CVPR 2026 Papers

Existing Multimodal Large Language Models (MLLMs) suffer from significant performance degradation on the long document understanding task as document length increases. This stems from two fundamental challenges: 1) a low Signal-to-Noise Ratio (SNR), with crucial evidence buried in irrelevant pages; and 2) supervision scarcity, as datasets offering only final short answers provide a weak learning signal. In this paper, we address these challenges by proposing a paradigm that requires the model to execute a structured "Analysis, Localization and Reasoning" workflow. To instill this capability, we design a two-stage training framework: we first perform Supervised Fine-Tuning on high-quality data generated via an efficient knowledge distillation strategy. Subsequently, we employ an Evidence-aware Group Relative Policy Optimization which jointly optimizes for both evidence localization and answer accuracy. Additionally, we introduce a Evidence-Guided Resolution Allocation strategy to mitigate memory constraints of training on multi-pages documents. Extensive experiments demonstrate that DocSeeker achieves superior performance on both in-domain and out-of-domain tasks. We show it robustly generalizes from short-page training to ultra-long documents and is naturally synergistic with visual Retrieval-Augmented Generation systems, serving as an ideal foundation for their implementation.

Current visual grounding models are either based on a Multimodal Large Language Model (MLLM) that performs auto-regressive decoding, which is slow and risks hallucinations, or on re-aligning an LLM with vision features to learn new special or object tokens for grounding, which may undermine the LLM's pretrained reasoning ability. In contrast, we propose **VGent**, a modular encoder-decoder architecture that explicitly disentangles high-level reasoning and low-level bounding box prediction. Specifically, a frozen MLLM serves as the encoder to provide untouched powerful reasoning capabilities, while a decoder takes high-quality boxes proposed by detectors as queries and selects target box(es) via cross-attending on encoder's hidden states. This design fully leverages advances in both object detection and MLLM, avoids the pitfalls of auto-regressive decoding, and enables fast inference. Moreover, it supports modular upgrades of both the encoder and decoder to benefit the whole system: we introduce (i) **QuadThinker**, an RL-based training paradigm for enhancing multi-target reasoning ability of the encoder; (ii) **mask-aware label** for resolving detection-segmentation ambiguity; and (iii) **global target recognition** to improve the recognition of all the targets which benefits the selection among augmented proposals. Experiments on multi-target visual grounding benchmarks show that VGent achieves a new state-of-the-art with **+20.6%** F1 improvement over prior methods, and further boosts gIoU by **+8.2%** and cIoU by **+5.8%** under visual reference challenges, while maintaining constant, fast inference latency.

Despite recent progress in 3D visual grounding, existing methods still struggle with three core challenges: 1) cross-modal misalignment that prevents textual cues from being reliably delivered to visual representations, 2) intra-class confusion arising from insufficient understanding of fine-grained expression cues, and 3) geometric reasoning errors caused by inaccurate aggregation of spatially relevant visual features. We propose EG-3DVG, a unified framework that addresses these issues through an expression and geometry aware grounding decoder. The decoder integrates two complementary attention modules--position-guided expression cross-attention (PECA) for reliable text-vision alignment and geometry-aware masked attention (GMA) for selective aggregation of geometry-consistent visual cues. To further distinguish semantically similar instances, we introduce expression-aware contrastive learning (ECL), which strengthens the alignment between the target object token and expression-relevant words. Extensive experiments on ScanRefer and SR3D/NR3D demonstrate that EG-3DVG achieves state-of-the-art performance in both 3D bounding box localization and mask prediction, validating the effectiveness of our geometry- and expression-aware design.