ByDeWay: Depth-Prompting for MLLMs
- ByDeWay is a prompting-based, zero-training framework that adds explicit depth context to MLLMs to improve spatial grounding and reduce hallucination.
- It employs Layered-Depth-Based Prompting to convert RGB images into structured depth captions, partitioned into close, mid, and far layers.
- Evaluations on benchmarks like POPE and GQA demonstrate significant accuracy gains across various models without any modifications to internal weights.
Searching arXiv for the ByDeWay paper to ground the article in the latest record. ByDeWay is a prompting-based, zero-training framework for multimodal LLMs (MLLMs) that improves spatial reasoning and grounding by adding explicit spatial context to the input. Introduced as “Boost Your multimodal LLM with DEpth prompting in a Training-Free Way,” it targets two recurrent failure modes in MLLMs: hallucination, in which answers describe objects or properties not supported by the image, and weak spatial reasoning or grounding, in which the model struggles with location, depth, or relative arrangement. Its central mechanism, Layered-Depth-Based Prompting (LDP), converts an RGB image into a depth-aware textual description and appends that description to the original image-question prompt, yielding a lightweight, modular, and black-box-compatible method that does not require fine-tuning, additional supervision, architectural modifications, or access to model internals (Roy et al., 11 Jul 2025).
1. Conceptual basis and problem setting
ByDeWay is designed around the claim that many hallucination and reasoning failures arise because an MLLM receives only the 2D image and the question, but not a structured representation of what is near, mid-range, or far in the scene (Roy et al., 11 Jul 2025). The framework therefore injects depth-derived textual evidence into the prompt rather than modifying the target model itself. In the paper’s formulation, the downstream MLLM is supplied with a pseudo-3D scene summary that can anchor its response in spatially organized evidence.
The framework is explicitly described as training-free and modular. It is intended for black-box MLLMs, provided that they accept image-plus-text inputs. This design choice distinguishes ByDeWay from approaches that depend on fine-tuning or architectural intervention. The method is also presented as model-agnostic: the same prompting pipeline can be applied across multiple MLLMs with different capacities and provenance, including proprietary and open models (Roy et al., 11 Jul 2025).
The core prompting strategy is Layered-Depth-Based Prompting. LDP operates by estimating a monocular depth map, segmenting the scene into three depth layers, captioning each layer with a grounded vision-LLM, and concatenating those captions with the original task prompt. The resulting prompt is richer in spatial context and is intended to guide the target MLLM toward more grounded and less hallucinated responses (Roy et al., 11 Jul 2025).
2. Layered-Depth-Based Prompting
LDP is the central methodological contribution of ByDeWay. Its purpose is to translate raw depth information into natural-language spatial cues that are directly usable by an MLLM at inference time (Roy et al., 11 Jul 2025). The method begins with monocular depth estimation using Depth Anything V2, applied to a normal RGB image. The paper denotes the depth map by
where is the estimated relative depth at pixel . This step is described as zero-shot and supervision-free, and it yields an affine-invariant inverse depth representation suitable for coarse spatial ordering.
Once the depth map is available, the scene is partitioned into three coarse depth layers. The paper specifies thresholds and at the 30th and 70th percentiles. These thresholds produce masks for the three layers corresponding to the closest layer, the mid-range layer, and the farthest layer. The split is stated in two equivalent ways in the source material: as top 30%, middle 40%, and bottom 30% of depth values, and as percentile thresholds at the 30th and 70th percentiles. In effect, the image is partitioned into foreground-like, intermediate, and background-like regions (Roy et al., 11 Jul 2025).
Each masked region is then passed to KOSMOS-2, described in the paper as a grounded vision-LLM. KOSMOS-2 generates one caption per layer. The method therefore yields a region-specific textual decomposition of the image rather than a single global caption. The examples given in the paper use outputs such as “A baseball player” for the closest layer, “The players” for the mid-range layer, and “A crowd” for the farthest layer. This layered textualization is intended to make scene layout explicit (Roy et al., 11 Jul 2025).
The final prompt is formed by appending the three captions to the original image-question prompt. In the paper’s procedural summary, the augmented prompt is written as
The prompt includes the original question, a section labeled “Image Caption about depth,” the captions for Closest, Mid-range, and Farthest, and instructions telling the model not to guess and to answer based on the image and depth info (Roy et al., 11 Jul 2025).
