Beyond Either-Or Reasoning: Transduction and Induction as Cooperative Problem-Solving Paradigms

TL;DR

This paper introduces a cooperative approach combining transduction and induction for programming-by-example, improving reasoning capabilities and performance over existing hierarchical models by interleaving both modes.

Contribution

It proposes a novel cooperative problem-solving paradigm that interleaves transductive and inductive reasoning, moving beyond hierarchical models to enhance reasoning and accuracy.

Findings

TIIPS outperforms state-of-the-art baselines across three PBE domains.

Generated programs more closely match ground-truth in syntax and semantics.

Cooperative reasoning enhances the reasoning capacity and accuracy of program synthesis.

Abstract

Traditionally, in Programming-by-example (PBE) the goal is to synthesize a program from a small set of input-output examples. Lately, PBE has gained traction as a few-shot reasoning benchmark, relaxing the requirement to produce a program artifact altogether which allows transductive methods to directly the missing output sample. Transduction and induction are complementary reasoning modes--where induction derives general rules from examples, transduction leverages the examples directly to infer specific outputs without intermediate generalization. Yet existing approaches either treat them as mutually exclusive or couple them in hybrid structures where one paradigm dictates a fixed trajectory for the other -- undermining the latter's reasoning potential and creating cascading errors. We move away from these hierarchical models and introduce cooperative transductive-inductive problem solving: by interleaving both reasoning modes and ensuring neither unconditionally dominates the other, we preserve the search autonomy and reasoning capacity of each paradigm. We instantiate this concept in TIIPS. Across three PBE domains, TIIPS consistently outperforms state-of-the-art baselines and generates programs that more closely mirror ground-truth trajectories in both syntax and semantics, indicating a better match to the intended program behavior. Our findings highlight cooperative reasoning as a promising new direction for harnessing the full power of symbolic, inductive and neural, transductive reasoning.