Two Black Boxes, One Solver: Encoder Probing and Decoder Attribution for Neural Multi-Attribute VRP under Hard-Mask and Recourse Decoders
arXiv:2607.04487v1 Announce Type: cross Abstract: Neural autoregressive solvers for the Multi-Attribute Vehicle Routing Problem (MAVRP) reach competitive cost but offer no per-step justification, a problem when dispatchers must validate, accept, or compare them. We open two complementary black boxes in one protocol. On the encoder side, linear probes, spontaneous-organization metrics, rank-based richness measures, and discovered-direction analyses with intervention validation characterize how the latent represents constraint families at the graph, node, and edge level. On the decoder side, thr
The growing adoption of AI in critical operational domains necessitates greater transparency and justification for its decisions, driving research into interpretability methods for complex neural solvers.
This research provides a methodology to understand the internal workings of AI solvers for critical problems like vehicle routing, enabling greater trust, validation, and integration into real-world logistics and dispatch systems.
The ability to probe encoders and attribute decoder decisions will transform black-box AI models into more transparent and auditable tools, significantly impacting their deployability in sensitive applications.
- · Logistics and supply chain companies
- · AI interpretability researchers
- · Developers of robust AI systems
- · Regulators and auditing bodies
- · Developers of opaque AI models
- · Companies reliant on AI without explainability
Increased adoption of neural network-based VRP solutions due to enhanced explainability.
Development of a new generation of 'explainable AI' tools specifically for combinatorial optimization problems.
Potential for AI to make legally binding decisions in critical infrastructure, backed by transparent justifications.
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Read at arXiv cs.AI