Target-Agnostic Transport Programs

• Target-Agnostic Transport Programs are frameworks that execute data and signal tasks without committing to a specific target, ensuring flexibility and resilience.
• They use advanced formulations such as Schroedinger bridges and RB random walks to uniformly distribute network load and optimize pathways.
• Employing adaptive, adversarial, and LP-based methods, these systems scale efficiently across diverse domains while supporting protocol-independent deployment.

Target-agnostic transport programs are algorithmic and system frameworks that execute transport, adaptation, or inference tasks over data, signals, or network flows while avoiding hard-coded commitment to a particular target endpoint, output variable, or destination. These programs are engineered for broad robustness, adaptability, and efficiency across heterogeneous domains, networks, and applications without tailoring their mechanisms to specific targets. Technical implementations span discrete-time Markov scheduling in network routing, general-purpose optimal transport, regression adaptation, event-driven protocol abstractions, and multi-target adversarial flows. Target-agnosticism increases flexibility, resilience to change and failure, and facilitates scalable deployments across evolving environments.

1. Mathematical Formulations and Theoretical Foundations

Target-agnostic transport is closely tied to maximum-entropy principles and advanced stochastic control. In robust network scheduling, the paradigm is formalized via the discrete-time Schroedinger bridge problem, where the optimal path measure $P$ is selected to minimize relative entropy $D(P\|\text{prior})$ subject to specified initial and final marginals. This yields the general solution

$$\pi_{ij}(t) = m_{ij} \cdot \frac{\varphi(t+1, j)}{\varphi(t, i)},$$

with $m_{ij}$ and scaling potentials $\varphi$ dictated by the chosen prior and boundary conditions (Chen et al., 2016).

Adaptations in optimal transport further rely on adversarial minimax games and variational KL divergence characterizations. For continuous or high-dimensional transport, the adaptive approach constructs maps $T(x) = \nabla\phi(x)$ found by solving

$$\min_\phi\,\max_g\, \mathbb{E}[g(T(X))] - \mathbb{E}[e^{g(Y)}],$$

where $g$ is a discriminator-like test function learned to expose mismatches between the source-pushed distribution and its target (Essid et al., 2018).

Recent high-efficiency linear programs in discrete optimal transport reformulate the transport task as a "positive LP" under bounded cost, enabling nearly linear running times and rapid re-computation as the target changes, which is crucial for target-agnosticity in large-scale settings (Quanrud, 2018).

2. Target-Agnostic Path Spreading over Networks

A central feature is the avoidance of mass concentration along narrowly defined routes. The Ruelle-Bowen (RB) random walk prior, which assigns equiprobable weights to all paths of a given length joining any two nodes, generates extremely robust flows. The RB law uses the Perron–Frobenius eigenvectors and spectral radius $\lambda_A$ of the adjacency matrix $A$, generating transitions

$r_{ij} = \frac{v_j}{\lambda_A v_i}, \quad \text{(when $a_{ij} = 1$)},$

and invariant measure $\nu_{\mathrm{RB}}(i) = u_i v_i$ (Chen et al., 2016). When utilized within the Schroedinger bridge formalism, the resulting transport distributes probability mass uniformly across all permitted network paths, maximizing topological entropy and mitigating congestion, bottlenecks, and vulnerability to network failures.

Under weighted network models, edge costs $U_{ij}$ are exponentiated ($B_{ij} = e^{-U_{ij}}$) before spectral analysis, producing an RB-type measure that further biases mass allocation in favor of low-cost paths while still spreading across equal-cost alternatives.

3. Adaptive, Sample-Efficient and Adversarial Methodologies

Adaptive optimal transport programs address the lack of prior knowledge and respond dynamically to observed data differences. The KL-divergence minimax characterization promotes adversarial interplay between feature construction (discrepancy detector $g$) and transport mapping. Local optimal transport problems are solved between consecutive intermediate distributions, with displacement interpolation (e.g., McCann’s theory) linking these steps to produce composite global transport maps robustly spanning disparate distributions (Essid et al., 2018). This architecture supports arbitrary complexity without dependence on target structure specifics.

Similarly, in regression adaptation without access to source data, a label density map is estimated from "confident" predictions on target data, capturing their empirical distribution; the source model is then calibrated using weighted pseudo-labels constructed from this estimated distribution. This mechanism directly accommodates target-agnostic and source-free adaptation in varied scenarios (He et al., 2023).

