Paper Citation Record · LEDGER

Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation

As of 19 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2507.17204.

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

pith.paper-citation-record.v1
2507.17204 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:58:44.224235Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

16 of 16 outbound references displayed

verified exact0
verified fuzzy2
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 988356ee-b128-436c-a8ef-0b97bc76ba13 · outbound

This paper cites Mistral 7B.

Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation Mistral 7B

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T14:58:42.953734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0a3a464-39ed-4780-8357-e9719c8c46eb · outbound

This paper cites IPS: In-Prompt Process Supervision for Short Video Content Moderation.

Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation IPS: In-Prompt Process Supervision for Short Video Content Moderation

Reference 5

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T14:58:45.318999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 71de29d7-7c0f-430c-8a2d-0990e53459ca · outbound

This paper cites Adapting Large Language Models for Content Moderation: Pitfalls in Data Engineering and Supervised Fine-tuning.

Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation Adapting Large Language Models for Content Moderation: Pitfalls in Data Engineering and Supervised Fine-tuning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T14:58:43.165069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f39de5d2-e178-4293-996e-9311c4bec4e9 · outbound

This paper cites Enhancing Few-Shot Vision-Language Classification with Large Multimodal Model Features.

Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation Enhancing Few-Shot Vision-Language Classification with Large Multimodal Model Features

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T14:58:43.281185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23627c4d-aaaf-44fb-9531-01c815843b04 · outbound

This paper cites GPT-4 Technical Report.

Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation GPT-4 Technical Report

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T14:58:43.372402Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67ba5547-cdf6-48cf-a087-e56304231486 · outbound

This paper cites In Findings of the Association for Computational Linguistics: ACL 2024, pages 5735– 5748, Bangkok, Thailand.

Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation In Findings of the Association for Computational Linguistics: ACL 2024, pages 5735– 5748, Bangkok, Thailand

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:58:45.799257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation afba8959-19d8-42b6-9574-00a5845b1a6e · outbound

This paper cites Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs.

Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T14:58:43.493356Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1087e48-b2b8-41dd-92b6-02c36f16c4de · outbound

This paper cites ICM-Assistant: Instruction-tuning Multimodal Large Language Models for Rule-based Explainable Image Content Moderation.

Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation ICM-Assistant: Instruction-tuning Multimodal Large Language Models for Rule-based Explainable Image Content Moderation

Reference 11

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T14:58:45.040986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 50e0c0df-1fa7-4c8f-b19f-bb0dcda53901 · outbound

This paper cites FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data.

Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data

Reference 12

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T14:58:44.850381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 962b06d7-7772-4a20-b809-db75de7a2320 · outbound

This paper cites USM: Unbiased Survey Modeling for Limiting Negative User Experiences in Recommendation Systems.

Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation USM: Unbiased Survey Modeling for Limiting Negative User Experiences in Recommendation Systems

Reference 13

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T14:58:44.559603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5b47dfc0-6f42-40af-9456-f23975f858c7 · outbound

This paper cites Why are Visually-Grounded Language Models Bad at Image Classification?.

Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation Why are Visually-Grounded Language Models Bad at Image Classification?

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T14:58:44.118082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a19600b-785c-4f81-89fa-571c33ae0777 · outbound

This paper cites In Findings of the Association for Computational Linguistics: ACL 2024, pages 10057–10084, Bangkok, Thailand.

Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation In Findings of the Association for Computational Linguistics: ACL 2024, pages 10057–10084, Bangkok, Thailand

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:58:45.567416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e4f707dc-4835-4fa4-8c53-b371b38b35ad · outbound

This paper cites Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts.

Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T14:58:44.002049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37823ac5-a2e9-497d-9586-7e39cc2bea91 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T14:58:42.783915Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 455e2a99-45a1-4abe-8724-6e181a5c3fde · outbound

This paper cites MLLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training.

Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation MLLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T14:58:42.431248Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57437c8c-ddc2-45a8-aa45-aa22c5afefaa · outbound

This paper cites Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?.

Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T14:58:42.623068Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.