7 papers
Mental Model Management: An Operator-Based Framework for LLM Memory
Oliver Kramer
Large language models process large amounts of information but usually lack an explicit mechanism for maintaining compact and evolving conceptual representations. We introduce Ment…
Cognitive BASIC: An In-Model Interpreted Reasoning Language for LLMs
Oliver Kramer
Cognitive BASIC is a minimal, BASIC-style prompting language and in-model interpreter that structures large language model (LLM) reasoning into explicit, stepwise execution traces.…
Cognitive Prompts Using Guilford's Structure of Intellect Model
Oliver Kramer
Large language models (LLMs) demonstrate strong language generation capabilities but often struggle with structured reasoning, leading to inconsistent or suboptimal problem-solving…
Conceptual Metaphor Theory as a Prompting Paradigm for Large Language Models
Oliver Kramer
We introduce Conceptual Metaphor Theory (CMT) as a framework for enhancing large language models (LLMs) through cognitive prompting in complex reasoning tasks. CMT leverages metaph…
Unlocking Structured Thinking in Language Models with Cognitive Prompting
Oliver Kramer, Jill Baumann
We propose cognitive prompting as a novel approach to guide problem-solving in large language models (LLMs) through structured, human-like cognitive operations, such as goal clarif…
Towards Explainable Evolution Strategies with Large Language Models
Jill Baumann, Oliver Kramer
This paper introduces an approach that integrates self-adaptive Evolution Strategies (ES) with Large Language Models (LLMs) to enhance the explainability of complex optimization pr…
Large Language Models for Tuning Evolution Strategies
Oliver Kramer
Large Language Models (LLMs) exhibit world knowledge and inference capabilities, making them powerful tools for various applications. This paper proposes a feedback loop mechanism…