Boost AI Brainstorming With Static And Dynamic Personas

Hey everyone! 👋 I'm super excited to share an idea I've been tinkering with for the AI Scientist v2 project. First off, hats off to the team for creating such a cool tool! I've been experimenting with it in my spare time (you know, juggling limited resources and compute 😅), and I've come up with a concept I'm calling the Persona System with static and dynamic personas. The goal? To inject a bit more of that human research vibe into the AI's ideation process. Since Large Language Models (LLMs) are already emulating human thinking, I figured this could be a fun and effective way to add a human spark to the AI Scientist's brainstorming sessions. It's still early days, but I'm really eager to get your thoughts and see if anyone else has explored similar avenues!

The Story Behind the Persona System

When we, as humans, embark on a research journey, especially during the ideation phase, we often start with ambitious, world-changing dreams. Think about it – the initial brainstorming sessions are filled with bold ideas and boundless enthusiasm. However, as we delve deeper, as we iterate on our concepts, engage in discussions with mentors, and receive valuable feedback, our ideas begin to evolve. They become more refined, more practical, and sometimes, they undergo a complete transformation. This messy, iterative, and inherently human process is what makes research so exhilarating and rewarding. I wanted to capture this dynamic and bring that human element into the AI Scientist v2.

So, I've developed a simple yet powerful persona system designed to mimic this journey. Imagine a virtual research team, where each member plays a distinct role in shaping the ideas. It starts with a visionary who dreams big, followed by others who poke holes, challenge assumptions, and refine the concepts – essentially guiding the AI through a feedback-like loop. Given my limited time and compute resources, I've kept the implementation relatively straightforward, but I believe it serves as a solid foundation for further exploration and development. The core idea is to introduce a collaborative brainstorming environment within the AI, mirroring how human research teams operate.

Static Personas: The Trusty Research Crew

The static personas act as the bedrock of our virtual research team. They embody consistent roles, providing a stable framework for the AI to bounce ideas off. Think of them as the core members of a research group, each with their unique expertise and perspective. The Visionary Pioneer persona, for example, is the dreamer, the one who pushes for groundbreaking, paradigm-shifting ideas. This persona is all about blue-sky thinking and challenging the status quo. Then we have the Creative Strategist, who excels at finding clever and elegant solutions, weaving together different concepts to form a cohesive whole. This persona is the master of synthesis, able to see connections that others might miss. Next up is the Rigorous Reviewer, the technical expert who ensures that the ideas are not only innovative but also technically sound and feasible. This persona is the gatekeeper of realism, ensuring that the concepts are grounded in scientific principles. The Skeptical Critic plays a crucial role by questioning assumptions, identifying potential flaws, and challenging the underlying logic of the ideas. This persona is the devil's advocate, forcing the AI to rigorously defend its proposals. Finally, the Synthesis Expert steps in to tie everything together, crafting a polished and compelling proposal that effectively communicates the essence of the research. This persona is the storyteller, weaving a narrative that captivates the audience and highlights the significance of the work.

Dynamic Personas: Adapting to the Context

Moving beyond the fixed roles of static personas, the dynamic mode introduces a layer of adaptability and context-awareness. These personas are designed to evolve and adapt based on the specific context of the workshop – its research domain (such as healthcare, machine learning, or materials science) and the prevailing themes (like ethics in AI, robotics, or sustainable energy). Imagine a healthcare-focused workshop; a "Medical Innovator" persona might emerge, bringing expertise in medical technologies and patient care. Conversely, an ML conference might see the rise of a "Theoretical ML Guru," a persona deeply versed in the mathematical foundations of machine learning. This approach allows for a more tailored and nuanced brainstorming process, ensuring that the AI considers the specific challenges and opportunities within a given field. The dynamic personas essentially bring a sense of specialization to the AI's thought process, allowing it to leverage domain-specific knowledge and insights.

While the current implementation of dynamic personas is a stepping stone towards this vision, the ultimate goal is to have the AI seamlessly generate personas that are not only contextually relevant but also possess rich, character-like qualities. This would involve leveraging the power of LLMs to create personas with distinct personalities, backgrounds, and motivations, further enhancing the human-like quality of the ideation process. The dynamic personas are the chameleons of the research team, adapting to the environment and bringing the most relevant expertise to the table.

