News

marktechpost4d
Chain-of-Thought May Not Be a Window into AI’s Reasoning: Anthropic’s New Study Reveals Hidden Gaps
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 ...
marktechpost4d
Agentic AI in Financial Services: IBM’s Whitepaper Maps Opportunities, Risks, and Responsible Integration
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 ...
marktechpost5d
LLMs Struggle to Act on What They Know: Google DeepMind Researchers Use Reinforcement Learning Fine-Tuning to Bridge the Knowing-Doing Gap
Language models trained on vast internet-scale datasets have become prominent language understanding and generation tools. Their potential extends beyond language tasks to functioning as ...
marktechpost8d
Meet LangGraph Multi-Agent Swarm: A Python Library for Creating Swarm-Style Multi-Agent Systems Using LangGraph
LangGraph Multi-Agent Swarm is a Python library designed to orchestrate multiple AI agents as a cohesive “swarm.” It builds on LangGraph, a framework for constructing robust, stateful agent workflows, ...
marktechpost9d
Rime Introduces Arcana and Rimecaster (Open Source): Practical Voice AI Tools Built on Real-World Speech
Arcana is trained on a diverse range of conversational data collected in natural settings. This allows it to generalize across speaking styles, accents, and languages, and to perform reliably in ...
marktechpost6d
SWE-Bench Performance Reaches 50.8% Without Tool Use: A Case for Monolithic State-in-Context Agents
Recent advancements in LM agents have shown promising potential for automating intricate real-world tasks. These agents typically operate by proposing and executing actions through APIs, supporting ...
marktechpost7d
LLMs Struggle with Real Conversations: Microsoft and Salesforce Researchers Reveal a 39% Performance Drop in Multi-Turn Underspecified Tasks
Conversational artificial intelligence is centered on enabling large language models (LLMs) to engage in dynamic interactions where user needs are revealed progressively. These systems are widely ...
marktechpost5d
Reinforcement Learning Makes LLMs Search-Savvy: Ant Group Researchers Introduce SEM to Optimize Tool Usage and Reasoning Efficiency
Recent progress in LLMs has shown their potential in performing complex reasoning tasks and effectively using external tools like search engines. Despite this, teaching models to make smart decisions ...
marktechpost13d
Microsoft Researchers Introduce ARTIST: A Reinforcement Learning Framework That Equips LLMs with Agentic Reasoning and Dynamic Tool Use
LLMs have made impressive gains in complex reasoning, primarily through innovations in architecture, scale, and training approaches like RL. RL enhances LLMs by using reward signals to guide the model ...
marktechpost9d
Meta AI Introduces CATransformers: A Carbon-Aware Machine Learning Framework to Co-Optimize AI Models and Hardware for Sustainable Edge Deployment
As machine learning systems become integral to various applications, from recommendation engines to autonomous systems, there’s a growing need to address their environmental sustainability. These ...
marktechpost8d
A Step-by-Step Guide to Build an Automated Knowledge Graph Pipeline Using LangGraph and NetworkX
In this tutorial, we demonstrate how to construct an automated Knowledge Graph (KG) pipeline using LangGraph and NetworkX. The pipeline simulates a sequence of intelligent agents that collaboratively ...
marktechpost13d
ZeroSearch from Alibaba Uses Reinforcement Learning and Simulated Documents to Teach LLMs Retrieval Without Real-Time Search
Large language models are now central to various applications, from coding to academic tutoring and automated assistants. However, a critical limitation persists in how these models are designed; they ...