ANY2ANY Framework: Modular Arbitrary Mapping

• ANY2ANY framework is a versatile architecture that enables efficient, scalable mapping across modalities, formats, and embodiments via a shared latent space.
• It utilizes lightweight adaptation layers and universal pivot representations to perform arbitrary translations in domains like remote sensing, robotics, and multimodal retrieval.
• Empirical results confirm that ANY2ANY systems reduce combinatorial complexity and maintain robust performance even with zero-shot and incomplete data scenarios.

The term "ANY2ANY framework" refers to a class of architectures, protocols, and model-driven methodologies that enable flexible, systematic, and often provably efficient transformation, translation, or transfer between arbitrary pairs within a domain: modalities, formats, models, or embodiments. Across research, "ANY2ANY" designates fundamentally different frameworks within remote sensing, multimodal retrieval, constraint programming, model transfer for robotics, medical AI, software engineering, and security. Each is unified by the structural principle of arbitrary (not fixed or pairwise) mapping, translation, or adaptation.

1. Foundational Principle: Arbitrary (Any-to-Any) Mapping

The essential characteristic of an ANY2ANY framework is the capacity for systematic and scalable mapping or translation from any source "element" (modality, language, embodiment, etc.) to any target, often without needing direct pairwise supervision or individualized bridges for every possible combination. This stands in contrast to conventional pairwise ("one-to-one") approaches that scale quadratically in the number of supported elements.

Representative key principles include:

• Latent Semantic Alignment: Mapping all source and target elements into a shared latent or semantic space, decoupling modality-specific encoding/decoding from the semantic backbone (Chen et al., 4 Mar 2026).
• Universal or Pivot Representations: Introducing a universal pivot (e.g., metamodel, embedding, latent representation) which all transformations traverse (Chenouard et al., 2010).
• Lightweight Adaptation Layers: Integrating per-target residual or adapter modules to capture systematics or residual mismatches at the output decoding stage, with bounded complexity (Chen et al., 4 Mar 2026).
• Provable Modal-Agnosticism: Enabling arbitrary collections of input and output conditions, including cases with incomplete, missing, or extra components, sometimes with theoretical performance guarantees (Li et al., 2024).

2. Architecture and Methodological Instantiations

a. Unified Latent Diffusion for Modality Translation

In remote sensing, the Any2Any paradigm implements arbitrary modality translation by encoding each modality-specific observation through a Variational Autoencoder into a common latent space and performing diffusion-based semantic regression to sample target representations. Critical components:

• Modality-specific encoders/decoders for geometric alignment.
• Single shared diffusion backbone (e.g., DiT Transformer) handling all mappings.
• Residual adapters per target to correct systematic manifold mismatch at marginal parameter cost.
• Trained with paired data from a highly connected graph (RST-1M dataset), allowing transitive supervision for unseen (zero-shot) modality pairs (Chen et al., 4 Mar 2026).

b. Conformal Prediction for Incomplete Multimodal Retrieval

For retrieval tasks with heterogeneous, incomplete multimodal data, the Any2Any retrieval framework computes all feasible cross-modal similarity pairs between query/reference, then harmonizes their disparate scoring scales with two-stage conformal prediction:

• Pretrained cross-modal encoders provide vector-space representations.
• Similarity matrices constructed over all observed modality pairs.
• Calibration via conformal prediction first normalizes per-encoder score distributions, then fuses scores into a single unified probability of correct retrieval.
• No generative imputation or retraining required; provable minimal coverage guarantees are model-agnostic (Li et al., 2024).

c. Model-Driven Transformation via Pivot Metamodels

The ANY2ANY paradigm in constraint modeling encodes any input modeling or solver language into a carefully crafted, expressive pivot metamodel. All optimizations, refactorings (flattening, simplification, reification), and language-specific mappings are declaratively specified as concept-oriented ATL rules:

• Text/model injection to produce metamodel-compliant EMF models.
• Source → pivot and pivot → target translations via independently constructed ATL mappings.
• Refactoring steps implemented as modular ATL transformations upon the pivot (not language-pair), enabling O(N) instead of O(N²) maintenance for N languages.
• Supported by robust inject/extract bridges for arbitrary constraint languages (Chenouard et al., 2010).

d. Cross-Embodiment Transfer for Robotics

Any2Any in robotics enables efficient transfer of whole-body tracking policies from a pretrained source robot to an arbitrary new target robot, with minimal adaptation:

• Layered kinematic alignment transforms data layouts and joint conventions (via scattering, hip decoupling, and parallel joint coupling matrices).
• Lightweight dynamics adaptation uses PEFT (e.g., LoRA) in targeted neural network layers (actor/critic) with most weights frozen, enabling rapid convergent adaptation with only 1% of the original data/compute (Yang et al., 22 May 2026).
• No need to retrain policies from scratch per embodiment; systematic pipeline reduces repeated engineering and resource cost.

