arXiv Computer Vision
Plan-Bench: Can Existing VLMs Go Well Beyond Perception to Reason and Plan in Embodied Worlds?
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ParallelWorld: Test-Time Scaling for Agentic Embodied Reasoning
arXiv:2608.22971v2 Announce Type: replace Abstract: Embodied AI has emerged as a key paradigm for autonomous systems operating in complex physical environments. While end-to-end models have establish...
arXiv AI
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ParallelWorld: Test-Time Scaling for Embodied Reasoning
arXiv:2608.22971v1 Announce Type: new Abstract: Embodied Reasoning constitutes a fundamental capability of embodied intelligence, serving as the basis for autonomous perception, reasoning, and intera...
arXiv AI
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Planning Takes More Than Token Prediction: Causal Plan for Benchmarking and Building Physically Grounded Embodied Reasoners
The paper argues that current embodied vision‑language planning benchmarks favor linguistic next‑token prediction over physically grounded next‑state reasoning, leading models to rely on language priors rather than true causal dependencies. To address this, the authors introduce Causal‑Plan‑Bench, a diagnostic suite covering four causal dimensions, and Causal‑Plan‑1M, a million‑scale corpus of explicit causal reasoning traces extracted from egocentric videos. Extensive experiments show that existing models perform poorly on these tasks, while a new model trained with a tailored recipe—Causal Planner based on Qwen3‑VL‑8B—achieves significant gains, demonstrating the feasibility of physically grounded causal reasoning.
arXiv AI
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ViPlan: A Benchmark for Visual Planning with Symbolic Predicates and Vision-Language Models
arXiv:2505.13180v3 Announce Type: replace Abstract: Integrating Large Language Models with symbolic planners is a promising direction for obtaining verifiable and grounded plans, with recent works ex...
Hugging Face Trending Papers
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Hy-Embodied-VLM-1.0: Efficient Physical-World Agents
Building capable embodied agents requires not only multimodal perception and understanding, but also agentic capabilities for reasoning about actions, adapting to evolving situations, and interacting with the physical world. In this report, we introduce Hy-Embodied-VLM-1.
arXiv AI
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SpatialWorld: Benchmarking Interactive Spatial Reasoning of Multimodal Agents in Real-World Tasks
arXiv:2606. 09669v1 Announce Type: new Abstract: Spatial reasoning is a foundational capability for multimodal large language models (MLLMs) to perceive and operate within the physical world.