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Universe of Thoughts: Enabling Creative Reasoning with Large Language Models

Yuto Suzuki, Farnoush Banaei-Kashani

TL;DR

This work tackles the challenge of autonomous creative reasoning in ill-defined problems where conventional reasoning frameworks struggle. It introduces the Universe of Thoughts (UoT), a cognitive-inspired framework with three paradigms—combinational, exploratory, and transformative—that generate novel solutions by expanding and reconfiguring the search space. A modular prompt-based instantiation of UoT demonstrates superior creative performance on three open-ended tasks, validated by a novel evaluation benchmark that jointly considers feasibility, novelty, and utility. The results show that UoT can surpass several state-of-the-art reasoning methods and rival proprietary models, highlighting the practical impact of structured, creativity-focused reasoning for complex real-world problems.

Abstract

Reasoning based on Large Language Models (LLMs) has garnered increasing attention due to outstanding performance of these models in mathematical and complex logical tasks. Beginning with the Chain-of-Thought (CoT) prompting technique, numerous reasoning methods have emerged that decompose problems into smaller, sequential steps (or thoughts). However, existing reasoning models focus on conventional problem-solving and do not necessarily generate creative solutions by ``creative reasoning''. In domains where the solution space is expansive and conventional solutions are suboptimal, such as drug discovery or business strategization, creative reasoning to discover innovative solutions is crucial. To address this gap, first we introduce a computational framework for creative reasoning inspired by established cognitive science principles. With this framework, we propose three core creative reasoning paradigms, namely, \textit{combinational}, \textit{exploratory}, and \textit{transformative} reasoning, where each offers specific directions for systematic exploration of the universe of thoughts to generate creative solutions. Next, to materialize this framework using LLMs, we introduce the \textit{Universe of Thoughts} (or \textit{UoT}, for short), a novel set of methods to implement the aforementioned three creative processes. Finally, we introduce three novel tasks that necessitate creative problem-solving, along with an evaluation benchmark to assess creativity from three orthogonal perspectives: feasibility as constraint, and utility and novelty as metrics. With a comparative analysis against the state-of-the-art (SOTA) reasoning techniques as well as representative commercial models with reasoning capability, we show that UoT demonstrates superior performance in creative reasoning.

Universe of Thoughts: Enabling Creative Reasoning with Large Language Models

TL;DR

This work tackles the challenge of autonomous creative reasoning in ill-defined problems where conventional reasoning frameworks struggle. It introduces the Universe of Thoughts (UoT), a cognitive-inspired framework with three paradigms—combinational, exploratory, and transformative—that generate novel solutions by expanding and reconfiguring the search space. A modular prompt-based instantiation of UoT demonstrates superior creative performance on three open-ended tasks, validated by a novel evaluation benchmark that jointly considers feasibility, novelty, and utility. The results show that UoT can surpass several state-of-the-art reasoning methods and rival proprietary models, highlighting the practical impact of structured, creativity-focused reasoning for complex real-world problems.

Abstract

Reasoning based on Large Language Models (LLMs) has garnered increasing attention due to outstanding performance of these models in mathematical and complex logical tasks. Beginning with the Chain-of-Thought (CoT) prompting technique, numerous reasoning methods have emerged that decompose problems into smaller, sequential steps (or thoughts). However, existing reasoning models focus on conventional problem-solving and do not necessarily generate creative solutions by ``creative reasoning''. In domains where the solution space is expansive and conventional solutions are suboptimal, such as drug discovery or business strategization, creative reasoning to discover innovative solutions is crucial. To address this gap, first we introduce a computational framework for creative reasoning inspired by established cognitive science principles. With this framework, we propose three core creative reasoning paradigms, namely, \textit{combinational}, \textit{exploratory}, and \textit{transformative} reasoning, where each offers specific directions for systematic exploration of the universe of thoughts to generate creative solutions. Next, to materialize this framework using LLMs, we introduce the \textit{Universe of Thoughts} (or \textit{UoT}, for short), a novel set of methods to implement the aforementioned three creative processes. Finally, we introduce three novel tasks that necessitate creative problem-solving, along with an evaluation benchmark to assess creativity from three orthogonal perspectives: feasibility as constraint, and utility and novelty as metrics. With a comparative analysis against the state-of-the-art (SOTA) reasoning techniques as well as representative commercial models with reasoning capability, we show that UoT demonstrates superior performance in creative reasoning.

Paper Structure

This paper contains 40 sections, 35 equations, 2 figures, 4 tables.

Figures (2)

  • Figure 1: Visualization of basic analytical reasoning methods (a) versus different Universe of Thoughts creative reasoning methods (b-d). While analytical reasoning methods explore thoughts confined to the original solution space (a), C-UoT transfers and combines thoughts from analogous domains (b), E-UoT introduces novel thoughts to expand the existing problem space (c), and T-UoT alters fundamental rules to create a new, transformed solution space (d).
  • Figure 2: Sensitivity analysis for UoT