Layered-Depth Prompting in MLLMs

• LDP is a training-free strategy that augments prompts by structuring scene data into three distinct depth layers (closest, mid-range, farthest).
• By partitioning images using 30% and 70% percentiles and applying KOSMOS-2 for layer-specific captioning, LDP enhances spatial grounding.
• Empirical evaluations on POPE and GQA show improved accuracies, precision, recall, and F1 scores, confirming LDP's effectiveness.

Searching arXiv for papers on Layered-Depth-Based Prompting and closely related depth-prompting work. Layered-Depth-Based Prompting (LDP) is a prompting strategy that injects explicit depth-aware spatial structure into model inputs by organizing scene information according to depth strata or decoder-layer hierarchy. In the most direct usage of the term, LDP denotes the prompt augmentation method introduced in ByDeWay for multimodal LLMs (MLLMs): a single RGB image is processed by monocular depth estimation, partitioned into closest, mid-range, and farthest regions, captioned per region, and then augmented with those depth-ordered captions before querying a downstream MLLM (Roy et al., 11 Jul 2025). More broadly, the same phrase can describe a family of prompt-like mechanisms in which depth information is supplied as a structured conditioning signal rather than left implicit in raw pixels alone. This includes metric-depth prompting for depth foundation models, depth-prompt modules for sensor-agnostic depth estimation, and earlier layered-depth scene representations that expose occluded or side content through depth-ordered organization (Lin et al., 2024, Park et al., 2024, Takyar et al., 2013).

1. Definition and conceptual scope

In ByDeWay, LDP is defined as a training-free prompt augmentation framework for MLLMs that uses monocular depth estimation to divide an image into three depth strata—closest layer, mid-range layer, and farthest layer—and then appends region-specific captions for those strata to the original image-question prompt (Roy et al., 11 Jul 2025). The stated purpose is to improve spatial reasoning and grounding without modifying model parameters, thereby making responses more grounded and less hallucinated (Roy et al., 11 Jul 2025).

The central intuition is that many MLLM errors arise from weak scene grounding rather than weak language modeling. A model may know what objects are plausible while still confusing front/back, near/far, foreground versus background, or the presence of an object in the queried region. LDP addresses this by converting a single RGB image into a structured textual summary of depth layers and presenting that summary as explicit prompt context (Roy et al., 11 Jul 2025).

Within the supplied literature, the term also admits a broader technical interpretation. Prompt Depth Anything does not formalize the exact name “Layered-Depth-Based Prompting,” but its core mechanism is described as a multi-scale prompt fusion design in which low-cost LiDAR depth is injected into a Depth Anything / DPT decoder at multiple scales to guide metric depth prediction (Lin et al., 2024). Likewise, Any2Full reformulates depth completion as a scale-prompting adaptation of a pretrained monocular depth estimation model, using hierarchical scale prompts and multi-level fusion into the decoder (Zhou et al., 5 Mar 2026). These works suggest that “layered-depth-based prompting” can denote two related but distinct ideas: depth-layer prompting in the input/prompt space for reasoning models, and layered or hierarchical depth conditioning inside depth-estimation architectures.

A plausible implication is that LDP is less a single algorithm than a design principle: depth structure is externalized into a prompt or prompt-like conditioning pathway so that a pretrained model receives an explicit geometric scaffold instead of having to infer all spatial organization from RGB alone.

2. ByDeWay and the canonical LDP formulation

ByDeWay provides the clearest formalization of LDP as a prompt construction method for MLLMs (Roy et al., 11 Jul 2025). The workflow begins with an RGB image and applies Depth Anything V2 to produce a dense depth map $D(x,y)$. The resulting representation is described as affine-invariant inverse depth, used for coarse scene segmentation rather than metric recovery (Roy et al., 11 Jul 2025).