3. Inference pipeline and prompt construction
ByDeWay is organized as a sequence of discrete stages rather than as a learned end-to-end optimization procedure. The paper presents the pipeline as follows: input image acquisition, monocular depth estimation, depth-based segmentation into layers, region-aware captioning, prompt construction, and downstream inference (Roy et al., 11 Jul 2025). This structure is central to its characterization as lightweight and modular.
The first stage takes a standard RGB image . Depth Anything V2 then computes the dense depth map . The next stage computes percentile thresholds and at 30% and 70%, which are used to generate three binary masks for the close, mid, and far regions. These masks isolate the visual content corresponding to different coarse depth bands. KOSMOS-2 then captions each masked image region, producing 0, 1, and 2. The target MLLM finally receives the original image together with the LDP-augmented prompt and produces the answer (Roy et al., 11 Jul 2025).
The paper emphasizes that no formal mathematical optimization objective is introduced for the method because the framework is training-free. Its “algorithm” is a prompt construction and inference pipeline. This is a significant design property: all gains are sought through better evidence presentation at inference time rather than through weight updates or task-specific adaptation (Roy et al., 11 Jul 2025).
The prompting change is minimal in form but consequential in intended effect. The baseline prompt is image plus question only. Under LDP, the prompt becomes image plus question plus depth-layer captions. The paper’s example contrasts a generic instruction—“Based on the image, answer the following question...” —with a depth-aware variant—“Based on the image and its depth information, answer the following question...” —followed by the layered captions. This structured addition is presented as the mechanism by which the model is given an explicit textual scaffold describing scene structure (Roy et al., 11 Jul 2025).
4. Mechanism for reducing hallucination and improving grounding
The paper argues that hallucinations often arise from ambiguous or incomplete scene interpretation, and that depth-based captions provide spatial anchors that reduce this ambiguity (Roy et al., 11 Jul 2025). In this account, the method improves performance not by teaching the MLLM new parameters, but by giving it better evidence at inference time.
Four effects are highlighted. First, the method supports disambiguation of object location: if a question refers to “the person in the front,” the depth captions identify which entities are closest. Second, it improves scene decomposition by encoding a rough 3D organization directly in the prompt rather than leaving the model to infer all structure from pixels alone. Third, it reduces guessing because the prompt contains instructions not to assume or guess, and because the appended region descriptions are grounded in visible content. Fourth, it improves reasoning over spatial relations such as “left of,” “behind,” “in front of,” or object attributes tied to specific scene regions (Roy et al., 11 Jul 2025).
These claims are consistent with the two benchmark choices in the paper. POPE is described as hallucination-sensitive and binary, whereas GQA is reasoning-intensive and emphasizes spatial reasoning, compositional understanding, object attributes, and logic over scene structure. ByDeWay’s design is therefore matched to benchmarks in which explicit depth-aware cues should be relevant (Roy et al., 11 Jul 2025).
A plausible implication is that ByDeWay externalizes part of the spatial grounding problem into text. The paper states this directly in interpretive terms: prompt-level spatial grounding is a strong lever for improving multimodal reasoning, because the model receives a better “map” of the scene, clearer information about which objects are near or far, and reduced ambiguity (Roy et al., 11 Jul 2025).
5. Experimental evaluation
The evaluation uses two benchmarks: POPE and GQA. POPE is described as a hallucination-sensitive benchmark for binary visual question answering with yes/no questions, and its reported metrics are Accuracy, Precision, Recall, and F1. GQA is described as a visual reasoning benchmark stressing spatial reasoning, compositional understanding, object attributes, and logic over scene structure. The authors evaluate on a curated subset of 150 samples for POPE and on a 150-sample subset for GQA (Roy et al., 11 Jul 2025).
The models tested are gpt-4o, Qwen2.5-VL, ViLT, and BLIP. The comparison is between a baseline prompt consisting of image plus question only and the LDP prompt consisting of image plus question plus depth-layer captions. The output format is standardized for fair comparison (Roy et al., 11 Jul 2025).
