ARGOS: Who, Where, and When in Agentic Multi-Camera Person Search
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866908963100950528 |
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| author | Kim, Myungchul Park, Kwanyong Kim, Junmo Kweon, In So |
| author_facet | Kim, Myungchul Park, Kwanyong Kim, Junmo Kweon, In So |
| contents | We introduce ARGOS, the first benchmark and framework that reformulates multi-camera person search as an interactive reasoning problem requiring an agent to plan, question, and eliminate candidates 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 responses, all within a limited turn budget. Reasoning is grounded in 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 (Who), spatial reasoning (Where), and temporal reasoning (When). Experiments with four LLM backbones show the benchmark is far from solved (best TWS: 0.383 on Track 2, 0.590 on Track 3), and ablations confirm that removing domain-specific tools drops accuracy by up to 49.6 percentage points. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_12762 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ARGOS: Who, Where, and When in Agentic Multi-Camera Person Search Kim, Myungchul Park, Kwanyong Kim, Junmo Kweon, In So Computer Vision and Pattern Recognition Artificial Intelligence Multiagent Systems We introduce ARGOS, the first benchmark and framework that reformulates multi-camera person search as an interactive reasoning problem requiring an agent to plan, question, and eliminate candidates 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 responses, all within a limited turn budget. Reasoning is grounded in 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 (Who), spatial reasoning (Where), and temporal reasoning (When). Experiments with four LLM backbones show the benchmark is far from solved (best TWS: 0.383 on Track 2, 0.590 on Track 3), and ablations confirm that removing domain-specific tools drops accuracy by up to 49.6 percentage points. |
| title | ARGOS: Who, Where, and When in Agentic Multi-Camera Person Search |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2604.12762 |