The depth map is partitioned into three coarse regions using percentile thresholds computed at the 30th and 70th percentiles. This yields masks for the closest, mid-range, and farthest regions, corresponding to top 30%, middle 40%, and bottom 30% of depth values (Roy et al., 11 Jul 2025). Each masked region is then captioned independently with KOSMOS-2, which serves as a grounded vision-LLM. The method therefore produces three depth-ordered captions, one per stratum, instead of a single holistic image caption (Roy et al., 11 Jul 2025).

These captions are concatenated with the original task prompt. The paper’s example prompt for POPE includes an instruction block, the question, a section titled “Image Caption about depth” with entries for Closest, Mid-range, and Farthest, and output constraints such as “Do not guess or assume” and “Return only a single 'yes' or 'no' string” (Roy et al., 11 Jul 2025). The downstream MLLM is then queried with the original image plus this depth-aware textual augmentation, with no fine-tuning, no parameter updates, and no architectural modification (Roy et al., 11 Jul 2025).

The method is explicitly positioned against two alternatives. First, it differs from ordinary image captioning because its captions are layer-specific, structured, and grounding-oriented rather than holistic (Roy et al., 11 Jul 2025). Second, it differs from chain-of-thought prompting because it supplies external visual evidence in textual form before reasoning rather than asking the model to generate internal reasoning traces (Roy et al., 11 Jul 2025). In this respect, LDP functions as a scene decomposition prior expressed in natural language.

3. Algorithmic structure and prompt construction

The algorithmic structure of ByDeWay’s LDP can be summarized directly from the paper’s explicit workflow (Roy et al., 11 Jul 2025). The image $I$ is mapped to a depth field $D(x,y)$, percentile thresholds $T_1$ and $T_2$ are computed at 30% and 70%, binary masks isolate three depth strata, each masked region is captioned by KOSMOS-2, and the resulting captions are appended to the task prompt in a structured template (Roy et al., 11 Jul 2025).

Several design choices are emphasized. The use of three layers only is deliberate: foreground, mid-ground, and background are considered sufficient to supply a concise and interpretable prompt (Roy et al., 11 Jul 2025). The thresholds are percentile-based rather than absolute, which makes the partition depend on relative rank and therefore more robust across different images (Roy et al., 11 Jul 2025). The captioner is grounded, rather than purely generative, because the goal is to constrain the MLLM toward evidence-supported entities and relations (Roy et al., 11 Jul 2025).

The prompt template itself is a substantive component of the method. The structure comprises a task instruction, the original question, a depth-caption block labeled by layer, and an output constraint (Roy et al., 11 Jul 2025). This organization matters particularly on POPE, where answers are restricted to “yes” or “no,” and where hallucination suppression depends not only on extra scene context but also on discouraging unsupported guessing (Roy et al., 11 Jul 2025).

A common misconception is that LDP is merely “adding depth captions.” The paper’s formulation is narrower and more technical. The captions are not arbitrary summaries; they are generated from explicitly masked depth regions, ordered by depth, and injected into a prompt format designed to make spatial reference resolution easier for the MLLM (Roy et al., 11 Jul 2025). The improvement is attributed to this structured evidence summary, not simply to longer prompts.

4. Empirical findings, utility, and limitations

ByDeWay evaluates LDP on POPE and GQA, using subsets of 150 samples from each dataset, and tests gpt-4o, Qwen2.5-VL, ViLT, and BLIP (Roy et al., 11 Jul 2025). On POPE, the reported accuracies improve for all tested models: gpt-4o from 0.860 to 0.873, Qwen2.5-VL from 0.7267 to 0.9000, ViLT from 0.8533 to 0.9267, and BLIP from 0.8733 to 0.9533 (Roy et al., 11 Jul 2025). The paper also states that precision, recall, and F1 improve, with particular emphasis on recall and F1 as indicators of fewer missed grounded positives and stronger overall classification quality (Roy et al., 11 Jul 2025).

On GQA, accuracy likewise improves: Qwen2.5-VL from 0.5007 to 0.6592, ViLT from 0.527 to 0.627, and BLIP from 0.5552 to 0.6704 (Roy et al., 11 Jul 2025). The paper interprets these gains as evidence that depth-aware textual context helps with object reasoning and spatial understanding, especially on hallucination-sensitive tasks and for lighter-weight models such as BLIP and ViLT (Roy et al., 11 Jul 2025).