The Human Touch: Why Personas Feel Right

Considering that Large Language Models (LLMs) learn by mimicking human thought patterns (trained on vast datasets of human-generated text and code), integrating personas feels like a natural and intuitive way to infuse a human-like quality into the ideation process. The static personas provide a reliable and consistent structure, much like a familiar and trusted research team that has worked together for years. They offer a sense of stability and predictability, allowing the AI to build upon a solid foundation of diverse perspectives. On the other hand, dynamic personas bring an element of adaptability and flexibility, mirroring the way researchers pivot and adjust their thinking when tackling new projects or exploring unfamiliar domains. They ensure that the AI's brainstorming remains relevant and responsive to the ever-changing landscape of research.

The real magic, I believe, lies in the synergy between these two types of personas. By combining the stability of static roles with the adaptability of dynamic ones, we can create a virtual research environment that is both structured and flexible, fostering a more creative and robust ideation process. Furthermore, the introduction of variance in reflection personas can be a game-changer. It empowers the AI Scientist to generate ideas that start with bold and ambitious concepts, which are then gradually refined and shaped through feedback and critical analysis. This mirrors the human research process, where initial ideas are often raw and unpolished, but through iterative feedback and collaboration, they evolve into mature and well-developed proposals. It's about embracing the messiness of the creative process and allowing ideas to emerge organically.

One exciting avenue I haven't fully explored yet, primarily due to compute limitations, is generating dynamic personas using an LLM. This approach has the potential to create even richer and more character-like roles, making the virtual research team feel even more vibrant and engaging. Imagine personas with intricate backstories, personal motivations, and unique perspectives – it could truly elevate the ideation process to a new level. However, this approach comes with a trade-off: increased computational cost. Generating complex personas requires more processing power and token usage, which could potentially increase the cost per paper, especially considering the current cost of around $15–$20 per run, as highlighted in the original paper. It's a balancing act between richness and efficiency, and further exploration is needed to determine the optimal approach.

Testing the Waters: My Prototype and Initial Findings

To put these ideas into practice, I've developed a simple prototype in my fork of the AI-Scientist-v2 repository: Mirmix/AI-Scientist-v2-personas. The core components of the prototype are:

• reflection_personas.py: This module handles all the persona-related logic, including the creation, management, and interaction of both static and dynamic personas. It's the brains behind the persona system, responsible for theme extraction and domain detection for dynamic personas, ensuring that the right expertise is brought to the table at the right time.
• perform_ideation_temp_free.py: This is the updated ideation script that supports the three modes: baseline, static, and dynamic. It allows users to switch between different brainstorming approaches and experiment with the impact of personas on the generated ideas. It also includes options to save the personas used during a session for further analysis, allowing us to track their influence and refine their roles.

I've conducted some preliminary experiments with this prototype, generating ideas with different persona configurations and observing the results. Due to budget constraints (both time and compute), I've limited the experiments to 2 idea generations and 6 reflection iterations. The results, which can be found at ai_scientist/ideas, are intriguing. So far, both static and dynamic personas appear to yield more evolved and refined experimental suggestions compared to the baseline mode. This suggests that the persona system is indeed having a positive impact on the AI's ideation process, pushing it to generate more creative and well-developed ideas.

However, it's crucial to emphasize that these are just preliminary findings. More extensive experiments, with a larger number of iterations and diverse research topics, are necessary to draw definitive conclusions and gain a deeper understanding of the system's strengths and limitations. The initial results are encouraging, but we need to dive deeper to truly unlock the potential of personas in AI-driven research.

Let's Brainstorm Together!

I fully recognize that this is a nascent idea – a simple prototype born out of side-project constraints. However, I'm genuinely excited about the potential and eager to gather your insights and perspectives. Could the integration of static and dynamic personas be the key to unlocking even greater creativity and robustness in the AI Scientist's ideas? I believe so, but I'm open to all feedback and alternative viewpoints.

Here are some questions to kickstart our discussion:

• Do you believe that this human-inspired approach, mimicking the dynamics of a research team, could lead to the generation of better and more innovative research proposals? What are the potential benefits and drawbacks?
• Has anyone else experimented with similar concepts or techniques within the AI Scientist framework? What worked well? What were the challenges encountered? Sharing our experiences can help us learn from each other and avoid reinventing the wheel.
• How can we strike the right balance between the desire for richer and more complex personas (perhaps generated by LLMs) and the practical need to keep computational costs within reasonable limits? Are there alternative approaches to persona creation that could offer a good balance between quality and efficiency?

For those interested in exploring the prototype, you can find setup details in README_PERSONA.md. I'm incredibly eager to hear your feedback, your wildest ideas, and any experiences you may have had with similar approaches. Let's collaborate and transform the AI Scientist's brainstorming capabilities into something as vibrant and dynamic as a human research lab! 😄 And, apologies for any cheesiness – I'm just genuinely passionate about this topic.

Cheers, A fan