AWS re:Invent 2025: Frontier AI agents replace chatbotsAI News According to AWS at this week’s re:Invent 2025, the chatbot hype cycle is effectively dead, with frontier AI agents taking their place. That is the blunt message radiating from Las Vegas this week. The industry’s obsession with chat interfaces has been replaced by a far more demanding mandate: “frontier agents” that don’t just talk, but
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According to AWS at this week’s re:Invent 2025, the chatbot hype cycle is effectively dead, with frontier AI agents taking their place. That is the blunt message radiating from Las Vegas this week. The industry’s obsession with chat interfaces has been replaced by a far more demanding mandate: “frontier agents” that don’t just talk, but
The post AWS re:Invent 2025: Frontier AI agents replace chatbots appeared first on AI News. Read More
The Best Data Scientists are Always LearningTowards Data Science Why continuous learning matters & how to come up with topics to study
The post The Best Data Scientists are Always Learning appeared first on Towards Data Science.
Why continuous learning matters & how to come up with topics to study
The post The Best Data Scientists are Always Learning appeared first on Towards Data Science. Read More
Do Labels Make AI Blind? Self-Supervision Solves the Age-Old Binding ProblemTowards Data Science A new NeurIPS 2025 paper shows how self-supervised learning imbues ViT with better image understanding than supervised learning
The post Do Labels Make AI Blind? Self-Supervision Solves the Age-Old Binding Problem appeared first on Towards Data Science.
A new NeurIPS 2025 paper shows how self-supervised learning imbues ViT with better image understanding than supervised learning
The post Do Labels Make AI Blind? Self-Supervision Solves the Age-Old Binding Problem appeared first on Towards Data Science. Read More
The Machine Learning “Advent Calendar” Day 4: k-Means in ExcelTowards Data Science How to implement a training algorithm that finally looks like “real” machine learning
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How to implement a training algorithm that finally looks like “real” machine learning
The post The Machine Learning “Advent Calendar” Day 4: k-Means in Excel appeared first on Towards Data Science. Read More
Engineering more resilient crops for a warming climateGoogle DeepMind News Scientists are using AlphaFold to strengthen a photosynthesis enzyme for resilient, heat-tolerant crops.
Scientists are using AlphaFold to strengthen a photosynthesis enzyme for resilient, heat-tolerant crops. Read More
Emergent Introspective Awareness in Large Language ModelsKDnuggets An overview, summary, and position of cutting-edge research conducted on the emergent topic of LLM introspection on self internal states
An overview, summary, and position of cutting-edge research conducted on the emergent topic of LLM introspection on self internal states Read More
Build and Deploy Your First Supply Chain App in 20 MinutesTowards Data Science A factory operator that discovered happiness by switching from notebook to streamlit – (Image Generated with GPT-5.1 by Samir Saci)
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A factory operator that discovered happiness by switching from notebook to streamlit – (Image Generated with GPT-5.1 by Samir Saci)
The post Build and Deploy Your First Supply Chain App in 20 Minutes appeared first on Towards Data Science. Read More
On the Temporal Question-Answering Capabilities of Large Language Models Over Anonymized Datacs.AI updates on arXiv.org arXiv:2504.07646v2 Announce Type: replace-cross
Abstract: The applicability of Large Language Models (LLMs) in temporal reasoning tasks over data that is not present during training is still a field that remains to be explored. In this paper we work on this topic, focusing on structured and semi-structured anonymized data. We not only develop a direct LLM pipeline, but also compare various methodologies and conduct an in-depth analysis. We identified and examined seventeen common temporal reasoning tasks in natural language, focusing on their algorithmic components. To assess LLM performance, we created the textit{Reasoning and Answering Temporal Ability} dataset (RATA), featuring semi-structured anonymized data to ensure reliance on reasoning rather than on prior knowledge. We compared several methodologies, involving SoTA techniques such as Tree-of-Thought, self-reflexion and code execution, tuned specifically for this scenario. Our results suggest that achieving scalable and reliable solutions requires more than just standalone LLMs, highlighting the need for integrated approaches.
