Paper Citation Record · LEDGER

RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants

As of 7 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2607.16243.

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

pith.paper-citation-record.v1
2607.16243 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T09:56:13.602702Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

17 of 17 outbound references displayed

verified exact0
verified fuzzy0
unresolved17
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 368f8203-43e8-40f2-8533-735b50ca9d1a · outbound

This paper cites Phi-4 Technical Report.

RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants Phi-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T09:56:12.052180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 590283b5-2799-4c76-a65a-581a66643f34 · outbound

This paper cites Evaluating LLM Metrics Through Real-World Capabilities.

RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants Evaluating LLM Metrics Through Real-World Capabilities

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T09:56:13.408658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d4a0390-9c78-4ebf-98fd-f7704fe91976 · outbound

This paper cites Slm-bench: A comprehensive benchmark of small language models on environmental impacts.

RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants Slm-bench: A comprehensive benchmark of small language models on environmental impacts

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T09:56:13.517258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8a04340-16c0-4446-a3e6-3f19b7b6c37f · outbound

This paper cites OpenAI GPT-5 System Card.

RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants OpenAI GPT-5 System Card

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T09:56:13.526070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9a0a793-09ab-4cc7-8872-a3204e765b8d · outbound

This paper cites InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency.

RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T09:56:13.536053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53c2da82-7f54-4610-9227-7181ad221b22 · outbound

This paper cites Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning.

RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T09:56:13.548378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e2b66b2-e6e2-4f0a-a5c6-623be771668d · outbound

This paper cites Qwen2.5 Technical Report.

RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants Qwen2.5 Technical Report

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T09:56:13.554260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13283cde-9d76-4911-99d7-a5aa997f4eef · outbound

This paper cites Qwen3 Technical Report.

RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants Qwen3 Technical Report

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T09:56:13.570143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b983336-e74b-4269-b620-1e440b8c5df6 · outbound

This paper cites MiniCPM-V: A GPT-4V Level MLLM on Your Phone.

RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants MiniCPM-V: A GPT-4V Level MLLM on Your Phone

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-02T09:56:13.579487Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ded2336-e74e-4785-bf1c-62f20a8e4a94 · outbound

This paper cites MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe.

RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T09:56:13.589103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7efea19-a9b5-41fa-acbe-02740900c2bf · outbound

This paper cites The inter-judge agreement is high (κ> 0.8; (Landis & Koch, 1977)) across the most critical dimensions: Technical Accuracy, Comprehensiveness, Relevance, and Overall Score.

RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants The inter-judge agreement is high (κ> 0.8; (Landis & Koch, 1977)) across the most critical dimensions: Technical Accuracy, Comprehensiveness, Relevance, and Overall Score

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T09:56:13.602702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4198e69-d8a5-42c9-96ae-3fe926e7a11a · outbound

This paper cites Anomalygpt: Detecting industrial anomalies using large vision-language models.

RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants Anomalygpt: Detecting industrial anomalies using large vision-language models

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-02T09:56:13.089466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f64f4e8-e456-42d9-bc5a-3377813b5579 · outbound

This paper cites Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead.

RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-02T09:56:12.376403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e009162-110a-41d8-b568-b70b2696e5ce · outbound

This paper cites Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling.

RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-02T09:56:12.859451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f5f3fe5-ac4d-4d46-855d-20cb31ddd5c5 · outbound

This paper cites Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs.

RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-02T09:56:12.209945Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe429a8d-304b-4264-92b2-559494a18e8b · outbound

This paper cites Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, et al.

RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, et al

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-02T09:56:12.930114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 368344b8-5407-490d-a23d-06a03dab1986 · outbound

This paper cites Adversarialvqa: Anewbenchmarkforevaluatingtherobustness of vqa models.

RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants Adversarialvqa: Anewbenchmarkforevaluatingtherobustness of vqa models

Reference 2026

Resolution: unresolved; no resolver link, observed 2026-08-02T09:56:13.237311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.