PROBEACT is a plug-and-play intervention framework that combines hidden-state probing, kinematic failure detection, and CBF-based correction to boost success rates of pre-trained VLA models on the LIBERO-plus benchmark from 69.6% to 74.1%.
Cyclevla: Proactive self-correcting vision-language-action models via subtask backtracking and minimum bayes risk decoding.arXiv preprint arXiv:2601.02295, 2026
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4representative citing papers
Topology-aware attention over hierarchical scene graphs lets a 3D-LLM ground, caption, and answer questions across multi-room homes, with large gains on a new HM3D benchmark.
Progress-enhanced VLA model raises simulated bimanual furniture assembly success from 48% to 80% across three furniture types and shows 16% drop on real Kinova robot.
VLA-FAIL introduces last-layer Mahalanobis distance and action chunk consistency detectors that together enable early, reliable failure detection in finetuned VLAs without failure data or expensive sampling.
citing papers explorer
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ProbeAct: Probe-Guided Training-Free Failure Recovery in Vision-Language-Action Models
PROBEACT is a plug-and-play intervention framework that combines hidden-state probing, kinematic failure detection, and CBF-based correction to boost success rates of pre-trained VLA models on the LIBERO-plus benchmark from 69.6% to 74.1%.
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CAIRN: Cross-Room 3D Scene Understanding with Topology-Aware Large Multimodal Models
Topology-aware attention over hierarchical scene graphs lets a 3D-LLM ground, caption, and answer questions across multi-room homes, with large gains on a new HM3D benchmark.
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FurnitureVLA: Learning Long-Horizon Bimanual Furniture Assembly with Vision-Language-Action Model
Progress-enhanced VLA model raises simulated bimanual furniture assembly success from 48% to 80% across three furniture types and shows 16% drop on real Kinova robot.
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VLA-FAIL: Efficient Task Failure Detection for Finetuned Vision-Language-Action Models
VLA-FAIL introduces last-layer Mahalanobis distance and action chunk consistency detectors that together enable early, reliable failure detection in finetuned VLAs without failure data or expensive sampling.