arXiv:2606.18923v1 Announce Type: new Abstract: Programmability is a missing first-class interface in fixed-tensor neural networks: editing a relation, freezing a subgraph, auditing a local function, or changing the execution backend should be an operation on the neural program rather than ad-hoc parameter surgery. GrapNet studies this graph-as-network setting. The graph is the architecture and executable program, not an input data graph. Each compute node owns its next-layer child references and a trainable allocation vector aligned with those references; deleting a relation physically remove
The increasing complexity and opacity of fixed-tensor neural networks are driving research into programmable and dynamic architectures to address current limitations in adaptability and auditability.
This research introduces a novel approach to neural network architecture, potentially enabling more flexible, interpretable, and efficient AI systems, crucial for advanced AI development and deployment across various sectors.
Neural networks could evolve from fixed-tensor models to dynamic, graph-based architectures where programmability is a core feature, allowing on-the-fly modification and auditing of neural programs.
- · AI researchers and developers
- · Companies seeking explainable and auditable AI
- · Hardware developers for dynamic AI systems
- · Developers solely focused on fixed-parameter models
- · Systems unprepared for dynamic architectural changes
First, AI systems become significantly more adaptable and easier to debug, accelerating AI development cycles.
Second, this could foster entirely new paradigms in AI deployment, particularly in sensitive or safety-critical applications requiring transparency.
Third, it might lead to a re-evaluation of current AI hardware designs, pushing for more flexible compute substrates tailored for dynamic graph architectures.
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Read at arXiv cs.LG