arXiv:2606.09930v1 Announce Type: cross Abstract: The boundary between program execution and gradient-based optimization has long limited the use of code itself as a learnable scientific model. We present a compiler that translates a self-hosting subset of Scheme into differentiable computation graphs for autograd backends. Because the subset can compile its own evaluator, this yields differentiable meta-circular interpretation (DMCI): a compiled Scheme interpreter executes programs supplied as data, while reverse-mode autodiff propagates gradients to continuous constants embedded in those pro
The convergence of advanced compiler design and growing demand for differentiable programming in AI research is driving innovation in making code directly learnable.
This development could fundamentally alter how AI models are built, allowing programs themselves to be optimized through gradient descent, blurring the lines between code and model.
The ability to compile and differentiate code directly opens new paradigms for AI, enabling more powerful meta-learning, program synthesis, and systems that learn to generate and optimize their own logic.
- · AI researchers
- · Machine learning framework developers
- · Software developers in AI/ML
- · Automated programming systems
- · Traditional symbolic AI approaches
- · Systems focused solely on neural network architectures
AI models will gain the ability to directly optimize programmatic structures and logic, expanding their capability beyond data-driven pattern recognition.
This could lead to more robust and interpretable AI systems, as their 'thought processes' become explicitly differentiable programs rather than opaque neural network weights.
The integration of program execution with gradient-based optimization might enable AIs to better reason about and interact with complex, rule-based environments, accelerating progress in fields like scientific discovery or complex systems control.
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Read at arXiv cs.LG