Large Language Models (LLMs) such as GPT, Gemini, and DeepSeek have achieved remarkable performance across natural language tasks. Yet, they remain opaque black boxes, often generating outputs without transparent reasoning processes. Recent prompting methods like Chain-of-Thought (CoT) enhance interpretability, but lack mathematical rigor and structural guarantees. We propose Cognitive Calculus, a framework that formalizes reasoning as a calculus of thought—decomposing problems into multiple reasoning dimensions, extracting insights through structured analysis, and synthesizing them into a coherent answer. 1. Formalize reasoning with Cognitive Calculus (Differentiation → Integration → Summation). 2. Provide theoretical alignment with LLM architectures (attention, embedding aggregation, decoding). 3. Implement the framework in a fine-tuned “E-Model” and evaluate on reasoning benchmarks. 4. Demonstrate superior accuracy, interpretability, and robustness compared with CoT. (You can render this with TikZ in LaTeX or draw in PowerPoint/Illustrator) (Bar chart with three bars per dataset: Baseline LLM, CoT, Cognitive Calculus. Highlight Cognitive Calculus as consistently higher.) GSM8K:…
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Задача: Генерация текста
Автор: aifeifei798
Теги: qwen3, qwen, unsloth, qiming, qiming-holos, bagua, decision-making, strategic-analysis
Лайков: 3 | Загрузок: 27
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
