https://aibussin.com/tags/adaptive-ai/Recent content in Adaptive AI on AIBussin — AI applications, systems and booksHugoen-USMon, 23 Jun 2025 11:10:59 +0100https://aibussin.com/post/thoughts/Mon, 23 Jun 2025 11:10:59 +0100https://aibussin.com/post/thoughts/<blockquote> <p>How a self-evolving AI learns to reflect, score, and rewrite its own reasoning</p> </blockquote> <h2 id="-summary">🧪 Summary</h2> <p>What if an AI could think not just solve problems, but reevaluate its beliefs in the face of new information?</p> <p>In this post, we introduce a system that does exactly that. At the core of our pipeline is a lightweight scoring model called MR.Q, responsible for evaluating ideas and choosing the best ones. But when it encounters a new domain, a new goal, or a shift in task format, it doesn’t freeze it adapts.</p>https://aibussin.com/post/symbolic/Wed, 04 Jun 2025 20:57:20 +0100https://aibussin.com/post/symbolic/<h2 id="-summary">🧪 Summary</h2> <p>&ldquo;What if AI systems could learn how to improve themselves not just at the level of weights or prompts, but at the level of strategy itself? In this post, we show how to build such a system, powered by symbolic rules and reflection.</p> <p>The paper <a href="https://arxiv.org/pdf/2406.18532v1" target="_blank" class="paper-badge" style="display: inline-block; padding: 6px 10px; background: #f3f4f6; border-left: 4px solid #3b82f6; border-radius: 4px; margin: 4px 0; text-decoration: none; color: #1f2937;"> <strong>Symbolic Agents</strong>: Symbolic Learning Enables Self-Evolving Agents </a> introduces a framework where <strong>symbolic rules</strong> guide, evaluate, and evolve agent behavior.</p>