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cs.CV2026

SAB-LVLM: Significance-Aware Binarization for Large Vision-Language Models

Qi Lyu, Jiahua Dong, Baichen Liu +7

Large Vision-Language Models (LVLMs) have achieved remarkable progress in multimodal understanding, yet their enormous parameter scale and cross-modal computation incur substantial…

cs.CV2026

LatentPilot: Scene-Aware Vision-and-Language Navigation by Dreaming Ahead with Latent Visual Reasoning

Haihong Hao, Lei Chen, Mingfei Han +5

Existing vision-and-language navigation (VLN) models primarily reason over past and current visual observations, while largely ignoring the future visual dynamics induced by action…

cs.CV2026

GeoSense: Internalizing Geometric Necessity Perception for Multimodal Reasoning

Ruiheng Liu, Haihong Hao, Mingfei Han +4

Advancing towards artificial superintelligence requires rich and intelligent perceptual capabilities. A critical frontier in this pursuit is overcoming the limited spatial understa…

cs.CV2026

Implicit Geometry Representations for Vision-and-Language Navigation from Web Videos

Mingfei Han, Haihong Hao, Liang Ma +6

Vision-and-Language Navigation (VLN) has long been constrained by the limited diversity and scalability of simulator-curated datasets, which fail to capture the complexity of real-…

cs.CV2025

EvolveNav: Empowering LLM-Based Vision-Language Navigation via Self-Improving Embodied Reasoning

Bingqian Lin, Yunshuang Nie, Khun Loun Zai +10

Recent studies have revealed the potential of training open-source Large Language Models (LLMs) to unleash LLMs' reasoning ability for enhancing vision-language navigation (VLN) pe…

cs.CV2024

EACO: Enhancing Alignment in Multimodal LLMs via Critical Observation

Yongxin Wang, Meng Cao, Haokun Lin +5

Multimodal large language models (MLLMs) have achieved remarkable progress on various visual question answering and reasoning tasks leveraging instruction fine-tuning specific data…