4. Algorithmic Structures and Practical Implementations

Target-agnostic frameworks are diverse, spanning:

• Network scheduling systems: Efficient iterative algorithms solve the Schroedinger bridge recurrences, requiring linear algebraic and recurrency computations scalable to large graphs.
• Sample-based OT solvers: Variational adversarial programs build maps and discriminators from batches of samples, enabling data-driven feature adaptation, rapid learning, and seamless composition for incremental transport tasks.
• LP-based approximations for large-scale transport: When cost matrices are bounded, nearly linear time algorithms find $\varepsilon$-uniform transportation matrices, followed by simple "oblivious repair" schemes that account for leftover mass, making the programs highly responsive to shifting targets (Quanrud, 2018).
• Self-supervised and pseudo-likelihood deep learning: Joint distributions and conditional inference models are trained to recover any subset of missing values, permitting arbitrary selection of target attributes at inference time (Jin et al., 2020).
• Sweep planners for physical manipulation: In robotic granular manipulation, planners operate over height map distributions, using OT solutions to propose sweeping actions, then reactively adjusting based on sensor feedback—all without knowledge of specific material properties (Alatur et al., 2023).

5. Robustness, Generalization, and Resilience

By construction, target-agnostic transport plans avoid over-committing to fragile or capacity-limited routes and bypass the need for detailed priors about specific target distributions or endpoints. Uniform spreading via RB walks ensures resilience against topology changes and network faults; adaptive and adversarial methods flexibly adjust to observed data anomalies, noise, and domain shifts. LP-based algorithms, self-supervised learning, and multi-stage anchor-based matching further insulate against outlier effects and failure modes (Lin et al., 2020). In experimental design, "Target Balance" allocates units so that outcomes, after importance weighting, reliably generalize to the desired population, not just the sampled group (Phan et al., 2020, Zeng et al., 2023).

Table: Key Properties of Target-Agnostic Transport Programs

Mechanism	Target Specification	Spreading/Adaptation Principle
Schroedinger bridge + RB	Endpoint marginals only	Uniform path distribution across topology
LP-based fast OT	Flexible (dynamic)	Approximate feasibility, rapid re-computation
Adaptive adversarial OT	None required	Data-driven, adversarial feature construction
Granular sweep planner	Target shape only	Physical distribution matching via reactive control
Source-free regression adaptation	Estimated label density only	Calibration via pseudo-label weighting

6. System and Layer Abstractions: Protocol Independence

In network transport, target-agnosticism also mandates protocol-agnostic operation. Transport programs may be specified in high-level event-driven domain-specific languages (DSLs) that isolate protocol logic from target execution environment details. For example, the TINF abstraction allows transport protocol logic (TCP, Homa, QUIC-Lite, etc.) to be expressed as event-chain programs, outputting "target-agnostic instructions" (such as reassembly, segmentation, scheduling, timer manipulation) for the substrate to naturally implement (Mizuno et al., 25 Sep 2025). This facilitates re-targeting to diverse execution substrates (DPDK, kernel eXpress DataPath/XDP, NIC hardware) without logic modification, supporting automated program analysis and protocol verification.

7. Applications and Wider Implications

Target-agnostic programs have broad utility:

• Network routing: Improved scalability, congestion mitigation, and failure robustness (Chen et al., 2016).
• Domain adaptation: High-efficiency adaptation to shifting or hidden target domains with privacy constraints (He et al., 2023).
• Virtual screening and molecular search: Retrieval engines trained for potency-based activity, independent of protein target (BIOPTIC), enable exhaustive, target-agnostic candidate discovery through SIMD-optimized brute-force embedding search (Vinogradov et al., 13 Jun 2024).
• Multi-target adversarial attack generation: Dual-flow frameworks produce transferable attacks capable of efficiently addressing multiple targets via cascaded diffusion-based optimization (Chen et al., 4 Feb 2025).
• Experimental and causal inference: Methods for transporting average treatment effects across populations with different covariate supports are efficiently computed without redesigning experiments for each potential target (Zeng et al., 2023).

As demands for generalization, scalability, and robustness in transport, inference, and adaptation increase, the architectural and mathematical principles established in target-agnostic transport programs provide a foundation for systematic, resilient solutions across a spectrum of scientific and engineering domains.