I nf AL : Inference Time Adversarial Learning for Improving Research Ideation (2025)

Abstract: Advancements in Large Language Models (LLMs) have opened new opportunities for scientific discovery by assisting researchers in generating novel hypotheses and ideas. In this process, a major challenge is how to optimally and efficiently utilize LLMs’ parametric knowledge obtained from their pretraining process. Inspired by Generative Adversarial Networks (GANs), we propose inference time adversarial learning (termed InfAL), implemented through multi-LLM-agent interactions, to enhance research ideation. This approach optimizes the utilization of LLMs’ parametric knowledge without requiring additional model training, making adversarial learning efficient and context-driven. To evaluate the quality of generated ideas, we propose a relative quality ranking metric as a scalable alternative to human evaluation. Our results show that InfAL significantly improves idea generation, with GPT-4o achieving a 21% increase in novelty and a 322% increase in feasibility, demonstrating its transformative potential for driving innovation in scientific research.

## introduction recent breakthroughs in large language models (llms) have revealed their remarkable potential to advance scientific research, particularly through automated hypothesis generation. studies like @xcite ; @xcite demonstrate that llm-generated research ideas can approach human-level creativity, fueling significant academic interest. however, despite extensive exploration into leveraging llms-such as prompt engineering, supervised fine-tuning, and retrievalaugmented generation @xcite @xcite @xcite @xcite -a critical challenge remains: effectively optimizing the immense but underutilized parametric knowledge within llms at inference time without costly retraining. the parametric knowledge of an llm refers to internal representations and information implicitly encoded in its learned parameters during pretraining. for example, parametric knowledge allows an llm to implicitly understand fundamental concepts like gravity or biological evolution. however, tapping into precisely the right portion of this vast parametric knowledge to generate optimal answers for user queries is inherently challenging due to its implicit, distributed nature. current methods often rely heavily on extensive external knowledge bases or costly retraining, rendering them inefficient or infeasible for on-the-fly optimization. to address this gap, we propose inference time adversarial learning (infal), a novel framework inspired by generative adversarial networks (gans) @xcite . infal directly optimizes parametric knowledge utilization during inference, enabling llms to produce high-quality outputs without additional training or labeled data. unlike prompt optimization methods such as adv-icl @xcite , which narrowly focus on prompts for in-context learning, infal represents a universal, more powerful paradigm capable of enhancing end-to-end performance across diverse downstream tasks. we implement infal via a lightweight multi-llm-agent system comprising generator, optimizer, and discriminator agents that iteratively refine generated ideas. to facilitate scalable and fair evaluation of open-ended ideas, we introduce a relative quality ranking metric (denoted as q) that closely aligns with human judgment. experiments demonstrate infal's substantial impact: integrating infal with gpt-4o improves the novelty and feasibility of generated ideas by 21% and 322%, respectively. in summary, our contributions are twofold: inference-time in-context learning. recent studies explored llm inference-time learning via implicit bayesian inference @xcite , "induction heads" in transformers @xcite , and attention-based implicit meta-optimization @xcite . textgrad @xcite introduced textual gradients for inferencetime optimization. ## method in this section, we first present the theoretical framework for inference time adversarial learning, aiming at optimizing the utilization of llms' parametric knowledge to perform user-specified tasks. in addition, we describe how this can be implemented through llm-based agent interactions to enhance the generation of research ideas. following this, we introduce a relative quality rankingbased metric designed to approximate human evaluation of the generated ideas. ## experiments the primary objective of our experiments is fourfold: (1) to assess whether infal enhances the quality of research ideas generated by llms, (2) to examine how research ideas evolve and converge during the process of infal, (3) to assess infal's each component's contribution to overall performance, and (4) whether relative quality ranking given by gpt-4o aligns with human judgment. ## conclusion in this work, we formulated inference time adversarial learning and implemented it through a multi-llm-agent interaction system to enhance the scientific research ideation process. additionally, we developed a relative quality ranking metric to evaluate the generated ideas in a customizable and fair manner, serving as a proxy for human evaluation. our promising results demonstrate that inference time adversarial learning not only improves scientific ideation but also holds potential for enhancing other tasks involving user interaction with llms.