LLMs have demonstrated exceptional performance across multiple tasks by utilizing few-shot inference, also known as in-context learning (ICL). The main problem lies in selecting the most representative demonstrations from large training datasets. Early methods selected demonstrations based on relevance using similarity scores between each example and the input question. Current methods suggest using additional selection […] The post FEEDER: A Pre-Selection Framework for Efficient Demonstration Selection in LLMs appeared first on MarkTechPost. read more

Large Language Models (LLMs) have revolutionized many areas of natural language processing, but they still face critical limitations when dealing with up-to-date facts, domain-specific information, or complex multi-hop reasoning. Retrieval-Augmented Generation (RAG) approaches aim to address these gaps by allowing language models to retrieve and integrate information from external sources. However, most existing graph-based RAG […] The post EraRAG: A Scalable, Multi-Layered Graph-Based Retrieval System for Dynamic and Growing Corpora appeared first on MarkTechPost. read more

Alibaba has introduced Qwen3-MT (qwen-mt-turbo) via Qwen API, its latest and most advanced machine translation model, designed to break language barriers with unprecedented accuracy, speed, and flexibility. Trained on trillions of multilingual tokens, Qwen3-MT supports over 92 languages—covering more than 95% of the global population. Leveraging cutting-edge architecture, reinforcement learning, and rich customization options, it delivers […] The post Alibaba Qwen Introduces Qwen3-MT: Next-Gen Multilingual Machine Translation Powered by Reinforcement Learning appeared first on MarkTechPost. read more

Existing long-CoT reasoning models have achieved state-of-the-art performance in mathematical reasoning by generating reasoning trajectories with iterative self-verification and refinement. However, open-source long-CoT models depend only on natural language reasoning traces, making them computationally expensive and prone to errors without verification mechanisms. Although tool-aided reasoning provides greater efficiency and reliability for large-scale numerical computations through […] The post DualDistill and Agentic-R1: How AI Combines Natural Language and Tool Use for Superior Math Problem Solving appeared first on MarkTechPost. read more

Artificial intelligence research is rapidly evolving beyond pattern recognition and toward systems capable of complex, human-like reasoning. The latest breakthrough in this pursuit comes from the introduction of Energy-Based Transformers (EBTs)—a family of neural architectures specifically designed to enable “System 2 Thinking” in machines without relying on domain-specific supervision or restrictive training signals. From Pattern […] The post Unsupervised System 2 Thinking: The Next Leap in Machine Learning with Energy-Based Transformers appeared first on MarkTechPost. read more

In this tutorial, we are walking through a hands-on fusion of symbolic logic and generative AI. We set up PySwip to embed a Prolog knowledge base, wrap its predicates as LangChain tools, and then wire everything into a ReAct-style agent. Along the way, we are crafting family-relationship rules, mathematical predicates like factorial, and list utilities, […] The post A Coding Guide to Build a Tool-Calling ReAct Agent Fusing Prolog Logic with Gemini and LangGraph appeared first on MarkTechPost. read more

GitHub has introduced Spark, a groundbreaking addition to its suite of developer tools, aimed at revolutionizing the way full-stack intelligent applications are built and deployed. With Spark, available in public preview for Copilot Pro+ subscribers, developers can go from idea to a fully deployed app in minutes—all using natural language prompts and without the usual […] The post GitHub Introduces Vibe Coding with Spark: Revolutionizing Intelligent App Development in a Flash appeared first on MarkTechPost. read more

Introduction Wearable devices are transforming health monitoring by enabling continuous collection of physiological and behavioral signals such as heart rate, activity, temperature, and skin conductance. However, the real-world data that these devices generate is highly prone to missingness due to sensor failures, device removal, charging, motion artifacts, battery-saving modes, and other interruptions. This presents a […] The post Google Researchers Introduced LSM-2 with Adaptive and Inherited Masking (AIM): Enabling Direct Learning from Incomplete Wearable Data appeared first on MarkTechPost. read more

Multimodal foundation models (MFMs) like GPT-4o, Gemini, and Claude have shown rapid progress recently, especially in public demos. While their language skills are well studied, their true ability to understand visual information remains unclear. Most benchmarks used today focus heavily on text-based tasks, such as VQA or classification, which often reflect language strengths more than […] The post GPT-4o Understands Text, But Does It See Clearly? A Benchmarking Study of MFMs on Vision Tasks appeared first on MarkTechPost. read more

Visual reasoning tasks challenge artificial intelligence models to interpret and process visual information using both perception and logical reasoning. These tasks span a wide range of applications, including medical diagnostics, visual math, symbolic puzzles, and image-based question answering. Success in this field requires more than object recognition—it demands dynamic adaptation, abstraction, and contextual inference. Models […] The post This AI Paper Introduces PyVision: A Python-Centric Framework Where AI Writes Tools as It Thinks appeared first on MarkTechPost. read more