LLM-based agents are increasingly used across various applications because they handle complex tasks and assume multiple roles. A key component of these agents is memory, which stores and recalls information, reflects on past knowledge, and makes informed decisions. Memory plays…
Language models (LMs) have great capabilities as in-context learners when pretrained on vast internet text corpora, allowing them to generalize effectively from just a few task examples. However, fine-tuning these models for downstream tasks presents significant challenges. While fine-tuning requires…
Fine-tuning LLMs often requires extensive resources, time, and memory, challenges that can hinder rapid experimentation and deployment. Unsloth AI revolutionizes this process by enabling fast, efficient fine-tuning state-of-the-art models like Qwen3-14B with minimal GPU memory, leveraging advanced techniques such as…
Meta has introduced KernelLLM, an 8-billion-parameter language model fine-tuned from Llama 3.1 Instruct, aimed at automating the translation of PyTorch modules into efficient Triton GPU kernels. This initiative seeks to lower the barriers to GPU programming by simplifying kernel development…
Google has officially rolled out the NotebookLM mobile app, extending its AI-powered research assistant to Android devices. The app aims to bring personalized learning and content synthesis directly to users’ pockets by introducing new features that combine mobility, context-awareness, and…
While RAG enables responses without extensive model retraining, current evaluation frameworks focus on accuracy and relevance for answerable questions, neglecting the crucial ability to reject unsuitable or unanswerable requests. This creates high risks in real-world applications where inappropriate responses can…
The ability to search high-dimensional vector representations has become a core requirement for modern data systems. These vector representations, generated by deep learning models, encapsulate data’s semantic and contextual meanings. This enables systems to retrieve results not based on exact…
Chain-of-thought (CoT) prompting has become a popular method for improving and interpreting the reasoning processes of large language models (LLMs). The idea is simple: if a model explains its answer step-by-step, then those steps should give us some insight into…
As autonomous AI agents move from theory into implementation, their impact on the financial services sector is becoming tangible. A recent whitepaper from IBM Consulting, titled “Agentic AI in Financial Services: Opportunities, Risks, and Responsible Implementation”, outlines how these AI…
Recent developments have shown that RL can significantly enhance the reasoning abilities of LLMs. Building on this progress, the study aims to improve Audio LLMs—models that process audio and text to perform tasks like question answering. The MMAU benchmark is…
