Explicit-Implicit Dual Grounding

• Explicit–Implicit Dual Grounding is a multimodal machine learning strategy that anchors perceptual evidence through both human-interpretable explicit cues and deep implicit alignments.
• It employs explicit grounding to produce intermediate tokens like descriptions and spatial anchors, while using implicit grounding to optimize end-to-end feature fusion.
• This approach is applied in diverse tasks such as multimodal reasoning, GUI automation, video grounding, and robotic control to enhance auditability and performance.

Explicit–Implicit Dual Grounding is a foundational architectural and algorithmic strategy in multimodal machine learning where perceptual evidence—whether visual, textual, or spatiotemporal—is anchored through two complementary mechanisms: explicit grounding, which enforces intermediate interpretable representations, and implicit grounding, which relies on internal model alignments without surfacing interpretable steps. The dual grounding paradigm has become central in tasks including multimodal reasoning, grounding in text and images, GUI automation, temporal video grounding, and robotic control. This approach enables models to balance robust, auditable inference with flexible, scalable generalization, and is supported by a growing body of experimental evidence across diverse domains.

1. Core Definitions and Taxonomy

Explicit grounding requires the model to generate, consume, or align with intermediate representations—often human-interpretable—that enumerate salient perceptual facts before reasoning or action. These can be model-generated descriptions, external annotations, or explicit spatial/temporal cues. For instance, in vision–LLMs, explicit grounding can appear as forcing the model to output a visual “description” block prior to giving an answer, or as supplying grids/ruler tokens to anchor pixel coordinates (Ding et al., 29 Sep 2025, Wang et al., 3 Oct 2025).

Implicit grounding, by contrast, is characterized by direct optimization of end-to-end objectives, with perceptual association encoded in deep features or model attention, never surfaced as distinct interpretable variables. The model must align, for example, patch embeddings and language through gradients imposed solely by the final loss, as in standard transformer-based fusions or attention-driven grounding (Cao et al., 10 Oct 2024, Wang et al., 18 Feb 2025).

A formal taxonomy distinguishes these as:

Mode	Representation Surface	Optimization Target
Explicit	Intermediate tokens/fields (e.g., <description>, RULER, visual notes)	Cross-entropy or auxiliary reward on interpretable elements
Implicit	Internal vectors/attention (not externalized)	Standard next-token or regression loss, no intermediate targets

Explicit and implicit modes exist in all modalities (visual, textual, spatial, temporal) and may be instantiated in sequence, in parallel branches, or in intertwined reward functions (Ding et al., 29 Sep 2025, Luo et al., 29 Feb 2024, Luo et al., 25 Aug 2024).

2. Mathematical Formulations

In explicit grounding, models introduce a separate head or prompt designed to produce discrete intermediate representations. For visual grounding, this may involve an attention-weighted projection from patch embeddings to a sequence of “description tokens,” optimized under a cross-entropy loss:

$$L_{\text{desc}} = -\sum_{t} \log p(y^d_t | y^d_{<t}, x)$$

where $y^d$ is the description sequence. The reasoning module is conditioned on these tokens:

$$h^0 = \text{TransformerEmbed}(\langle\text{description}\rangle, \langle\text{think}\rangle)$$

For explicit spatial grounding, anchor tokens ("RULER") carrying face-value coordinates are appended to the token stream, enabling a two-step copy-and-refine operation for regression tasks (Wang et al., 3 Oct 2025):

$$(x_{\text{pred}}, y_{\text{pred}}) = (x_k, y_k) + (\delta_x, \delta_y), \quad |\delta_{(\cdot)}| \leq b$$

Implicit grounding is instantiated by fusing modality-specific embeddings and propagating joint features through attention or self-attention networks, with alignment enforced only by the primary downstream objective (e.g., next-token loss, regression to output):

$L_{\text{imp}} = -\sum_t \log p(y_t | y_{<t}, x)$

In weakly or partially supervised regimes, explicit–implicit dual grounding may be realized via causal graphical models (Luo et al., 29 Feb 2024) or progressive label refining pipelines (Wang et al., 18 Feb 2025), often using intervention and counterfactual estimators to disentangle explicit/direct from implicit/indirect effects.

