One Task at a Time: Task-Level Adaptation for Inductive Program Synthesis
S.M. Rasing (TU Delft - Electrical Engineering, Mathematics and Computer Science)
S. Dumančić – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
T.R. Hinnerichs – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
S. Proksch – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
Existing Inductive Program Synthesis (IPS) techniques generate programs from
examples and are restricted to specific task classes to exploit domain knowledge.
To compete with general-purpose LLM-based code generation, a single IPS system
must handle tasks with different types of inputs, outputs, and instructions without
relying on domain-specific knowledge. This thesis introduces task-level adapta-
tion, a framework in which a synthesizer must learn how to search only from the in-
dividual task it is solving rather than from a predefined domain. We focus specific-
ally on search guided by automatically generated properties that capture useful
aspects of a program’s output. We introduce PHALCON to demonstrate the frame-
work’s viability. PHALCON repeatedly samples programs, selects properties that
distinguish incorrect outputs from intended behavior, and uses them to constrain
subsequent sampling. PHALCON outperforms state-of-the-art techniques special-
ized for string and bit-vector transformations, and solves tasks from the challenging
Abstraction and Reasoning Corpus. These results establish task-level adaptation as
a promising foundation for efficient, general program synthesis.