3 papers
cs.LG2026
Collaborative Reasoning Distillation via Cross-Feedback and Coherent Curation
Taehoon Kim, Seunggeun Cho, Dongsu Han
Reasoning capabilities are critical for advancing Large Language Models, yet current approaches either require massive computational budgets or struggle to effectively distill reas…
cs.SE2025
Comparative Analysis of Large Language Models for the Machine-Assisted Resolution of User Intentions
Justus Flerlage, Alexander Acker, Odej Kao
Large Language Models (LLMs) have emerged as transformative tools for natural language understanding and user intent resolution, enabling tasks such as translation, summarization,…
cs.AI2025
Towards Machine-Generated Code for the Resolution of User Intentions
Justus Flerlage, Ilja Behnke, Odej Kao
The growing capabilities of Artificial Intelligence (AI), particularly Large Language Models (LLMs), prompt a reassessment of the interaction mechanisms between users and their dev…