PND reduces object hallucination in VLMs via a dual-path contrast during decoding that amplifies visual features and penalizes linguistic priors, achieving reported SOTA results on POPE, MME, and CHAIR without retraining.
Training language models to follow instructions with human feedback.Ad- vances in neural information processing systems, 35:27730– 27744
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Parameter-efficient fine-tuning lets MLLMs serve as effective retrievers for natural-language-guided cross-view geo-localization, beating dual-encoder baselines on GeoText-1652 and CVG-Text while using far fewer trainable parameters.
3DrawAgent lets LLMs create complex 3D sketches from text prompts by using pairwise comparisons of their own outputs to self-improve spatial drawing skills without parameter updates.
GeoWorld applies hyperbolic geometry to JEPA world models and introduces geometric reinforcement learning, reporting modest success-rate gains of ~3% and ~2% on 3- and 4-step planning tasks versus V-JEPA 2.
MOMO merges sensor-specific models from three Mars orbital instruments at matched validation loss stages to form a foundation model that outperforms ImageNet, Earth observation, sensor-specific, and supervised baselines on nine Mars-Bench tasks.
citing papers explorer
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Turning Generators into Retrievers: Unlocking MLLMs for Natural Language-Guided Geo-Localization
Parameter-efficient fine-tuning lets MLLMs serve as effective retrievers for natural-language-guided cross-view geo-localization, beating dual-encoder baselines on GeoText-1652 and CVG-Text while using far fewer trainable parameters.
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3DrawAgent: Teaching LLM to Draw in 3D with Early Contrastive Experience
3DrawAgent lets LLMs create complex 3D sketches from text prompts by using pairwise comparisons of their own outputs to self-improve spatial drawing skills without parameter updates.
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GeoWorld: Geometric World Models
GeoWorld applies hyperbolic geometry to JEPA world models and introduces geometric reinforcement learning, reporting modest success-rate gains of ~3% and ~2% on 3- and 4-step planning tasks versus V-JEPA 2.
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MOMO: Mars Orbital Model Foundation Model for Mars Orbital Applications
MOMO merges sensor-specific models from three Mars orbital instruments at matched validation loss stages to form a foundation model that outperforms ImageNet, Earth observation, sensor-specific, and supervised baselines on nine Mars-Bench tasks.