The practical advantages emphasized are that the method is training-free, modular, model-agnostic, and black-box compatible (Roy et al., 11 Jul 2025). Because the downstream MLLM need only accept image-plus-text input, LDP can be used even when the model is closed-source or too large to retrain (Roy et al., 11 Jul 2025).

The most explicit limitation stated is longer prompts, which may increase inference latency or token cost (Roy et al., 11 Jul 2025). The details also identify several implicit dependencies: monocular depth estimation must be good enough to supply a usable coarse partition, KOSMOS-2 captions must accurately describe masked regions, and three depth layers must be sufficient to summarize the scene (Roy et al., 11 Jul 2025). This suggests that LDP inherits failure modes from both the depth estimator and the regional captioner; errors in either stage can propagate into the final prompt.

5. Relation to earlier layered-depth representations

Although ByDeWay applies LDP in prompt engineering for MLLMs, the broader idea of organizing scene information by depth layers has antecedents in layered scene representations for view synthesis and navigation. “Multiview Navigation based on Extended Layered Depth Image Representation” represents a navigation segment with an extended LDI that stores scene content in multiple depth-ordered layers and enlarges image support to preserve side information that would fall outside a reference camera’s field of view (Takyar et al., 2013). Its goal is to avoid repeating the same 3D point across multiple views, support synthesis of arbitrary views inside the segment, and transmit a self-contained representation for interactive navigation (Takyar et al., 2013).

In that representation, Layer 1 contains the visible surface from the reference camera, deeper layers contain progressively more occluded surfaces, and the enlarged support retains side regions from neighboring views that classical LDI would discard (Takyar et al., 2013). The decoder can reconstruct views by “unwarping” the extended LDI, and the representation removes inter-view redundancy at the representation stage rather than relying solely on multiview predictive coding (Takyar et al., 2013). The paper reports a significant rate-distortion gain compared with classical multiview compression approaches in the navigation scenario (Takyar et al., 2013).

A related but different use of layered depth appears in “3D Photography using Context-aware Layered Depth Inpainting,” which converts a single RGB-D image into a 3D photo using an LDI with explicit pixel connectivity and a context-aware inpainting pipeline for occluded regions (Shih et al., 2020). The method identifies depth discontinuities, disconnects foreground and background across the edge, defines local synthesis and context regions, predicts missing depth edges, then inpaints color and depth (Shih et al., 2020). The paper is not a prompting method in the language-model sense, but it treats layered geometry and connectivity as a structured scaffold for conditional generation (Shih et al., 2020).

These earlier works do not define LDP as a prompt augmentation technique for MLLMs. However, they demonstrate a recurring technical principle: depth-ordered decomposition can preserve hidden structure, resolve occlusions, and provide a compact or conditionally useful representation of a scene (Takyar et al., 2013, Shih et al., 2020). This suggests that ByDeWay’s LDP is part of a longer line of work in which depth layers are used to expose scene organization explicitly rather than leaving it latent.

6. Adjacent prompt-based depth paradigms

Depth prompting also appears in depth-estimation research, though often without the exact ByDeWay meaning of LDP. “Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation” reframes metric depth estimation as a prompting problem in which low-cost LiDAR depth acts as a metric prompt for a Depth Anything v2 backbone with a ViT + DPT decoder (Lin et al., 2024). The prompt is injected at multiple decoder scales by resizing LiDAR depth to stage resolution, encoding it with a shallow convolutional network, projecting it with a zero-initialized $1\times1$ convolution, and fusing it additively with decoder features (Lin et al., 2024). The paper describes this as a multi-scale prompt fusion design and reports state-of-the-art results on ARKitScenes and ScanNet++, including L1 0.0135 and RMSE 0.0326 on ARKitScenes at $384 \times 512$, with testing up to $2160 \times 3840$ (Lin et al., 2024).

