VGenST-Bench is a new video benchmark for MLLM spatio-temporal reasoning built via generative synthesis, a multi-agent pipeline with human oversight, a 3x2x2 taxonomy, and hierarchical tasks separating perception from reasoning.
hub
Lego-puzzles: How good are mllms at multi-step spatial reasoning?
10 Pith papers cite this work. Polarity classification is still indexing.
10
Pith papers citing it
hub tools
citation-role summary
background 1
citation-polarity summary
years
2026 10roles
background 1polarities
background 1representative citing papers
Vesta is a unified embodied generalist model that outperforms specialist baselines by over 20% on average and improves real-world robotic task success by over 35%.
Introduces ChronoVision benchmark with three datasets showing VLMs rely on superficial cues such as color filters rather than genuine chronological reasoning.
Brick-Composer trains MLLMs on brick assembly via three signals, raising step-level success from under 1% to around 15% on the new BC-Bench benchmark.
Flat-Pack Bench is a new evaluation suite that shows state-of-the-art LVLMs perform poorly on nuanced spatio-temporal reasoning required for furniture assembly videos.
MLLMs show a large gap in spatial mathematical reasoning compared to humans, and a new 10,000-problem dataset helps narrow it through training.
PVPO is a sample-efficient RL method that improves semantic, geometric, and physical quality in LLM LEGO assembly generation by mitigating the PhysHack failure mode where validity alone fails to ensure fidelity.
Latent visual reasoning fails in current models because standard datasets make oracle latents uninformative and inference-time latents collapse away from useful representations.
Generation-to-Understanding synergy lets multimodal models create self-generated visual edits as intermediate steps, improving performance on twelve benchmarks while revealing limits in task-aligned self-reflection.
citing papers explorer
-
VGenST-Bench: A Benchmark for Spatio-Temporal Reasoning via Active Video Synthesis
VGenST-Bench is a new video benchmark for MLLM spatio-temporal reasoning built via generative synthesis, a multi-agent pipeline with human oversight, a 3x2x2 taxonomy, and hierarchical tasks separating perception from reasoning.
-
Vesta: A Generalist Embodied Reasoning Model
Vesta is a unified embodied generalist model that outperforms specialist baselines by over 20% on average and improves real-world robotic task success by over 35%.
-
Seeing Time: Benchmarking Chronological Reasoning and Shortcut Biases in Vision-Language Models
Introduces ChronoVision benchmark with three datasets showing VLMs rely on superficial cues such as color filters rather than genuine chronological reasoning.
-
Brick-Composer: Using MLLMs for Assembly with Diverse Bricks
Brick-Composer trains MLLMs on brick assembly via three signals, raising step-level success from under 1% to around 15% on the new BC-Bench benchmark.
-
Flat-Pack Bench: Evaluating Spatio-Temporal Understanding in Large Vision-Language Models through Furniture Assembly
Flat-Pack Bench is a new evaluation suite that shows state-of-the-art LVLMs perform poorly on nuanced spatio-temporal reasoning required for furniture assembly videos.
-
Do MLLMs Really Understand Space? A Mathematical Reasoning Evaluation
MLLMs show a large gap in spatial mathematical reasoning compared to humans, and a new 10,000-problem dataset helps narrow it through training.
-
Sample-Efficient Post-Training for LEGO Spatial-Physics Reasoning
PVPO is a sample-efficient RL method that improves semantic, geometric, and physical quality in LLM LEGO assembly generation by mitigating the PhysHack failure mode where validity alone fails to ensure fidelity.
-
What's Holding Back Latent Visual Reasoning?
Latent visual reasoning fails in current models because standard datasets make oracle latents uninformative and inference-time latents collapse away from useful representations.
-
Reversing the Flow: Generation-to-Understanding Synergy in Large Multimodal Models
Generation-to-Understanding synergy lets multimodal models create self-generated visual edits as intermediate steps, improving performance on twelve benchmarks while revealing limits in task-aligned self-reflection.
- The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes