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New ARGOS benchmark tackles vague witness accounts for multi-camera person search

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced ARGOS, a new benchmark and agent framework designed for multi-camera person search. Unlike previous methods that relied on complete visual queries, ARGOS handles vague and partial information from witness accounts. The framework requires agents to interactively reason, plan questions, and utilize spatial and temporal tools within a limited turn budget, grounded by a Spatio-Temporal Topology Graph. AI

IMPACT This benchmark could advance agentic reasoning capabilities for complex, real-world information retrieval tasks.

RANK_REASON The cluster contains a research paper detailing a new benchmark and framework for a specific AI task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New ARGOS benchmark tackles vague witness accounts for multi-camera person search

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The cluster contains a research paper detailing a new benchmark and framework for a specific AI task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Myungchul Kim, Kwanyong Park, Junmo Kim, In So Kweon ·

    ARGOS: Who, Where, and When in Agentic Multi-Camera Person Search

    arXiv:2604.12762v2 Announce Type: replace-cross Abstract: Existing person search methods assume access to complete visual queries or exhaustive tracking, yet real-world witness accounts are vague, partial, and spread across cameras and time. We introduce ARGOS (Agentic Retrieval …

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