3D-Aware Vision-Language Models Fine-Tuning with Geometric Distillation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Vision-Language Models (VLMs) have shown remarkable performance on diverse visual and linguistic tasks, yet they remain limited in their understanding of 3D spatial structures. |
| Approach: | They propose a framework that injects human-inspired geometric cues into pretrained VLMs . they use sparse correspondences, relative depth relations and dense cost volumes . |
| Outcome: | The proposed framework outperforms existing methods on vision-language reasoning and 3D perception benchmarks. |
Similar Papers
PseudoGD: Enhancing Spatial Reasoning in Vision-Language Models through Pseudo Geometric Knowledge Distillation (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent Large Vision-Language Models (LVLMs) have shown remarkable success in general semantic understanding, but struggle with 3D spatial reasoning tasks. |
| Approach: | They propose a framework to help vision encoders internalize 3D geometric information using only standard 2D images. |
| Outcome: | The proposed framework achieves State-of-the-Art (SOTA) performance across various model architectures. |
SceneLM: 3D-Aware Language Models for Editable 3D Scene Synthesis (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for synthesising 3D scenes from a single image are text-driven and lack precise metric understanding from images. |
| Approach: | They propose a language-model-based framework that grounds 3D scene synthesis in visual evidence by recovering an executable metric 3D layout directly from a single image. |
| Outcome: | The proposed framework recovers an executable metric 3D layout directly from an RGB image and instantiates, places, and edits objects for iterative refinement. |
GeoCoder: Solving Geometry Problems by Generating Modular Code through Vision-Language Models (2025.findings-naacl)
Copied to clipboard
| Challenge: | Various vision-language models (VLMs) have made significant progress in multimodal tasks, but they still struggle with geometry problems. |
| Approach: | They propose a vision-language model that leverages modular code-finetuning to generate and execute code using a predefined geometry function library. |
| Outcome: | The proposed model improves geometric reasoning abilities by 16% on a GeomVerse dataset compared to other methods. |
EfficientVLM: Fast and Accurate Vision-Language Models via Knowledge Distillation and Modal-adaptive Pruning (2023.findings-acl)
Copied to clipboard
| Challenge: | Pre-trained vision-language models have achieved impressive results in a range of vision-linguistic tasks. |
| Approach: | They propose a distilling then pruning framework to compress large vision-language models into smaller, faster ones. |
| Outcome: | The proposed framework reduces the size of a pre-trained large vision-language model and improves its performance on vision-linguistic tasks. |
SpaRE: Enhancing Spatial Reasoning in Vision-Language Models with Synthetic Data (2025.acl-long)
Copied to clipboard
| Challenge: | Vision-language models struggle with spatial reasoning, a skill that humans excel at. |
| Approach: | They propose to use a spatial-reasoning Enhanced (SpaRE) VLM to improve spatial reasoning in visual question answering and robotics. |
| Outcome: | The proposed model achieves a 49% performance gain on the What's Up benchmark while maintaining strong results on general tasks. |
Making VLMs More Robot-Friendly: Self-Critical Distillation of Low-Level Procedural Reasoning (2025.emnlp-main)
Copied to clipboard
Chan Young Park, Jillian Fisher, Marius Memmel, Dipika Khullar, Seoho Yun, Abhishek Gupta, Yejin Choi
| Challenge: | Large language models (LLMs) are human-centric, but omit low-level, spatially grounded details needed for robotic execution. |
| Approach: | They propose a lightweight framework for vision-language procedural planning that enables iteratively critique, revise and verify their own plans without external supervision or teacher models. |
| Outcome: | a new framework outperforms weaker models 100X the size in vision-language procedural planning . the framework generates higher-quality, execution-ready plans that can be used at inference and fine-tuning . |
Infinite Babble: Inflating 3D Vision-Language Model Inference Overhead via Adversarial Geometric Perturbation (2026.findings-acl)
Copied to clipboard
| Challenge: | 3D Vision-Language Models (VLMs) are critical cognitive backbone for spatial intelligence, but their reliance on autoregressive decoding introduces a fundamental vulnerability regarding inference efficiency. |
| Approach: | They propose a framework that triggers computational and economic exhaustion in 3D-VLMs by injecting imperceptible noise that forces the model into a state of pathological verbosity. |
| Outcome: | The proposed framework amplifies output length and energy consumption by up to 6.45, demonstrating a potent capability to drain system resources. |
Language-to-Space Programming for Training-Free 3D Visual Grounding (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for 3D visual grounding have been proposed, but they are limited by the scarcity of 3D vision-language datasets and the high cost of annotations. |
| Approach: | They propose a method for training-free 3D visual grounding that uses LLM-generated codes to analyze 3D spatial relations among objects. |
| Outcome: | The proposed method achieves 52.9% accuracy on the Nr3D benchmark and significantly reduces grounding time and token costs. |
GeoPQA: Bridging the Visual Perception Gap in MLLMs for Geometric Reasoning (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in reinforcement learning (RL) have enhanced the reasoning abilities of large language models, but the impact on multimodal LLMs is limited. |
| Approach: | They propose a two-stage RL framework that enhances visual perception and fosters reasoning capabilities. |
| Outcome: | The proposed framework improves geometric reasoning by 9.7% and problem-solving by 9.1% compared to direct reasoning training approach. |
GeoLAN: Geometric Learning of Latent Explanatory Directions in Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models lack transparency and are often unable to explain causal relationships . |
| Approach: | They propose a training framework that treats token representations as geometric trajectories and applies stickiness conditions to the Kakeya Conjecture. |
| Outcome: | The proposed training framework maintains task accuracy while improving geometric metrics and reducing fairness biases. |