ChronoPhyBench is a new benchmark and dataset for chronological physical dynamics reasoning that combines video-conditioned next-state prediction with VQA to reduce language bias in MLLM evaluation.
Large vision-language model alignment and misalignment: A survey through the lens of explainability
7 Pith papers cite this work. Polarity classification is still indexing.
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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.
OPPO is an evidence-aware preference optimization objective that contrasts faithful responses under varying visual evidence strengths to reduce hallucinations in MLLMs.
LMM-Searcher uses file-based visual UIDs and a fetch tool plus 12K synthesized trajectories to fine-tune a multimodal agent that scales to 100-turn horizons and reaches SOTA among open-source models on MM-BrowseComp and MMSearch-Plus.
State-of-the-art MLLMs show substantial inconsistency when reasoning over the same information presented in image, text, or mixed modalities, even after accounting for OCR errors, with inconsistency linked to visual factors and modality gap.
This survey organizes large VLM-based VLA models for robotic manipulation into monolithic and hierarchical paradigms, reviews their integrations and datasets, and outlines future directions.
A survey reviewing the integration of generative models with connected and automated vehicles to enhance predictive modeling, simulation accuracy, and decision-making.
citing papers explorer
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ChronoPhyBench: Do MLLMs Truly Understand the World or Merely Exploit Language Priors?
ChronoPhyBench is a new benchmark and dataset for chronological physical dynamics reasoning that combines video-conditioned next-state prediction with VQA to reduce language bias in MLLM evaluation.
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Breaking the Illusion: When Positive Meets Negative in Multimodal Decoding
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.
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Clearer Sight, Fewer Lies: Oriented Pickup Preference Optimization for Multimodal Hallucination Mitigation
OPPO is an evidence-aware preference optimization objective that contrasts faithful responses under varying visual evidence strengths to reduce hallucinations in MLLMs.
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Towards Long-horizon Agentic Multimodal Search
LMM-Searcher uses file-based visual UIDs and a fetch tool plus 12K synthesized trajectories to fine-tune a multimodal agent that scales to 100-turn horizons and reaches SOTA among open-source models on MM-BrowseComp and MMSearch-Plus.
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Same Content, Different Answers: Cross-Modal Inconsistency in MLLMs
State-of-the-art MLLMs show substantial inconsistency when reasoning over the same information presented in image, text, or mixed modalities, even after accounting for OCR errors, with inconsistency linked to visual factors and modality gap.
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Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey
This survey organizes large VLM-based VLA models for robotic manipulation into monolithic and hierarchical paradigms, reviews their integrations and datasets, and outlines future directions.
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Generative Models and Connected and Automated Vehicles: A Survey in Exploring the Intersection of Transportation and AI
A survey reviewing the integration of generative models with connected and automated vehicles to enhance predictive modeling, simulation accuracy, and decision-making.