modal Data Alignment Using Transformers

Reference 21

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source=pdf_text observed=2026-08-10T13:36:46.990166Z digest=sha256:ba6e437ed8e6958f4f08416d0ae2e5cbd109ec4fe1ba95f36c5b2aa40ff6f5d7

Observation 52183c54-24ef-4bac-85e0-c49cd5f09f8f · outbound

This paper cites Language-Conditioned Imitation Learning for Robot Manipulation Tasks.

Large Models in Dialogue for Active Perception and Anomaly Detection Language-Conditioned Imitation Learning for Robot Manipulation Tasks

Reference 22

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Observation 1d5e2dff-c3ec-4264-a377-9251356a8c71 · outbound

This paper cites ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions.

Large Models in Dialogue for Active Perception and Anomaly Detection ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions

Reference 23

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Observation 010e847c-9302-424c-a73c-063377be89db · outbound

This paper cites FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions.

Large Models in Dialogue for Active Perception and Anomaly Detection FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions

Reference 24

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Observation d76c90cc-2e2e-4689-92bf-42705971823b · outbound

This paper cites Chatting Makes Perfect: Chat-based Image Retrieval.

Large Models in Dialogue for Active Perception and Anomaly Detection Chatting Makes Perfect: Chat-based Image Retrieval

Reference 25

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local_arxiv, observed 2026-08-10T13:36:47.164816Z

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.

source=pdf_text observed=2026-08-10T13:36:47.005170Z digest=sha256:f0d88363b826586a5b8b456305f38ef5c46327c1c3ec04d2e82361460f417484

Observation e2cadad3-4a4a-4084-b589-f3f46edb67f9 · outbound

This paper cites Machine-to-machine visual dialoguing with chatgpt for enriched textual image description,.

Large Models in Dialogue for Active Perception and Anomaly Detection Machine-to-machine visual dialoguing with chatgpt for enriched textual image description,

Reference 26

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T13:36:47.008423Z digest=sha256:00a91fa7697430f18b823ccecae34d1ce12b8bd165c609dc33bb982c83d2b2df

Observation f25cd4fd-ab43-4b69-9d3b-cccf5b3696c3 · outbound

This paper cites PromptCap: Prompt-Guided Task-Aware Image Captioning.

Large Models in Dialogue for Active Perception and Anomaly Detection PromptCap: Prompt-Guided Task-Aware Image Captioning

Reference 27

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Observation 34153d35-58dc-42d9-a02e-55e44b1724a3 · outbound

This paper cites Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question Answering.

Large Models in Dialogue for Active Perception and Anomaly Detection Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question Answering

Reference 28

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local_arxiv, observed 2026-08-10T13:36:47.139732Z

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.

source=pdf_text observed=2026-08-10T13:36:47.015132Z digest=sha256:9e8aaaae5e6a6636a2a4de567164a71b4c1682ee6198a0ad6c2e528871195297

Observation 0249b1ac-f2ad-487e-af56-079b4dfe6739 · outbound

This paper cites VLC-BERT: Visual Question Answering with Contextualized Commonsense Knowledge.

Large Models in Dialogue for Active Perception and Anomaly Detection VLC-BERT: Visual Question Answering with Contextualized Commonsense Knowledge

Reference 29

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source=pdf_text observed=2026-08-10T13:36:47.018356Z digest=sha256:ff23bebcb605dc2d5ab1045b36001c2608afff9160c27c3c9f6c44327046b29d

Observation 23cb99ee-bade-4bd4-b0ee-3bb7ff2ac78d · outbound

This paper cites Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training.

Large Models in Dialogue for Active Perception and Anomaly Detection Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training

Reference 30

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source=pdf_text observed=2026-08-10T13:36:47.021721Z digest=sha256:87623aaa757a3c09768349b440c1d794bb06f1144752aadee3323e8faccbda0b

Observation 887e33d7-f74e-4daa-94d3-23655e2efce0 · outbound

This paper cites BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation.

Large Models in Dialogue for Active Perception and Anomaly Detection BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

Reference 31

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Observation 38739c18-28b0-4a8e-90ec-ad2667964874 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localiza- tion,.

Large Models in Dialogue for Active Perception and Anomaly Detection Grad-cam: Visual explanations from deep networks via gradient-based localiza- tion,

Reference 32

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raw_fallback, observed 2026-08-10T13:36:47.326532Z

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.

source=pdf_text observed=2026-08-10T13:36:47.028281Z digest=sha256:5df9412111eee73985f98189f48cb22e981ce5fe7f6cfbe9ceb4556d03523af4

Observation 0b38760c-98d3-4267-946a-45dc47b5ff23 · outbound

This paper cites Language models are few-shot learners,.

Large Models in Dialogue for Active Perception and Anomaly Detection Language models are few-shot learners,

Reference 33

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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.

source=pdf_text observed=2026-08-10T13:36:47.032272Z digest=sha256:bcda1b38198ddc96d106c2b6ce2bf97e310f60042f285a437fe179197b8393b1

Observation fe8a8b84-3886-4aec-89ea-9975e9b20918 · outbound

This paper cites Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models.

Large Models in Dialogue for Active Perception and Anomaly Detection Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

Reference 34

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T13:36:47.035424Z digest=sha256:febcbbcbb21d76175d90fba0a733fc380e44859e1efdcc9d9e21a5c25bfc1cf0

Observation c7835dad-8a51-4355-94f4-56c73e1af144 · outbound

This paper cites AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles.

Large Models in Dialogue for Active Perception and Anomaly Detection AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles

Reference 35

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T13:36:47.038964Z digest=sha256:178478d22610f17915f39c1046c3c6f7190a82f9414add51e67e99b16fb654ae

Pith citing papers

No inbound Pith citation observations are available.