Spatial Signal Prompting Overview

• Spatial Signal Prompting is a technique that embeds explicit spatial cues—such as keyframes, camera poses, and trajectory overlays—into models to improve geometric reasoning.
• It addresses multimodal models’ limitations in spatial consistency by replacing dense, raw context with structured, spatially grounded signals.
• Applications span robotics, video analysis, and remote sensing, demonstrating measurable improvements in tasks like robot manipulation and zero-shot 3D QA.

to=arxiv_search 】【。】【”】【json code {"query":"\"Spatial Signal Prompting\" OR TraceVLA OR See&Trek OR SpatialPrompting", "max_results": 10, "sort_by":"relevance"} Spatial Signal Prompting denotes a class of prompting strategies that inject explicit, spatially grounded cues into a model’s input or internal conditioning pathway so that geometry, locality, motion, topology, or pose are not left to be inferred solely from raw perceptual streams and unconstrained language. In the literature, these cues take heterogeneous forms—visual traces over images, keyframe sets with trajectory-aligned markers, camera poses, coordinate systems, region and point prompts, scene graphs, boundary-conditioned token subsets, graph neighborhoods, and prompt-conditioned spatial audio features—but they share the same functional goal: to bind symbolic intent to spatial structure in a form the target model can reliably parse and exploit (Zheng et al., 2024, Li et al., 19 Sep 2025, Zhu et al., 11 Mar 2025, Jiang et al., 2 Jul 2026).

1. Conceptual scope and motivating problem

The central motivation for Spatial Signal Prompting is that many multimodal models inherit strong semantic priors yet remain brittle on geometry, motion, and spatial consistency. In robot manipulation, large VLA models can recognize task semantics but still struggle with kinematics, trajectory history, and disambiguating moving from static scene elements; purely textual prompting cannot bind time-varying spatial structure to pixels, while video conditioning can flood the model with redundant frames (Zheng et al., 2024). In long-video MLLMs, the analogous bottlenecks are visual homogeneity and unknown motion: uniform frame sampling often surfaces uninformative fragments, and the absence of explicit trajectory or pose cues degrades reasoning about left/right/behind, distance, and temporal order (Li et al., 19 Sep 2025). In remote sensing, language alone is often too weak a referential mechanism for cluttered, multi-scale overhead scenes, because it does not specify which instance or location should ground the answer (Zhang et al., 2024). In LVLM spatial-relation benchmarks, models also hallucinate pairwise relations and violate basic inverse or transitivity constraints, which motivates prompt formats that make spatial structure explicit and logically checkable (Wu et al., 12 Feb 2025).

Across these settings, Spatial Signal Prompting is not a single algorithm but a design principle. TraceVLA describes it directly as providing “an explicit, spatially grounded cue to a policy—beyond raw images and text—to encode the geometry and temporal evolution of states and actions,” and further states that the idea generalizes to “a family of approaches—keypoint maps, heatmaps, affordance contours” (Zheng et al., 2024). Other papers instantiate the same principle with different substrates: keyframes plus camera poses for zero-shot 3D QA, coordinate-aware multi-view prompts for driving, prompt-guided enhancement of inter-channel phase difference for selective sound localization, and boundary-conditioned graph tokenization for urban region representations (Taguchi et al., 8 May 2025, Wu et al., 28 Oct 2025, Jiang et al., 2 Jul 2026, Zhu et al., 11 Mar 2025).

A common misconception is that Spatial Signal Prompting is equivalent to adding more perceptual context. The surveyed work argues otherwise. Several methods explicitly replace dense raw context with compressed, structured cues: traces instead of raw frame stacks, semantically selected keyframes instead of uniform video sampling, prompt-derived masks instead of full attention steering, and logical relation templates instead of free-form chain-of-thought (Zheng et al., 2024, Li et al., 19 Sep 2025, Wu et al., 12 Feb 2025). This suggests that the defining feature is not volume of context but the externalization of spatial structure into a model-compatible prompt.

2. Representational forms of spatial signals

The representational vocabulary of Spatial Signal Prompting is broad, but the signals are usually compact, localized, and instruction-aligned.

