Papers with VSI-Bench
Unleashing Spatial Reasoning in Multimodal Large Language Models via Textual Representation Guided Reasoning (2026.acl-long)
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| Challenge: | Existing Multimodal Large Language Models struggle with 3D spatial reasoning as they fail to construct structured abstractions of the 3D environment depicted in video inputs. |
| Approach: | They propose a prompting method that induces MLLMs to generate 3D representations as reasoning traces for more accurate spatial question answering. |
| Outcome: | Extensive experiments on VSI-Bench and OST-Bech show that TRACE improves over prior prompting strategies across a diverse range of MLLM backbones. |
VideoCuRL: Video Curriculum Reinforcement Learning with Orthogonal Difficulty Decomposition (2026.acl-long)
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| Challenge: | Reinforcement Learning (RL) is crucial for Video-LLMs with complex spatiotemporal reasoning. |
| Approach: | They propose a framework that decomposes difficulty into two axes in video understanding . they employ efficient, training-free proxies to map data onto a 2D curriculum grid . |
| Outcome: | The proposed framework surpasses strong RL baselines on reasoning and perception tasks. |