Paper Citation Record · LEDGER

DetGPT: Detect What You Need via Reasoning

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 20 inbound Pith citation observations for arXiv:2305.14167.

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

pith.paper-citation-record.v1
2305.14167 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 20 of 20 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:56:36.453824Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T10:48:03.072505Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 19bbc267-2a4d-4ac2-9fee-10ac288c2150 · inbound

A Survey on Multimodal Large Language Models cites this paper.

A Survey on Multimodal Large Language Models DetGPT: Detect What You Need via Reasoning

Reference 74

Resolution: verified exact; arxiv_id, observed 2026-05-16T02:56:42.566287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ba37d5b8-15de-4243-91ae-5f6308e18f60 · inbound

A Comprehensive Overview of Large Language Models cites this paper.

A Comprehensive Overview of Large Language Models DetGPT: Detect What You Need via Reasoning

Reference 283

Resolution: verified exact; arxiv_id, observed 2026-05-19T20:28:39.256297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 31e5778f-e762-403f-a85b-deadf8e9ce14 · inbound

MoE-LLaVA: Mixture of Experts for Large Vision-Language Models cites this paper.

MoE-LLaVA: Mixture of Experts for Large Vision-Language Models DetGPT: Detect What You Need via Reasoning

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-16T02:33:30.373244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3c9d1cda-54f3-45cf-bcb3-cfaaed5176d0 · inbound

MobileVLM V2: Faster and Stronger Baseline for Vision Language Model cites this paper.

MobileVLM V2: Faster and Stronger Baseline for Vision Language Model DetGPT: Detect What You Need via Reasoning

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-18T15:27:52.077756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3dfb30a6-7d31-4adf-8091-bd62893314cb · inbound

CCExpert: Advancing MLLM Capability in Remote Sensing Change Captioning with Difference-Aware Integration and a Foundational Dataset cites this paper.

CCExpert: Advancing MLLM Capability in Remote Sensing Change Captioning with Difference-Aware Integration and a Foundational Dataset DetGPT: Detect What You Need via Reasoning

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-12T18:41:08.144219Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0122c3b3-6318-48f1-8305-a6b8ec8deb41 · inbound

Agri-LLaVA: Knowledge-Infused Large Multimodal Assistant on Agricultural Pests and Diseases cites this paper.

Agri-LLaVA: Knowledge-Infused Large Multimodal Assistant on Agricultural Pests and Diseases DetGPT: Detect What You Need via Reasoning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T23:50:37.074294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db7e7b6e-c546-499e-903e-621b3554cf69 · inbound

EditScout: Locating Forged Regions from Diffusion-based Edited Images with Multimodal LLM cites this paper.

EditScout: Locating Forged Regions from Diffusion-based Edited Images with Multimodal LLM DetGPT: Detect What You Need via Reasoning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T22:07:33.037243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c8f212d-9576-4172-af34-77b7d3eac30b · inbound

Visual Large Language Models for Generalized and Specialized Applications cites this paper.

Visual Large Language Models for Generalized and Specialized Applications DetGPT: Detect What You Need via Reasoning

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-10T22:08:09.205049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c94a075a-25eb-4cd9-9294-99b3301a9e76 · inbound

Efficient Reasoning with Hidden Thinking cites this paper.

Efficient Reasoning with Hidden Thinking DetGPT: Detect What You Need via Reasoning

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-23T04:47:33.539696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation aa61e129-2dee-4416-ba92-2224c6c14788 · inbound

WorldSense: Evaluating Real-world Omnimodal Understanding for Multimodal LLMs cites this paper.

WorldSense: Evaluating Real-world Omnimodal Understanding for Multimodal LLMs DetGPT: Detect What You Need via Reasoning

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-17T05:53:26.411359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f697885c-f8e1-4f19-b77f-9c5f688a0203 · inbound

From Objects to Events: Unlocking Complex Visual Understanding in Object Detectors via LLM-guided Symbolic Reasoning cites this paper.

From Objects to Events: Unlocking Complex Visual Understanding in Object Detectors via LLM-guided Symbolic Reasoning DetGPT: Detect What You Need via Reasoning

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-08T17:46:32.035923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8bc7214d-8c26-4f91-8549-7a00ad107823 · inbound

HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluation cites this paper.

HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluation DetGPT: Detect What You Need via Reasoning

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:36.453824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 485d3758-0e6a-45bd-87f9-117d92c46fdb · inbound

MR. Judge: Multimodal Reasoner as a Judge cites this paper.

MR. Judge: Multimodal Reasoner as a Judge DetGPT: Detect What You Need via Reasoning

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-15T20:18:52.289028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfbc826a-a9ae-41dc-a853-cadfdb4e4d08 · inbound

Spatial-MLLM: Boosting MLLM Capabilities in Visual-based Spatial Intelligence cites this paper.

Spatial-MLLM: Boosting MLLM Capabilities in Visual-based Spatial Intelligence DetGPT: Detect What You Need via Reasoning

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-16T08:34:36.913740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 29cc9cfe-5045-4524-93c7-2073c24f3a36 · inbound

Spatial-MLLM: Boosting MLLM Capabilities in Visual-based Spatial Intelligence cites this paper.

Spatial-MLLM: Boosting MLLM Capabilities in Visual-based Spatial Intelligence DetGPT: Detect What You Need via Reasoning

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-22T01:00:51.355721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f0717450-6257-4e36-8e3a-1b0e092292d2 · inbound

KptLLM++: Towards Generic Keypoint Comprehension with Large Language Model cites this paper.

KptLLM++: Towards Generic Keypoint Comprehension with Large Language Model DetGPT: Detect What You Need via Reasoning

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T17:22:12.156699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70cc9fc4-fe99-41b9-b7a0-fb4f3ede6b9c · inbound

DriveQA: Passing the Driving Knowledge Test cites this paper.

DriveQA: Passing the Driving Knowledge Test DetGPT: Detect What You Need via Reasoning

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-05T13:58:09.831701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d439cbf9-a9f4-4c9a-b866-523447b75dd8 · inbound

Towards Sparse Video Understanding and Reasoning cites this paper.

Towards Sparse Video Understanding and Reasoning DetGPT: Detect What You Need via Reasoning

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-02T23:33:13.394055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e77257ba-9108-4f2d-ad73-f404fef7d292 · inbound

InternVideo3: Agentify Foundation Models with Multimodal Contextual Reasoning cites this paper.

InternVideo3: Agentify Foundation Models with Multimodal Contextual Reasoning DetGPT: Detect What You Need via Reasoning

Reference 107

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T10:48:03.074583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d2baaa12-d800-48d6-b78b-e8d4b2549eba · inbound

VADER: Adaptive Debiasing for Hallucination Mitigation in Video Large Language Models cites this paper.

VADER: Adaptive Debiasing for Hallucination Mitigation in Video Large Language Models DetGPT: Detect What You Need via Reasoning

Reference 236

Resolution: unresolved; no resolver link, observed 2026-08-14T04:35:52.880326Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.