3. Dual-Grounding Architectures and Training Pipelines

Representative explicit–implicit dual grounding systems employ one of three patterns:

• Sequential/Two-Stage (Decoupled): Stage I generates explicit perceptual descriptions or pseudo-labels; Stage II refines reasoning or grounding outputs, conditioned on these intermediate representations and optimizing both explicit and implicit objectives (Ding et al., 29 Sep 2025, Wang et al., 18 Feb 2025).
• Parallel/Hybrid (Fusion): Both explicit features (e.g., reconstructed maps, grid IDs) and implicit latents (e.g., successor-state distributions, attention bottlenecks) are concatenated or jointly attended by the downstream policy or reasoning head (Luo et al., 25 Aug 2024).
• Causal Intervention and Counterfactual Adjustment: Explicit/implicit signals are made separable through front-door adjustment (intervention) and counterfactual estimators that isolate direct (explicit) and indirect (implicit) effects (Luo et al., 29 Feb 2024).

A common design is the introduction of explicit anchor mechanisms (description tokens, RULER tokens, grid overlays), enabling models to fixate attention, copy values, or index spatial positions—enhancing auditability and supporting error tracing—while internal representations (implicit) capture residual dependencies and drive generalization (Ding et al., 29 Sep 2025, Wang et al., 3 Oct 2025).

Reinforcement learning or contrastive learning may be layered over explicit–implicit architectures, supplying fine-grained rewards or discriminative gradients for both representation forms (e.g., visual/textual key info, consistency, contrast-unity losses) (Ding et al., 29 Sep 2025, Wang et al., 18 Feb 2025).

4. Empirical Results and Benchmarking

The dual-grounding approach has been systematically evaluated across a range of benchmarks:

• Multimodal Reasoning Benchmarks: On tasks such as MathVista and MMMU, explicit–implicit dual grounding (as in VTPerception-R1) yields substantial gains, especially when employing explicit perceptual grounding followed by RL-based implicit alignment, with MathVista accuracy increasing from 66.4% (baseline) to 71.0% (full RL) (Ding et al., 29 Sep 2025).
• GUI Automation and Grounding: Mark-Grid Scaffold (explicit) enables a jump in click accuracy from 5.50% (direct prediction, implicit) to over 72% on ScreenSpot-v2, illustrating the critical value of explicit cues in coordinate emission (Li et al., 15 Sep 2025).
• Phrase and Object Grounding: In weakly supervised phrase grounding, causal dual-grounding (IECI) significantly outperforms both explicit-only and implicit-only methods, achieving Recall@1 of 61.32% on implicit cases (vs. 20–58% for baselines) (Luo et al., 29 Feb 2024).
• Video and Temporal Grounding: The Contrast-Unity progressive framework achieves nearly full supervision quality under partial supervision (single-frame or 2–4s clip), with explicit pseudo-label refinement benefiting from high-quality implicit-stage representations (e.g., [email protected] of 61.51% with single-frame seeds) (Wang et al., 18 Feb 2025).
• Robotic Perception–Action: The PIE framework demonstrates that explicit–implicit estimation fusion—explicit regression of height-maps/velocities and implicit VAE bottlenecks—results in robust, zero-shot transfer for legged robot parkour on challenging terrains (Luo et al., 25 Aug 2024).

A repeated finding is that explicit grounding yields immediate, especially strong gains for smaller models and in domains requiring semantic disambiguation, while implicit grounding offers additional refinement and generalization capacity, particularly in large-scale, high-dimensional settings.

5. Auditability, Generalization, and Limitations

Explicit–implicit dual grounding enables easier error tracing, debugging, and model audit. Intermediate fields (e.g., <description>, explicit overlays) create a step-wise audit trail mapping perceptual input to output, supporting systematic error localization and model introspection (Ding et al., 29 Sep 2025, Li et al., 15 Sep 2025).