The reported POPE results show improvements for all tested models. The paper gives the following accuracy values:
| Model | Baseline accuracy | With LDP |
|---|---|---|
| gpt-4o | 0.860 | 0.873 |
| Qwen2.5-VL | 0.7267 | 0.9000 |
| ViLT | 0.8533 | 0.9267 |
| BLIP | 0.8733 | 0.9533 |
The paper also reports precision, recall, and F1, and notes that recall and F1 improve consistently. One highlighted result is that Qwen2.5-VL increases from 0.7267 to 0.9000 accuracy on POPE (Roy et al., 11 Jul 2025).
On GQA, LDP again improves every reported model:
| Model | Baseline accuracy | With LDP |
|---|---|---|
| Qwen2.5-VL | 0.5007 | 0.6592 |
| ViLT | 0.527 | 0.627 |
| BLIP | 0.5552 | 0.6704 |
These gains are presented as evidence that the method helps not only with hallucination suppression but also with spatial and semantic reasoning. Because GQA requires understanding object relations and attributes, the paper interprets the depth captions as providing exactly the kind of structural context the model needs (Roy et al., 11 Jul 2025).
6. Modularity, compatibility, and scope
ByDeWay is presented as lightweight because it uses no training or fine-tuning and relies only on off-the-shelf components: Depth Anything V2 for depth estimation, KOSMOS-2 for region captioning, and a target MLLM for answering (Roy et al., 11 Jul 2025). The separation of these stages is also the basis for its modularity. Depth estimation, segmentation, captioning, and downstream reasoning are distinct components, and the paper notes that they can be swapped if better alternatives are available.
Its black-box compatibility follows from the fact that the target MLLM only receives a prompt. The method therefore remains usable when the model is proprietary, internal weights are inaccessible, or no gradient updates are possible (Roy et al., 11 Jul 2025). This property is operationally important because many deployed MLLMs are exposed only through inference APIs.
The experiments are intended to support a broader model-agnostic claim. The tested systems range from BLIP and ViLT to gpt-4o and Qwen2.5-VL, and all show improvement under LDP (Roy et al., 11 Jul 2025). This suggests that the contribution is not tied to one architecture. A plausible implication is that the gains arise from the structure of the added evidence rather than from any one model-specific inductive bias.
The scope of the reported findings nevertheless remains defined by the benchmarks and subsets used in the paper. The evaluation uses 150 samples on POPE and 150 samples on GQA (Roy et al., 11 Jul 2025). This does not diminish the reported gains, but it situates them within a controlled evaluation setting rather than a fully exhaustive benchmark sweep.
7. Interpretation and significance
The paper’s broader message is that explicit spatial context can be supplied at the prompt level in a way that materially improves multimodal performance without retraining (Roy et al., 11 Jul 2025). ByDeWay reframes part of multimodal reasoning as an evidence-formatting problem: rather than expecting the model to infer all scene structure directly from the image, it augments the prompt with a layered decomposition of the scene grounded in depth.
This is significant for two reasons. First, it addresses hallucination and grounding jointly. The same structured prompt that reduces unsupported guessing also supports finer-grained reasoning about relative location and scene layout. Second, it offers a practical intervention point for black-box MLLMs. Because no model parameters are modified, the method can be adopted in settings where fine-tuning is infeasible or impossible (Roy et al., 11 Jul 2025).
A common misconception would be to interpret ByDeWay as a depth-estimation method or as a training-time alignment procedure. The paper does not present it in either way. Depth estimation is an off-the-shelf preprocessing step, and the contribution lies in converting that information into region-specific captions that are appended to the prompt. Likewise, the method does not introduce a learned objective over the target MLLM; it is a prompt construction and inference pipeline (Roy et al., 11 Jul 2025).
In summary, ByDeWay proposes a training-free mechanism for enriching MLLM prompts with explicit depth-aware scene structure. Its Layered-Depth-Based Prompting strategy estimates 3, partitions the scene into closest, mid-range, and farthest layers using thresholds at the 30th and 70th percentiles, captions each layer with KOSMOS-2, and appends those captions to the original image-question prompt. Across POPE and GQA, the reported results show consistent improvement across multiple MLLMs, supporting the claim that structured depth-aware text can improve grounding, reduce hallucination, and strengthen multimodal reasoning in a zero-training setting (Roy et al., 11 Jul 2025).