“Depth Prompting for Sensor-Agnostic Depth Estimation” likewise treats sparse depth as a prompt, but not as layered depth strata (Park et al., 2024). The method uses a depth prompt encoder $f_{\mathcal{E}}$, a frozen monocular depth foundation model $f_{\mathcal{F}}$, and an adaptive affinity decoder $I$0 to produce an adaptive affinity map for spatial propagation (Park et al., 2024). A least-squares alignment

$I$1

converts relative monocular depth to metric absolute scale (Park et al., 2024). The paper explicitly states that this is prompt-based, but not layered-depth-based prompting in the sense of discrete depth layers or layer-wise prompt tokens (Park et al., 2024).

Any2Full extends this architectural direction by treating sparse metric depth as scale prompts for a frozen pretrained monocular depth estimator, specifically Depth Anything v2, using a Scale-Aware Prompt Encoder with Local Enrichment, Global Propagation, and hierarchical FiLM-style fusion into decoder features (Zhou et al., 5 Mar 2026). The method is described as one-stage, domain-general, and pattern-agnostic, and the paper reports that it outperforms OMNI-DC by 32.2\% in average AbsREL and provides a 1.4\times speedup over PriorDA with the same MDE backbone (Zhou et al., 5 Mar 2026).

Taken together, these papers show that “prompting with depth” currently spans at least three regimes. First, ByDeWay uses textualized depth layers to improve reasoning in MLLMs (Roy et al., 11 Jul 2025). Second, Prompt Depth Anything and Any2Full use hierarchical or multi-scale depth prompts inside depth foundation models (Lin et al., 2024, Zhou et al., 5 Mar 2026). Third, sensor-agnostic depth prompting uses sparse depth as a continuous prompt embedding rather than as discrete layers (Park et al., 2024). A plausible implication is that the common denominator is not the modality alone, but the conversion of depth from a latent cue into an explicit conditioning interface.

7. Interpretation, significance, and boundaries of the term

Within the available literature, the most precise use of “Layered-Depth-Based Prompting” is the ByDeWay method for MLLMs (Roy et al., 11 Jul 2025). In that strict sense, LDP means depth-stratified region captioning appended to the image-question prompt, with the aim of improving hallucination resistance and visual reasoning in a zero-training setting (Roy et al., 11 Jul 2025). Its significance lies in showing that prompt-level spatial grounding can improve multimodal reasoning without any model updates.

At the same time, the term is used more loosely in some technical syntheses to group together prompting methods that inject depth information in layered, multi-scale, or hierarchically structured form (Lin et al., 2024, Zhou et al., 5 Mar 2026). This broader usage should be treated carefully. Depth Prompting for Sensor-Agnostic Depth Estimation explicitly distinguishes itself from layered-depth prompting, since it does not discretize depth into layers, use ordered depth strata, or build prompts as a stack of layers (Park et al., 2024). Conversely, earlier LDI and layered inpainting methods are highly relevant conceptually, but they are not prompt engineering methods in the MLLM sense (Takyar et al., 2013, Shih et al., 2020).

A common source of confusion is the abbreviation LDP itself. In a different research area, “LDP” denotes local differential privacy, as in “Utility Analysis and Enhancement of LDP Mechanisms in High-Dimensional Space” (Duan et al., 2022). That literature concerns privacy-preserving perturbation and aggregation and is unrelated to layered-depth prompting despite the shared acronym (Duan et al., 2022).

The current evidence suggests that layered depth is useful when the target task depends on scene grounding, occlusion order, scale anchoring, or geometric consistency. In MLLMs, this manifests as improved handling of front/back and near/far relations (Roy et al., 11 Jul 2025). In depth estimation, it manifests as better metric consistency and cross-pattern robustness through prompt fusion into pretrained backbones (Lin et al., 2024, Zhou et al., 5 Mar 2026). A plausible implication is that future work will continue to move depth from an implicit latent variable to an explicit prompt substrate, whether expressed as text, masks, scale cues, or hierarchical decoder modulation.