mlsanityData to 14 Oct 2026

ByDeWay: Boost Your multimodal LLM with DEpth prompting in a Training-Free Way

arXiv 2507.08679 ↗First posted 11 Jul 2025cs.CVcited by 3

On the map: Multimodal LLMs / vision-language models → MLLM training and visual instruction tuning

Abstract (arXiv)

We introduce ByDeWay, a training-free framework designed to enhance the performance of Multimodal Large Language Models (MLLMs). ByDeWay uses a novel prompting strategy called Layered-Depth-Based Prompting (LDP), which improves spatial reasoning and grounding without modifying any model parameters. It segments the scene into closest, mid-range, and farthest layers using monocular depth estimation, then generates region-specific captions with a grounded vision-language model. These structured, depth-aware captions are appended to the image-question prompt, enriching it with spatial context. This guides MLLMs to produce more grounded and less hallucinated responses. Our method is lightweight, modular, and compatible with black-box MLLMs. Experiments on hallucination-sensitive (POPE) and reasoning-intensive (GQA) benchmarks show consistent improvements across multiple MLLMs, validating the effectiveness of depth-aware prompting in a zero-training setting.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 20 of its 26 references that are in the map. First posted on arXiv 11 Jul 2025.

Builds on

20 of 26 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024M2PT: Multimodal Prompt Tuning for Zero-shot Instruction Learning24 Sep
  2. 2024List Items One by One: A New Data Source and Learning Paradigm for Multimodal LLMs25 Apr
  3. 2023Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V17 Oct

Other references · 17

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,137
  3. 2023Evaluating Object Hallucination in Large Vision-Language Modelscited by 1,729
  4. 2017Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answeringcited by 1,312
  5. 2023Kosmos-2: Grounding Multimodal Large Language Models to the Worldcited by 1,089
  6. 2023VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Taskscited by 501
  7. 2023GLaMM: Pixel Grounding Large Multimodal Modelcited by 455
  8. 2024Hallucination of Multimodal Large Language Models: A Surveycited by 388
  9. 2020Scan2Cap: Context-aware Dense Captioning in RGB-D Scanscited by 218
  10. 2024Visual In-Context Learning for Large Vision-Language Modelscited by 128
  11. 2022X-Trans2Cap: Cross-Modal Knowledge Transfer using Transformer for 3D Dense Captioningcited by 70
  12. 2022Spatiality-guided Transformer for 3D Dense Captioning on Point Cloudscited by 40
5 more
  1. 2019Multi-Modal Attention-based Fusion Model for Semantic Segmentation of RGB-Depth Imagescited by 13
  2. 2025BannerAgency: Advertising Banner Design with Multimodal LLM Agentscited by 11
  3. 2024Hybridnet for depth estimation and semantic segmentationcited by 4
  4. 2023DEVICE: Depth and Visual Concepts Aware Transformer for OCR-based Image Captioningcited by 3
  5. 2023Enhancing image captioning with depth information using a Transformer-based frameworkcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Think3D: Thinking with Space for Spatial Reasoningcited by 24
  2. 2026When Depth Is Better Told Than Shown: Depth-Ordinal Prompting for Vision-Language Spatial Reasoningcited by 0
  3. 2026Explainable and Resource-Efficient Spatial Reasoning in Multimodal LLMs for Decision-Critical Applicationscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025QG-CoC: Question-Guided Chain-of-Captions for Large Multimodal Models
  2. 2024Visual Prompting in Multimodal Large Language Models: A Survey
  3. 2025VP-Bench: A Comprehensive Benchmark for Visual Prompting in Multimodal Large Language Models
  4. 2023LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models
  5. 2024TWIST & SCOUT: Grounding Multimodal LLM-Experts by Forget-Free Tuning
  6. 2026DeepSight: Bridging Depth Maps and Language with a Depth-Driven Multimodal Model
  7. 2025How Multimodal LLMs Solve Image Tasks: A Lens on Visual Grounding, Task Reasoning, and Answer Decoding
  8. 2025See&Trek: Training-Free Spatial Prompting for Multimodal Large Language Model