3. Training, Optimization, and Theoretical Guarantees

ANY2ANY frameworks formalize the separation of modality/embodiment-specific adaptation from shared semantic inference. Training typically proceeds in modular stages:

• Pretraining of modality-specific/applications-specific encoders: Ensures that all data types map compatibly into the latent or pivot space.
• Semantic or backbone module training: Learns universal salient structure with paired, transitive, or incomplete data support, sometimes leveraging large public backbones (e.g., pretrained video diffusion networks in radiotherapy planning) (Wang et al., 10 May 2026).
• Adapter or calibration tuning: Lightweight target-/task-specific regressor tuning corrects residual errors.
• Theoretical guarantees: In retrieval, model-agnostic conformal calibration enforces minimal coverage guarantees irrespective of the underlying encoder distributions (under the i.i.d. assumption) (Li et al., 2024).

4. Inference and Scalability

A common benefit is dramatic reduction in combinatorial complexity. For N supported modalities/languages/embodiments:

• Pairwise (one-to-one) frameworks require O(N²) models; Any2Any reduces this to O(1) universal core plus O(N) adapters or bridges.
• Inference cost remains bounded and uniform, with all translations routed via a shared semantic core; adapters are applied as a post-processing or calibration step.
• Zero-shot generalization: The shared semantic/pivot space allows successful inference on unseen (source, target) pairs, provided the adjacency graph of supervised data is sufficiently connected (Chen et al., 4 Mar 2026).

5. Empirical Performance and Comparative Assessment

Extensive evaluation across domains affirms robust performance:

• Remote sensing modality translation: Outperforms strong baselines (Pix2Pix, ControlNet, BBDM) in PSNR/SSIM/RMSE; maintains semantically coherent and quantitatively accurate output in both supervised and zero-shot settings (Chen et al., 4 Mar 2026).
• Multimodal retrieval: Achieves Recall@5 on par with full-modality-oracle models even with 50% modalities missing; outperforms single-pair and heuristic approaches (Li et al., 2024).
• Constraint programming: Demonstrates maintainable pipelines for multi-LLM translation, ensuring that optimizations, transformations, and verifications need not be duplicated per language-pair (Chenouard et al., 2010).
• Robotics: Adapts existing WBT models to new robots with 1% of the original data/compute while matching or surpassing specialist performance; LoRA-adapted layers suffice for dynamics gap bridging, given precise kinematic alignment (Yang et al., 22 May 2026).
• Medical imaging: Unified 3D diffusion models with Any2Any conditioning and RL-guided Scorecard post-training outperform prior state-of-the-art in dose prediction metrics, with demonstrated modular ablation-confirmed improvements (Wang et al., 10 May 2026).

6. Limitations and Future Research Directions

Limitations and open research problems for ANY2ANY paradigms include:

• Structural prerequisites: Some reliance on shared latent semantics, compatible topologies, or sufficiently expressive pivot models; highly divergent or out-of-distribution elements may require more sophisticated alignment (e.g., for non-humanoid robots or entirely new modalities).
• Adapter/Calibration Heuristics: The choice of fusion or calibration functions (mean, max, learned fusion) empirically matters but is not universally optimal; adaptivity and interpretability of these steps remain open challenges (Li et al., 2024).
• Dataset requirements: Construction of highly connected, multi-modal paired datasets at scale (e.g., RST-1M, AMASS) can be costly and domain-specific.
• Security risks: "ANY2ANY" backdoor attacks (e.g., Flareon) illustrate how arbitrary mapping principles can be weaponized, embedding flexible, stealthy backdoors robust to existing defenses without impacting clean accuracy (Qin et al., 2022).

Opportunities for extension include: direct end-to-end multi-element models, learned/calibration fusion modules, self-verifying and refining iterative pipelines, and broader applications to domains with chronic data incompleteness or cross-format translation needs (Li et al., 5 Mar 2026).

7. Domain-Specific Instantiations: Selected Frameworks

Domain / Task	ANY2ANY Instantiation	Core Reference
Remote Sensing	Latent diffusion for arbitrary modality translation with O(1) backbone and O(N) adapters	(Chen et al., 4 Mar 2026)
Multimodal Retrieval	Conformal-agnostic multi-encoder fusion for missing modalities	(Li et al., 2024)
Constraint Programming	Pivot metamodel, ATL concept-rule transformations	(Chenouard et al., 2010)
Robotics	Cross-embodiment transfer via kinematic alignment and PEFT (LoRA) adaptation	(Yang et al., 22 May 2026)
Medical Imaging	3D diffusion models with knowledge transfer and RL Scorecard guidance	(Wang et al., 10 May 2026)
Software Security	Training-agnostic, augmentation-based backdoor "any2any" attacks	(Qin et al., 2022)

These instantiations share the underlying ANY2ANY tenet: efficient, modular, and provably effective mapping between arbitrary combinations within their domain, with structural mechanisms (latent spaces, pivots, adapters, calibration, or transfer learning) supplanting combinatorially exploding pairwise translation schemes.