AiBrain 
Here are the top 5 most relevant AI papers from arXiv week 47/2025, complete with analysis and insights.
Publications at a Glance
Mathematical Analysis of Hallucination Dynamics in Large Language Models: Uncertainty Quantification, Advanced Decoding, and Principled Mitigation Moses Kiprono | 11/19/2025
Cognitive Foundations for Reasoning and Their Manifestation in LLMs Priyanka Kargupta, Shuyue Stella Li, Haocheng Wang, Jinu Lee, Shan Chen, Orevaoghene Ahia, Dean Light, Thomas L. Griffiths, Max Kleiman-Weiner, Jiawei Han, Asli Celikyilmaz, Yulia Tsvetkov | 11/20/2025
On the Fundamental Limits of LLMs at Scale Muhammad Ahmed Mohsin, Muhammad Umer, Ahsan Bilal, Zeeshan Memon, Muhammad Ibtsaam Qadir, Sagnik Bhattacharya, Hassan Rizwan, Abhiram R. Gorle, Maahe Zehra Kazmi, Ayesha Mohsin, Muhammad Usman Rafique, Zihao He, Pulkit Mehta, Muhammad Ali Jamshed, John M. Cioffi | 11/17/2025
EntroPIC: Towards Stable Long-Term Training of LLMs via Entropy Stabilization with Proportional-Integral Control Kai Yang, Xin Xu, Yangkun Chen, Weijie Liu, Jiafei Lyu, Zichuan Lin, Deheng Ye, Saiyong Yang | 11/19/2025
GEM: Generative Entropy-Guided Preference Modeling for Few-shot Alignment of LLMs
Key Insights
The research introduces GEM, a novel generative entropy-guided preference modeling approach that enables efficient alignment of large language models (LLMs) with human preferences in low-resource, domain-specific scenarios. By utilizing a closed-loop optimization architecture and cognitive filtering, GEM allows LLMs to internalize fine-grained human cognitive signals without relying on extensive annotated data.
Potential Impact
This approach could significantly reduce the need for large-scale preference labels in specialized fields like medicine and law, making LLMs more accessible and effective in professional applications. By empowering LLMs to self-evaluate and adapt based on limited feedback, GEM may enhance their performance in tasks requiring nuanced understanding and reasoning, thereby transforming how these models are utilized in critical domains.
Mathematical Analysis of Hallucination Dynamics in Large Language Models: Uncertainty Quantification, Advanced Decoding, and Principled Mitigation
Key Insights
This research introduces a mathematically rigorous framework for understanding and mitigating hallucinations in large language models, incorporating advanced techniques from probabilistic modeling and information theory. By proposing innovative uncertainty metrics and mitigation strategies, it connects disparate advances in the field to enhance the reliability of LLM outputs.
Potential Impact
The findings could significantly improve the safety and trustworthiness of LLM applications in various domains, including education, healthcare, and customer service, where factual accuracy is paramount. By providing a systematic approach to reduce hallucinations, this research may pave the way for more robust and user-friendly AI systems, fostering greater adoption in critical applications.
Cognitive Foundations for Reasoning and Their Manifestation in LLMs
Key Insights
This research introduces a comprehensive taxonomy of cognitive elements that underlie reasoning, revealing significant structural differences between human cognitive processes and those of large language models (LLMs). The study also presents a novel fine-grained cognitive evaluation framework and a large-scale analysis of reasoning traces, highlighting the potential for improved model performance through structured reasoning guidance.
Potential Impact
By aligning LLMs more closely with cognitive science principles, this work could transform how these models are developed and deployed, leading to more human-like reasoning capabilities in AI systems. Additionally, the findings may influence future research directions, emphasizing the importance of meta-cognitive controls and prompting a shift in focus towards enhancing the cognitive foundations of AI reasoning.
On the Fundamental Limits of LLMs at Scale
Key Insights
This research introduces a rigorous theoretical framework that identifies and formalizes the fundamental limitations of scaling Large Language Models (LLMs), addressing issues such as hallucination and reasoning degradation that have previously been described only empirically. By connecting these limitations to the foundational aspects of computation and information theory, this work provides a novel synthesis that enhances our understanding of the intrinsic constraints on LLM performance.
Potential Impact
The findings could significantly reshape the development and application of LLMs by guiding researchers and practitioners towards more effective scaling strategies and mitigation techniques, thereby improving model reliability and performance. Additionally, this theoretical foundation may inform future innovations in artificial intelligence by highlighting the boundaries of what is achievable with current methodologies and inspiring new approaches to overcome these limitations.
EntroPIC: Towards Stable Long-Term Training of LLMs via Entropy Stabilization with Proportional-Integral Control
Key Insights
This research introduces EntroPIC, a novel method for stabilizing entropy in the training of large language models, addressing the challenge of maintaining effective exploration during long-term training. By using Proportional-Integral Control to dynamically adjust loss coefficients, the method ensures balanced contributions from positive and negative samples, paving the way for more robust training processes.
Potential Impact
EntroPIC could significantly enhance the performance and reliability of large language models during long-term training by preventing sub-optimal convergence, thereby improving their applicability in real-world scenarios. This innovation may lead to more capable AI systems that can adaptively explore complex environments, expanding their use cases in various domains such as conversational AI, content generation, and automated reasoning.