Varalakshmi Thota
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Varalakshmi Thota has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Elyson De La Cruz
University of the Cumberlands, USA
1 shared publication - Karthik Meduri
University of the Cumberlands, Williamsburg, United States, USA
1 shared publication - Madhurika Aila
Belhaven University, USA
1 shared publication - Renjith Kathalikkattil Ravindran
University of Calicut
1 shared publication - Ruthvik Yedla
University of Central Missouri, Warrensburg, United States, USA
1 shared publication - Sumanth Banakar
University of the Cumberlands, USA
1 shared publication
Latest Publications
- Open Access
- 10 July 2026
- Published by MDPI AG in Preprints.org
MemANS: Event-Driven In-Memory Agentic Name Search for Efficient Multimodal Misinformation Detection on a Balanced Fakeddit Benchmark
Multimodal misinformation detection has advanced from text-only classification toward joint processing of language, imagery, and social metadata. Yet much of the literature still treats adaptation as an internal fusion problem rather than a systems-level routing problem, leaving open the question of when multimodal escalation is warranted and how expert usage can be reduced without materially weakening predictive quality. This paper introduces MemANS, an event-driven, in-memory Agentic Name Search architecture for multimodal misinformation detection. MemANS treats inference as a resolution process over named experts: a default text–metadata resolver handles ordinary cases, while a specialist is invoked only when cross-modal disagreement and confidence conditions jointly indicate that additional reasoning is justified. The empirical study is conducted on a balanced, fully image-available Fakeddit benchmark comprising 5400 training instances, 840 validation instances, and 840 test instances across six misinformation classes. On the final test set, MemANS achieves 0.6182 macro-F1, 0.6167 accuracy, 0.5405 MCC, and 0.8726 weighted one-vs-rest AUC, clearly surpassing the always-on fusion baseline (0.5732 macro-F1) with a statistically supported gain of 0.0454 (p < 0.001) while using only 1.7202 average experts versus 2.0000. The results indicate that Agentic Name Search is a viable and practically meaningful multimedia-systems abstraction for adaptive multimodal reasoning.
- 10 July 2026
- Published by MDPI AG in Preprints.org
Open AccessMemANS: Event-Driven In-Memory Agentic Name Search for Efficient Multimodal Misinformation Detection on a Balanced Fakeddit Benchmark
Multimodal misinformation detection has advanced from text-only classification toward joint processing of language, imagery, and social metadata. Yet much of the literature still treats adaptation as an internal fusion problem rather than a systems-level routing problem, leaving open the question of when multimodal escalation is warranted and how expert usage can be reduced without materially weakening predictive quality. This paper introduces MemANS, an event-driven, in-memory Agentic Name Search architecture for multimodal misinformation detection. MemANS treats inference as a resolution process over named experts: a default text–metadata resolver handles ordinary cases, while a specialist is invoked only when cross-modal disagreement and confidence conditions jointly indicate that additional reasoning is justified. The empirical study is conducted on a balanced, fully image-available Fakeddit benchmark comprising 5400 training instances, 840 validation instances, and 840 test instances across six misinformation classes. On the final test set, MemANS achieves 0.6182 macro-F1, 0.6167 accuracy, 0.5405 MCC, and 0.8726 weighted one-vs-rest AUC, clearly surpassing the always-on fusion baseline (0.5732 macro-F1) with a statistically supported gain of 0.0454 (p < 0.001) while using only 1.7202 average experts versus 2.0000. The results indicate that Agentic Name Search is a viable and practically meaningful multimedia-systems abstraction for adaptive multimodal reasoning.