Deja Vu in Plots: Leveraging Cross-Session Evidence with Retrieval-Augmented LLMs for Live Streaming Risk Assessment
arXiv:2601.16027v2 Announce Type: replace Abstract: The rise of live streaming has transformed online interaction, enabling massive real-time engagement but also exposing platforms to complex risks such as scams and coordinated malicious behaviors. Detecting these risks is challenging because harmful actions often accumulate gradually and recur across seemingly unrelated streams. To address this, we propose CS-VAR (Cross-Session Evidence-Aware Retrieval-Augmented Detector) for live streaming risk assessment. In CS-VAR, a lightweight, domain-specific model performs fast session-level risk infer
The proliferation of live streaming platforms has created new vectors for complex risks like scams and coordinated malicious behaviors, necessitating advanced AI-driven detection methods.
Sophisticated AI models capable of identifying cross-session patterns are crucial for maintaining platform integrity and user trust in the rapidly evolving landscape of real-time online interactions.
The development of retrieval-augmented LLMs that can leverage long-term evidence across sessions marks a significant advancement in detecting nuanced and evolving online risks.
- · Live streaming platforms
- · AI developers specializing in risk assessment
- · Users
- · Trust & Safety teams
- · Scammers
- · Malicious actors
- · Online fraud operations
Enhanced security and reduced harmful activities on live streaming platforms become possible due to more effective risk detection.
The improved security framework could lead to increased user engagement and monetization opportunities for platforms as trust grows.
This technology might be adapted for other online content moderation challenges, setting a new standard for AI-assisted platform governance.
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Read at arXiv cs.AI