Domain	Spatial signal form	Representative papers
Robot manipulation	2D motion traces, heatmap-style renderings, keypoint maps, affordance contours	(Zheng et al., 2024)
Video spatial reasoning	Keyframes with color-coded indices, BEV and 3D trajectory plots, relative “points” metadata	(Li et al., 19 Sep 2025)
Zero-shot 3D QA	Keyframes with camera position and Euler rotation, FOV/coverage proxies, sharpness-aware selection	(Taguchi et al., 8 May 2025)
Autonomous driving	Six-camera layout rules, explicit image coordinates, magenta markers, 256×256 crops, adaptive history	(Wu et al., 28 Oct 2025)
VLM spatial reasoning	Scene graphs, motion labels across frames, bidirectional and transitivity relation templates	(Ji et al., 6 Jul 2025, Wu et al., 12 Feb 2025)
Graph, RS, urban, audio	1-hop neighbor/value prompts, boxes and points, boundary-selected token sets, prompt-guided IPD enhancement	(Yan et al., 2024, Zhang et al., 2024, Zhu et al., 11 Mar 2025, Jiang et al., 2 Jul 2026)

In robotics, TraceVLA renders recent motion directly into the current image. It synthesizes 2D traces from image-plane point tracks, selects “active” points whose motion exceeds a threshold, and overlays a compact set of traces with configurable thickness, opacity, and temporal color gradients (Zheng et al., 2024). The key property is that trajectory history becomes a spatial mark in the same visual stream used for action prediction. STOP applies a related idea within CLIP-based video understanding, but its intra-frame prompts are injected only at the top-$N_s$ discriminative patches chosen by fusing CLS-to-patch attention with motion magnitude, while its inter-frame prompts are inserted only between frames with high temporal variation (Liu et al., 20 Mar 2025).

In MLLM spatial reasoning, the signals are often synthetic views rather than overlays. See&Trek selects semantically rich and temporally dispersed keyframes, reconstructs relative camera motion with monocular visual odometry, overlays each keyframe with an index and a color-coded marker, and appends both bird’s-eye-view and 3D trajectory visualizations together with textual instructions that explain how frame colors align with the trajectory plots (Li et al., 19 Sep 2025). SpatialPrompting for 3D QA instead supplies a smaller set of keyframes with explicit camera position and Euler-angle rotation metadata, relying on the model to infer layout from images plus pose descriptions (Taguchi et al., 8 May 2025). CoV extends the notion from passive prompting to active prompting: the “signal” includes not just selected anchor views but also the evolving sequence of camera actions and newly acquired observations during test-time exploration (Zhao et al., 8 Jan 2026).

Other domains broaden the term further. EarthMarker treats boxes and points as first-class visual prompts encoded by the same vision backbone as the image, enabling image-, region-, and point-level grounding in remote sensing (Zhang et al., 2024). BPURF turns a polygon, mask, buffer, or box into a boundary prompt that selects a token subset and subgraph from a heterogeneous urban graph (Zhu et al., 11 Mar 2025). LLM-OSR serializes 1-hop graph neighborhoods and the previous node value into a compact natural-language prompt for online graph-signal reconstruction (Yan et al., 2024). SelectTSL uses text and audio prompts to bias selective enhancement of inter-channel phase cues toward the queried sound source, so the prompt enters the spatial signal processing stack rather than only the semantic front-end (Jiang et al., 2 Jul 2026).

3. Integration patterns and system architectures

Spatial Signal Prompting is typically integrated in one of three ways: as additional input views, as structured prompt tokens or metadata, or as prompt-conditioned modulation inside a model submodule.

TraceVLA exemplifies dual-view fusion. The model concatenates the original image and the image with the trace overlay as two visual inputs separated by a special separator token, and adds a short text hint that the second image contains visual traces. This preserves unobstructed pixels while giving the VLM an explicit spatial-temporal cue. The architecture otherwise follows OpenVLA’s end-to-end design: a vision encoder maps image patches to token space, a multimodal transformer jointly attends over visual tokens and language, and an autoregressive action head predicts discretized action tokens. A training-time “trace dropout” replaces the trace image with the original and removes the hint, so the policy remains usable when traces fail at test time (Zheng et al., 2024).

Training-free systems usually leave the backbone unchanged and manipulate only prompt construction. See&Trek standardizes the prompt to a small set of annotated keyframes plus BEV and 3D trajectory plots, then appends textual instructions explaining frame indices, color mapping, and spatial points before a single forward pass through the target MLLM (Li et al., 19 Sep 2025). SpatialPrompting for zero-shot 3D QA builds a structured natural-language preamble, then lists per-keyframe camera position and rotation alongside the images, followed by dataset-specific answer templates and the query (Taguchi et al., 8 May 2025). In autonomous driving, task-specific expert prompts are paired with a visual assembly module that arranges the six camera views in canonical order, adds coordinate markers and centered crops for referenced objects, and selectively includes historical frames according to question type (Wu et al., 28 Oct 2025). CoV keeps the model unchanged as well, but stages prompting into a coarse view-selection phase and a fine-grained action–reasoning loop that acquires new views from the 3D scene representation during inference (Zhao et al., 8 Jan 2026).

