https://aibussin.com/tags/multi-dimensional-scoring/ Recent content in Multi-Dimensional Scoring on AIBussin — AI applications, systems and books Hugo en-US Sun, 27 Jul 2025 22:23:45 +0100 https://aibussin.com/post/gild/ Sun, 27 Jul 2025 22:23:45 +0100 https://aibussin.com/post/gild/ <h2 id="-introduction-the-dawn-of-self-reflective-ai">🌅 Introduction: The Dawn of Self-Reflective AI</h2> <p>What if your AI could not only answer questions but also question <em>itself</em> about those answers? Not with programmed doubt, but with genuine self-awareness recognizing when it&rsquo;s uncertain, analyzing why it made a mistake, and systematically improving its own reasoning process? This isn&rsquo;t science fiction. Today, we&rsquo;re unveiling the first working implementation of an AI that doesn&rsquo;t just think, but <em>learns how to think better</em>. It&rsquo;s a bit cold here</p> https://aibussin.com/post/lats/ Thu, 12 Jun 2025 09:23:46 +0100 https://aibussin.com/post/lats/ <h2 id="-summary">📖 Summary</h2> <p>In this post, we introduce the LATSAgent, an implementation of <a href="https://arxiv.org/pdf/2310.04406" 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>LATS</strong>: Language Agent Tree Search Unifies Reasoning.. </a> within the <a href="https://github.com/ernanhughes/co-ai">stephanie</a> framework. Unlike prior agents that followed a single reasoning chain, this agent explores multiple reasoning paths in parallel, evaluates them using multidimensional scoring, and learns symbolic refinements over time. This is our most complete integration yet of search, simulation, scoring, and symbolic tuning bringing together all of our previous work on sharpening, pipeline reflection, and symbolic rules into a unified, intelligent reasoning loop.</p>