Phil Tinn | Searcharxiv

arXiv2026

A Sonar-Visual Dataset for Cross-Modal Underwater Robot Perception

Underwater robots typically use both cameras and sonar for perception to leverage the rich semantic details of vision and the robust range measurements of acoustics. However, learning to map between these modalities via cross-modal prediction remains underexplored due to limited sonar-visual paired datasets. We present SOVIS, a sonar-visual dataset for cross-modal underwater perception. SOVIS comprises over 76,000 paired frames collected across 17 dives at six sites in the Trondheimfjord, supported by an end-to-end pipeline that cleans and synchronizes the cross-modal sensor data. We also introduce an interactive annotation tool designed to accelerate the labeling process for this paired data. Finally, we demonstrate a proof-of-concept cross-modal fish detection task using a small subset of labeled data, achieving a 7x improvement in mAP@0.10 over a monocular camera baseline. SOVIS serves as the first step toward advancing cross-modal underwater perception research, enabling research directions such as dense sonar prediction from monocular images.

cs.RO↗

arXiv2026

An Agentic Operationalization of DISARM for FIMI Investigation on Social Media

Interoperable data and intelligence flows among allied partners and operational end-users remain essential to NATO's collective defense across both conventional and hybrid threat environments. Foreign Information Manipulation and Interference (FIMI) increasingly spans multiple societal domains and information ecosystems, complicating threat characterization, persistent situational awareness, and coordinated response. Concurrent advances in AI have further lowered the barrier to conducting large-scale, AI-augmented FIMI activities -- including automated generation, personalization, and amplification of manipulative content. While frameworks such as DISARM offer a standardized analytical and metadata schema for characterizing FIMI incidents, their practical application for automating large-scale detection remains challenging. We present a framework-agnostic, agent-based operationalization of DISARM piloted to support FIMI investigation on social platforms. Our agent coordination pipeline integrates general agentic AI components that (1) identify candidate manipulative behaviors in social-media data and (2) map these behaviors to DISARM taxonomies through transparent, auditable reasoning steps. Evaluation on two practitioner-annotated, real-world datasets demonstrates that our approach can effectively scale analytic workflows that are currently manual, time-intensive, and interpretation-heavy. Notably, the experiment surfaced more than 30 previously undetected Russian bot accounts -- deployed for the 2025 election in Moldova -- during the prior non-agentic investigation. By enhancing analytic throughput, interoperability, and explainability, the proposed approach provides a direct contribution to defense policy and planning needs for improved situational awareness, cross-partner data integration, and rapid assessment of information-environment threats.

cs.SI↗