aiqbench analysis
This article addresses a critical flaw in video Large Language Models (MLLMs), specifically their tendency to hallucinate during dynamic scenes due to inadequate spatio-temporal monitoring of object trajectories. Although the authors have withdrawn the manuscript following identified errors in experimental evaluation, the core argument regarding the necessity of decomposing queries and verifying intermediate reasoning remains highly relevant for improving model reliability. The proposed framework's focus on structured object tracking highlights a key direction for future research aimed at reducing coincidental correctness in complex visual tasks.
Tracking the Truth: Object-Centric Spatio-Temporal Monitoring for Video Large Language Models
arXiv:2605.08974v2 Announce Type: replace-cross Abstract: 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 genuine temporal understanding from coincidental correctness. To address failure modes exposed by STEMO, we propose STEMO-Track, a novel object-centric framework that explicitly constructs and reasons over structured object trajectories via chunk-wise state extraction and temporal aggregation. Extensive experiments demonstrate that our object-centric framework significantly reduces hallucinated answers and improves spatio-temporal reasoning consistency over state-of-the-art MLLMs.
Abstract: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 genuine temporal understanding from coincidental correctness. To address failure modes exposed by STEMO, we propose STEMO-Track, a novel object-centric framework that explicitly constructs and reasons over structured object trajectories via chunk-wise state extraction and temporal aggregation. Extensive experiments demonstrate that our object-centric framework significantly reduces hallucinated answers and improves spatio-temporal reasoning consistency over state-of-the-art MLLMs.
The authors are withdrawing this manuscript due to errors identified in the experimental evaluation and result aggregation, which affect several reported quantitative results and some conclusions. These issues require substantial re-evaluation of the experiments and analysis
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2605.08974 [cs.CV]
(or arXiv:2605.08974v2 [cs.CV] for this version)
Full text under the publisher's open licence · 199 words
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