https://aibussin.com/tags/domain-classification/ Recent content in Domain Classification on AIBussin — AI applications, systems and books Hugo en-US Wed, 02 Jul 2025 14:15:50 +0100 https://aibussin.com/post/epistemic-engines/ Wed, 02 Jul 2025 14:15:50 +0100 https://aibussin.com/post/epistemic-engines/ <h2 id="-summary-building-the-engine-of-understanding">🔍 Summary: Building the Engine of Understanding</h2> <p>This is not a finished story. It’s the beginning of one and likely the most ambitious post we’ve written yet.</p> <p>We&rsquo;re venturing into new ground: designing epistemic engines modular, evolving AI systems that don’t just respond to prompts, but build understanding, accumulate beliefs, and refine themselves through In-Context Learning.</p> <p>In this series, we’ll construct a self-contained system separate from our core framework Stephanie that runs its own pipelines, evaluates its own beliefs, and continuously improves through repeated encounters with new data. Its core memory will be made of cartridges: scored, structured markdown artifacts distilled from documents, papers, and the web. These cartridges form a kind of belief substrate that guides the system’s judgments.</p> https://aibussin.com/post/docs/ Tue, 17 Jun 2025 23:31:13 +0100 https://aibussin.com/post/docs/ <h2 id="-summary">📖 Summary</h2> <p>Imagine drowning in a sea of research papers, each holding a fragment of the knowledge you need for your next breakthrough. How does an AI system, striving for self-improvement, navigate this information overload to find precisely what it needs? This is the core challenge our Document Intelligence pipeline addresses, transforming chaotic documents into organized, searchable knowledge.</p> <p>In this post we combine insights from <a href="https://arxiv.org/pdf/2505.21497" 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>Paper2Poster</strong>: Towards Multimodal Poster Automation from Scientific Papers </a> and <a href="https://arxiv.org/abs/2506.10952" 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>Domain2Vec</strong>: Vectorizing Datasets to Find the Optimal Data Mixture without Training </a> to build an AI document profiler that transforms unstructured papers into structured, searchable knowledge graphs.</p>