Tracking the Truth: Object-Centric Spatio-Temporal Monitoring for Video Large Language Models
While multimodal large language models (MLLMs) have advanced video understanding, they remain highly prone to hallucinations in dynamic scenes. We argue this stems from a failure in spatio-temporal monitoring, the ability to persistently track object identities, states, and relations over time. Existing benchmarks obscure this deficit by relying on single final-answer evaluations for queries that can often be resolved via local visual cues or statistical priors. To rigorously diagnose this, we introduce STEMO-Bench (Spatio-TEmporal MOnitoring), a benchmark of human-verified object-centric facts that evaluates intermediate reasoning by decomposing queries into sub-questions, distinguishing...
| Authors | Tri Cao, Khoi Le, Thong Nguyen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 9 May 2026 |
| Last updated at source | 15 August 2026 |
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