marktechpost4d
Zep AI Introduces a Smarter Memory Layer for AI Agents Outperforming the MemGPT in the Deep Memory Retrieval (DMR) Benchmark
The development of transformer-based large language models (LLMs) has significantly advanced AI-driven applications, particularly conversational agents. However, these models face inherent limitations ...
marktechpost5d
University of Bath Researchers Developed an Efficient and Stable Machine Learning Training Method for Neural ODEs with O(1) Memory Footprint
Neural Ordinary Differential Equations are significant in scientific modeling and time-series analysis where data changes every other moment. This neural network-inspired framework models ...
marktechpost5d
Top AI Coding Agents in 2025
AI-powered coding agents have significantly transformed software development in 2025, offering advanced features that enhance productivity and streamline workflows. Below is an overview of some of the ...
marktechpost6d
Transformer-Based Modulation Recognition: A New Defense Against Adversarial Attacks
The fast development of wireless communication technologies has increased the application of automatic modulation recognition (AMR) in sectors such as cognitive radio and electronic countermeasures.
marktechpost5d
Anthropic Introduces Constitutional Classifiers: A Measured AI Approach to Defending Against Universal Jailbreaks
Large language models (LLMs) have become an integral part of various applications, but they remain vulnerable to exploitation. A key concern is the emergence of universal jailbreaks—prompting ...
marktechpost7d
Creating an AI-Powered Tutor Using Vector Database and Groq for Retrieval-Augmented Generation (RAG): Step by Step Guide
Currently, three trending topics in the implementation of AI are LLMs, RAG, and Databases. These enable us to create systems that are suitable and specific to our use. This AI-powered system, ...
marktechpost4d
NYU Researchers Introduce WILDCHAT-50M: A Large-Scale Synthetic Dataset for Efficient LLM Post-Training
Large language model (LLM) post-training focuses on refining model behavior and enhancing capabilities beyond their initial training phase. It includes supervised fine-tuning (SFT) and reinforcement ...
marktechpost7d
This AI Paper from the Tsinghua University Propose T1 to Scale Reinforcement Learning by Encouraging Exploration and Understand Inference Scaling
Large language models (LLMs) are developed specifically for math, programming, and general autonomous agents and require improvement in reasoning at test time. Various approaches include producing ...
marktechpost6d
Dendritic Neural Networks: A Step Closer to Brain-Like AI
Artificial Neural Networks (ANNs) have their roots established in the inspiration developed from biological neural networks. Although highly efficient, ANNs fail to embody the neuronal structures in ...
marktechpost7d
Creating an AI Agent-Based System with LangGraph: Adding Persistence and Streaming (Step by Step Guide)
In our previous tutorial, we built an AI agent capable of answering queries by surfing the web. However, when building agents for longer-running tasks, two critical concepts come into play: ...
marktechpost6d
Bio-xLSTM: Efficient Generative Modeling, Representation Learning, and In-Context Adaptation for Biological and Chemical Sequences
Modeling biological and chemical sequences is extremely difficult mainly due to the need to handle long-range dependencies and efficient processing of large sequential data. Classical methods, ...
marktechpost5d
From Softmax to SSMax: Enhancing Attention and Key Information Retrieval in Transformers
Transformer-based language models process text by analyzing word relationships rather than reading in order. They use attention mechanisms to focus on keywords, but handling longer text is challenging ...