Generalization is improved by decoupling perception and reasoning, reducing reliance on spurious correlations, and establishing robust transfer across domains using a limited corpus of task-annotated examples plus explicit cues. For example, explicit–implicit frameworks scale to high-resolution, platform-diverse GUIs (negligible token overhead), multiple video domains, and real-robot applications (Wang et al., 3 Oct 2025, Luo et al., 25 Aug 2024), and robustly adapt to new environments with moderate annotation and rollout cost (Ding et al., 29 Sep 2025).

Limitations include potential overlay clutter in explicit spatial scaffolds, reduced granular precision for discretized explicit anchors, and the need for carefully balanced intervals and representation schemes; in some settings, explicit cues may under-sample high-density regions or interact unfavorably with model-specific spatial encodings (Li et al., 15 Sep 2025, Wang et al., 3 Oct 2025).

6. Analysis and Future Research Directions

Experimental analysis consistently reveals that explicit cues prevent overfitting and hallucination in models with limited capacity, directly exposing perceptual facts otherwise lost in deep representations. Implicit strategies—optimized via end-to-end loss, VAE bottlenecks, or causal effect reweighting—further refine alignment and support high-dimensional, out-of-distribution generalization (Ding et al., 29 Sep 2025, Luo et al., 29 Feb 2024, Luo et al., 25 Aug 2024).

Identified open challenges include:

• Commonsense and affordance reasoning for implicit grounding: Models struggle to bridge semantic gaps when target entities are not lexically referenced (as evidenced in ToG-Bench, where implicit accuracy lags explicit by 8–23 points even for top-tier models) (Xu et al., 3 Dec 2025).
• Adaptive explicit anchor mechanisms: Dynamic, density-aware grids or multi-scale anchors may mitigate overlay limitations and improve resolution adaptation in GUI and spatial grounding (Wang et al., 3 Oct 2025).
• Integration of structured knowledge and functional schemas to support grounded reasoning in the absence of direct mentions or with ambiguous, context-dependent targets (Xu et al., 3 Dec 2025).
• Unified causality-driven frameworks: Systematic use of intervention, counterfactual, and front-door adjustments to reconcile explicit–implicit attribution, especially for weakly or partially supervised data (Luo et al., 29 Feb 2024, Wang et al., 18 Feb 2025).

Potential advances include hybrid reinforcement learning and contrastive schemes keyed to both explicit anchors and implicit attention, automated annotation of explicit–implicit categories, and principled architectural motifs for joint or progressive information fusion.

7. Representative Frameworks and Summative Table

A condensed overview of recent systems illustrating explicit–implicit dual grounding approaches:

System	Explicit Mechanism	Implicit Mechanism	Domain	Reference
VTPerception-R1	<description> tokens and visual notes	End-to-end RL on fused features	Multimodal LLM reasoning	(Ding et al., 29 Sep 2025)
RULER + I-MRoPE	Explicit coordinate anchor tokens	Interleaved balanced RoPE (spatial)	GUI ground/nav	(Wang et al., 3 Oct 2025)
IECI	Causal intervention, explicit pairs	Counterfactual effect, latent attention	Phrase grounding (WPG)	(Luo et al., 29 Feb 2024)
Mark-Grid Scaffold	Grid overlays, labeled cell IDs	Attention heatmap/Pointing Game	GUI VLMs	(Li et al., 15 Sep 2025)
Contrast-Unity	Pseudo-labels, explicit regression	Quadruple contrastive representation	Temporal sentence grounding	(Wang et al., 18 Feb 2025)
PIE	Heightmap, velocity regression	Latent state predictor (VAE)	Legged robot control	(Luo et al., 25 Aug 2024)

These frameworks typify the trend toward bridging interpretability, robustness, and generalization by integrating explicit, human-intelligible constraints with implicit, deeply optimized model features in multimodal, spatiotemporal, and embodied machine learning.