A second integration pattern is prompt-aware encoding. EarthMarker rasterizes region and point prompts into a prompt image and feeds both the image and prompt image through the same Mixture of Visual Experts, so prompt and image tokens share feature semantics before projection into Llama 2 (Zhang et al., 2024). SSG-DiT derives a text-aware spatial mask from CLIP penultimate-layer patch features, composites a “prompted image” that sharpens semantically relevant regions and smooths background, then injects the resulting visual condition together with text into a frozen video DiT through a lightweight cross-attention SSG-Adapter (Hu et al., 23 Aug 2025). This arrangement is explicitly decoupled: spatial signal generation is separate from the generative backbone.

A third pattern is prompt-conditioned internal modulation. SelectTSL uses a Prompt-Guided Selective Attention module to produce target-aware embeddings from audio and text prompts, then routes these embeddings into an IPD Enhancer that refines raw phase cues before DoA decoding (Jiang et al., 2 Jul 2026). S-PRISM, in a different modality, places an inferring agent between spatial workspace events and the refining agent, converting frames, highlights, notes, and positional edits into explicit refinement goals and scoped text revisions (Tang et al., 21 Apr 2026). These systems do not merely append more context; they alter which internal features are amplified or revised.

4. Algorithmic and mathematical formulations

Although the implementations differ, the mathematical core of Spatial Signal Prompting is usually a mapping from latent or observed spatial structure into an explicit prompt variable.

TraceVLA formalizes trajectory prompting in image space. If a 3D pose sequence is available, it can be projected as

$u_t = \Pi\!\left(K [R \mid t] p_t\right),$

but the reported system usually bypasses calibration and works directly with 2D point tracks extracted by CoTracker. Given projected trajectory points over a time window, a drawing operator produces the overlay

$$V = \mathrm{Draw}(\{u_t\}_{t=1}^T; \theta_{\mathrm{draw}}),$$

and a heatmap-style rendering can instead use

$$V(x, y) = \sum_{t=1}^T w_t \cdot \exp\!\left(-\frac{\|[x,y]^\top - u_t\|^2}{2\sigma^2}\right).$$

To avoid clutter, motion is scored by per-step $L_1$ displacement and accumulated as $m(p)=\sum \Delta p_{t'}$, after which only active tracks above threshold $\kappa$ are sampled for rendering (Zheng et al., 2024).

See&Trek and SpatialPrompting formalize signal selection rather than overlay synthesis. See&Trek’s Maximum Semantic Richness Sampling chooses an initial frame with maximal detected class count and then selects one frame per temporal segment by minimizing overlap with already selected classes while maximizing class count. Motion reconstruction estimates an essential matrix from ORB matches,

$$E = \arg\min_{E'} \sum_i \rho\!\left((\hat{x}_i^t)^\top E' \hat{x}_i^{t-1}\right),$$

decomposes it into relative rotation and translation, and recursively updates global pose to render BEV and 3D trajectories (Li et al., 19 Sep 2025). SpatialPrompting for 3D QA uses point-cloud summaries and CLIP features to prune redundant frames. Its spatial dissimilarity is

$$d(i,j) = (\mu_i - \mu_j)^\top \left(\frac{\Sigma_i + \Sigma_j}{2}\right)^{-1} (\mu_i - \mu_j),$$

semantic similarity is CLIP cosine similarity $S(i,j)$, and the combined redundancy score is

$u_t = \Pi\!\left(K [R \mid t] p_t\right),$0

with per-frame quality

$u_t = \Pi\!\left(K [R \mid t] p_t\right),$1

Greedy pruning removes the lower-quality member of the most redundant pair until only $u_t = \Pi\!\left(K [R \mid t] p_t\right),$2 keyframes remain (Taguchi et al., 8 May 2025).

Some formulations are explicitly logical rather than geometric. Constraint-aware prompting for LVLMs defines bidirectional consistency through an inverse mapping $u_t = \Pi\!\left(K [R \mid t] p_t\right),$3 over spatial relations:

$u_t = \Pi\!\left(K [R \mid t] p_t\right),$4

and transitivity along an axis as

$u_t = \Pi\!\left(K [R \mid t] p_t\right),$5

These constraints are not enforced through decoding or training losses; they are embedded as ordered prompt templates that require the model to emit intermediate relations before the final answer (Wu et al., 12 Feb 2025). SceneGraph CoT applies a related strategy by forcing VLMs to first generate a JSON scene graph $u_t = \Pi\!\left(K [R \mid t] p_t\right),$6 containing objects, attributes, and relations, then answer from that graph in a second stage (Ji et al., 6 Jul 2025).

