REVIEW 7 cited by
STI-Bench: Are MLLMs Ready for Precise Spatial-Temporal World Understanding?
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The use of Multimodal Large Language Models (MLLMs) as an end-to-end solution for Embodied AI and Autonomous Driving has become a prevailing trend. While MLLMs have been extensively studied for visual semantic understanding tasks, their ability to perform precise and quantitative spatial-temporal understanding in real-world applications remains largely unexamined, leading to uncertain prospects. To evaluate models' Spatial-Temporal Intelligence, we introduce STI-Bench, a benchmark designed to evaluate MLLMs' spatial-temporal understanding through challenging tasks such as estimating and predicting the appearance, pose, displacement, and motion of objects. Our benchmark encompasses a wide range of robot and vehicle operations across desktop, indoor, and outdoor scenarios. The extensive experiments reveals that the state-of-the-art MLLMs still struggle in real-world spatial-temporal understanding, especially in tasks requiring precise distance estimation and motion analysis.
Forward citations
Cited by 7 Pith papers
-
Q-GeoMem: Question-Guided Geometric Memory for Video Spatial Reasoning
Question-guided dual geometric memories with relevance-novelty utility reportedly reach state-of-the-art video spatial reasoning on two in-domain and five out-of-distribution benchmarks.
-
$M^3-Verse$: A "Spot the Difference" Challenge for Large Multimodal Models
A new benchmark tests whether large multimodal models can compare paired 'before and after' videos to detect scene changes, and finds current models perform near random.
-
SpaceDrive: Infusing Spatial Awareness into VLM-based Autonomous Driving
SpaceDrive replaces textual coordinate tokens with shared 3D positional encodings in a VLM driving planner, achieving state-of-the-art open-loop planning on nuScenes and 78.02 Driving Score on Bench2Drive.
-
EOC-Bench: Can MLLMs Identify, Recall, and Forecast Objects in an Egocentric World?
EOC-Bench evaluates MLLMs on egocentric object cognition across past, present, and future temporal dimensions, finding large gaps versus humans, especially in absolute time perception.
-
VerIPO: Cultivating Long Reasoning in Video-LLMs via Verifier-Gudied Iterative Policy Optimization
VerIPO interleaves GRPO, a verifier that curates preference pairs from rollouts, and DPO to steadily improve accuracy and chain-of-thought consistency in video LLMs.
-
HCRMP: A LLM-Hinted Contextual Reinforcement Learning Framework for Autonomous Driving
The HCRMP planner feeds LLM semantic hints into state representation and critic weighting instead of letting the LLM decide actions, reporting better CARLA driving metrics.
-
The high-speed X-ray camera on AXIS: design and performance updates
An X-ray camera design-update whose supporting full text is a different paper (RynnEC, an embodied AI model), leaving all camera performance claims unverified.
Discussion (0). Continue with ORCID to comment.