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This paper introduces ARGOS, a novel benchmark and agent framework that transforms multi-camera person search into an interactive reasoning task driven by partial witness accounts rather than complete visual queries. By coupling dialogue with a Spatio-Temporal Topology Graph, the system forces agents to plan strategic questions under information asymmetry, marking a significant shift from static retrieval toward dynamic agentic workflows in computer vision.
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 with Grounded Observational Search), a benchmark and agent framework that recasts multi-camera person search from one-shot retrieval on a complete query into interactive reasoning from partial clues. To our knowledge, ARGOS is the first interactive benchmark to couple witness dialogue with camera-network topology, requiring an agent to plan, question, and eliminate under information asymmetry. An ARGOS agent receives a vague witness statement and must decide what to ask, when to invoke spatial or temporal tools, and how to interpret ambiguous natural-language responses, all within a limited turn budget. To ground reasoning in physical constraints, the agent accesses a Spatio-Temporal Topology Graph (STTG) encoding camera connectivity and empirically validated transition times. The benchmark comprises 2{,}691 tasks across 14 real-world scenarios in three progressive tracks: semantic perception (\emph{Who}, 989 tasks), spatial reasoning (\emph{Where}, 550 tasks), and temporal reasoning (\emph{When}, 1{,}152 tasks). We propose Turn-Weighted Success (TWS) as the primary metric, jointly measuring correctness and turn efficiency. Experiments with four LLM backbones show the benchmark is far from solved: the best agent achieves TWS of 0.383 (Track~2) and 0.590 (Track~3). Ablations confirm each component is essential: removing domain-specific tools drops Top-1 accuracy by up to 49.6 percentage points, and removing strategic reasoning halves TWS while barely affecting Top-1.
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 with Grounded Observational Search), a benchmark and agent framework that recasts multi-camera person search from one-shot retrieval on a complete query into interactive reasoning from partial clues. To our knowledge, ARGOS is the first interactive benchmark to couple witness dialogue with camera-network topology, requiring an agent to plan, question, and eliminate under information asymmetry. An ARGOS agent receives a vague witness statement and must decide what to ask, when to invoke spatial or temporal tools, and how to interpret ambiguous natural-language responses, all within a limited turn budget. To ground reasoning in physical constraints, the agent accesses a Spatio-Temporal Topology Graph (STTG) encoding camera connectivity and empirically validated transition times. The benchmark comprises 2{,}691 tasks across 14 real-world scenarios in three progressive tracks: semantic perception (\emph{Who}, 989 tasks), spatial reasoning (\emph{Where}, 550 tasks), and temporal reasoning (\emph{When}, 1{,}152 tasks). We propose Turn-Weighted Success (TWS) as the primary metric, jointly measuring correctness and turn efficiency. Experiments with four LLM backbones show the benchmark is far from solved: the best agent achieves TWS of 0.383 (Track~2) and 0.590 (Track~3). Ablations confirm each component is essential: removing domain-specific tools drops Top-1 accuracy by up to 49.6 percentage points, and removing strategic reasoning halves TWS while barely affecting Top-1.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
arXiv:2604.12762 [cs.CV]
(or arXiv:2604.12762v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2604.12762 Focus to learn more arXiv-issued DOI via DataCite
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