In signal-processing domains, the prompt can modulate feature refinement directly. SelectTSL computes

$u_t = \Pi\!\left(K [R \mid t] p_t\right),$7

uses cosine and sine encodings to avoid phase wrapping, and applies a gated FiLM fusion

$u_t = \Pi\!\left(K [R \mid t] p_t\right),$8

followed by cosine–sine residual prediction

$u_t = \Pi\!\left(K [R \mid t] p_t\right),$9

Here the prompt-conditioned semantic branch biases which phase cues are sharpened for DoA estimation (Jiang et al., 2 Jul 2026). BPURF, by contrast, defines the prompt as a spatial boundary $$V = \mathrm{Draw}(\{u_t\}_{t=1}^T; \theta_{\mathrm{draw}}),$$0 and extracts a token set

$$V = \mathrm{Draw}(\{u_t\}_{t=1}^T; \theta_{\mathrm{draw}}),$$1

optionally with soft membership weights $$V = \mathrm{Draw}(\{u_t\}_{t=1}^T; \theta_{\mathrm{draw}}),$$2, before type-wise aggregation and multi-channel subgraph message passing (Zhu et al., 11 Mar 2025).

5. Empirical performance across domains

The empirical record is heterogeneous but consistently favorable to explicit spatial signals over unstructured context expansion.

TraceVLA reports gains across simulation, real robots, and LIBERO. On SimplerEnv, the 7B model improves overall success from 40.2% for OpenVLA to 47.7% for TraceVLA, while the compact 4B version improves from 39.9% to 44.0%. On LIBERO, finetuned TraceVLA reaches $$V = \mathrm{Draw}(\{u_t\}_{t=1}^T; \theta_{\mathrm{draw}}),$$3 average versus $$V = \mathrm{Draw}(\{u_t\}_{t=1}^T; \theta_{\mathrm{draw}}),$$4 for finetuned OpenVLA, with the largest gain on LIBERO-Long, 54.1% versus 45.7%. On a real WidowX-250 task absent from training, pick-place corn, TraceVLA achieves 8/10 successes versus 1/10 for OpenVLA (Zheng et al., 2024).

See&Trek shows that training-free visual-spatial prompts improve long-video MLLMs on VSI-Bench and STI-Bench. On VSI-Bench, average gains across open-source MLLMs range from +1.0% to +3.5%, with the largest gain of +3.5% on InternVL3-1B; task gains include +10.8% on Appearance Order for LLaVA-OneVision-7B. On STI-Bench, the method improves the InternVL3 series by +1.7%, +1.0%, and +1.4%, and reports dynamic-task gains such as +8.6% on Trajectory Description for InternVL3-14B and +16.2% on Ego Orientation for Qwen2.5-VL-7B (Li et al., 19 Sep 2025).

SpatialPrompting for zero-shot 3D QA reports state-of-the-art zero-shot ScanQA performance with GPT-4o, including EM@1 27.34, ROUGE-L 43.39, METEOR 16.85, CIDEr 87.69, and SPICE 20.49, while achieving an SQA3D test average of 52.74. The same paper also shows that removing camera pose drops ScanQA EM@1 from 27.34 to 25.70, and that removing the annotation templates causes a large degradation to EM@1 19.83 and ROUGE-L 31.89 (Taguchi et al., 8 May 2025).

Other application-specific systems exhibit the same pattern. The autonomous-driving prompting framework on Qwen2.5-VL-72B reaches 70.87% average accuracy on Phase-1 clean data and 72.85% on Phase-2 corrupted data, with ablations showing that “+ Crops & coordinate system” gives the largest early gain, +7.05 average in Phase-1 (Wu et al., 28 Oct 2025). SelectTSL reports, on the full Mix condition, MAE 0.98°, Precision 98.28%, F1 95.67%, Recall 93.20%, MOTA* 91.57%, DetA 91.70%, and OSPA-T 2.08°, substantially outperforming prompt-based and prompt-agnostic baselines (Jiang et al., 2 Jul 2026). CoV obtains an average +11.56% improvement in LLM-Match on OpenEQA, with a maximum gain of +13.62% on Qwen3-VL-Flash, and further gains from increasing the minimum action budget by +2.51% on average (Zhao et al., 8 Jan 2026). BPURF reports average improvements over the best baselines of approximately 30.53% in MAE, 29.23% in RMSE, and 35.54% in $$V = \mathrm{Draw}(\{u_t\}_{t=1}^T; \theta_{\mathrm{draw}}),$$5 for dynamic-region prediction (Zhu et al., 11 Mar 2025).

The ablations are as informative as the headline numbers. TraceVLA shows that adding six historical frames separated by tokens reduces performance by 6% relative to OpenVLA, whereas a textual encoding of movements yields only +2.4% average improvement and visual trace prompting adds another