arXiv:2504.07646v2 Announce Type: replace-cross
Abstract: The applicability of Large Language Models (LLMs) in temporal reasoning tasks over data that is not present during training is still a field that remains to be explored. In this paper we work on this topic, focusing on structured and semi-structured anonymized data. We not only develop a direct LLM pipeline, but also compare various methodologies and conduct an in-depth analysis. We identified and examined seventeen common temporal reasoning tasks in natural language, focusing on their algorithmic components. To assess LLM performance, we created the textit{Reasoning and Answering Temporal Ability} dataset (RATA), featuring semi-structured anonymized data to ensure reliance on reasoning rather than on prior knowledge. We compared several methodologies, involving SoTA techniques such as Tree-of-Thought, self-reflexion and code execution, tuned specifically for this scenario. Our results suggest that achieving scalable and reliable solutions requires more than just standalone LLMs, highlighting the need for integrated approaches. Read More
CoT-X: An Adaptive Framework for Cross-Model Chain-of-Thought Transfer and Optimizationcs.AI updates on arXiv.org arXiv:2511.05747v2 Announce Type: replace
Abstract: Chain-of-Thought (CoT) reasoning enhances the problem-solving ability of large language models (LLMs) but leads to substantial inference overhead, limiting deployment in resource-constrained settings. This paper investigates efficient CoT transfer across models of different scales and architectures through an adaptive reasoning summarization framework. The proposed method compresses reasoning traces via semantic segmentation with importance scoring, budget-aware dynamic compression, and coherence reconstruction, preserving critical reasoning steps while significantly reducing token usage. Experiments on 7{,}501 medical examination questions across 10 specialties show up to 40% higher accuracy than truncation under the same token budgets. Evaluations on 64 model pairs from eight LLMs (1.5B-32B parameters, including DeepSeek-R1 and Qwen3) confirm strong cross-model transferability. Furthermore, a Gaussian Process-based Bayesian optimization module reduces evaluation cost by 84% and reveals a power-law relationship between model size and cross-domain robustness. These results demonstrate that reasoning summarization provides a practical path toward efficient CoT transfer, enabling advanced reasoning under tight computational constraints. Code will be released upon publication.
arXiv:2511.05747v2 Announce Type: replace
Abstract: Chain-of-Thought (CoT) reasoning enhances the problem-solving ability of large language models (LLMs) but leads to substantial inference overhead, limiting deployment in resource-constrained settings. This paper investigates efficient CoT transfer across models of different scales and architectures through an adaptive reasoning summarization framework. The proposed method compresses reasoning traces via semantic segmentation with importance scoring, budget-aware dynamic compression, and coherence reconstruction, preserving critical reasoning steps while significantly reducing token usage. Experiments on 7{,}501 medical examination questions across 10 specialties show up to 40% higher accuracy than truncation under the same token budgets. Evaluations on 64 model pairs from eight LLMs (1.5B-32B parameters, including DeepSeek-R1 and Qwen3) confirm strong cross-model transferability. Furthermore, a Gaussian Process-based Bayesian optimization module reduces evaluation cost by 84% and reveals a power-law relationship between model size and cross-domain robustness. These results demonstrate that reasoning summarization provides a practical path toward efficient CoT transfer, enabling advanced reasoning under tight computational constraints. Code will be released upon publication. Read More
AI in manufacturing set to unleash new era of profitAI News Manufacturing executives are wagering nearly half their modernisation budgets on AI, betting these systems will boost profit within two years. This aggressive capital allocation marks a definitive pivot. AI is now seen as the primary engine for financial performance. According to the Future-Ready Manufacturing Study 2025 by Tata Consultancy Services (TCS) and AWS, 88 percent
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Manufacturing executives are wagering nearly half their modernisation budgets on AI, betting these systems will boost profit within two years. This aggressive capital allocation marks a definitive pivot. AI is now seen as the primary engine for financial performance. According to the Future-Ready Manufacturing Study 2025 by Tata Consultancy Services (TCS) and AWS, 88 percent
The post AI in manufacturing set to unleash new era of profit appeared first on AI News. Read More