As large language model (LLM) agents gain traction across enterprise and research ecosystems, a foundational gap has emerged: communication. While agents today can autonomously reason, plan, and act, their ability to coordinate with other agents or interface with external tools…
With rising demand for AI systems that can handle tasks involving multi-step logic, mathematical proofs, and software development, researchers have turned their attention toward enhancing models’ reasoning potential. This capability, once believed to be exclusive to human intelligence, is now…
In this tutorial, we will explore how to leverage the capabilities of Fireworks AI for building intelligent, tool-enabled agents with LangChain. Starting from installing the langchain-fireworks package and configuring your Fireworks API key, we’ll set up a ChatFireworks LLM instance,…
Formal mathematical reasoning has evolved into a specialized subfield of artificial intelligence that requires strict logical consistency. Unlike informal problem solving, which allows for intuition and loosely defined heuristics, formal theorem proving relies on every step being fully described, precise,…
Meta has officially entered the standalone AI assistant arena with the launch of its new Meta AI app, unveiled at the inaugural LlamaCon developer conference on April 29, 2025. Powered by Meta’s latest large language model, Llama 4, the app…
Salesforce AI Research has outlined a comprehensive roadmap for building more intelligent, reliable, and versatile AI agents. The recent initiative focuses on addressing foundational limitations in current AI systems—particularly their inconsistent task performance, lack of robustness, and challenges in adapting…
The ambiguity in medical imaging can present major challenges for clinicians who are trying to identify disease. For instance, in a chest X-ray, pleural effusion, an abnormal buildup of fluid in the lungs, can look very much like pulmonary infiltrates,…
Despite notable advancements in large language models (LLMs), effective performance on reasoning-intensive tasks—such as mathematical problem solving, algorithmic planning, or coding—remains constrained by model size, training methodology, and inference-time capabilities. Models that perform well on general NLP benchmarks often lack…
Addressing the Challenges in Reasoning-Intensive Retrieval Despite notable progress in retrieval-augmented generation (RAG) systems, retrieving relevant information for complex, multi-step reasoning tasks remains a significant challenge. Most retrievers today are trained on datasets composed of short factual questions, which align…
In this tutorial, we will learn how to harness the power of Dappier AI, a suite of real-time search and recommendation tools, to enhance our conversational applications. By combining Dappier’s cutting-edge RealTimeSearchTool with its AIRecommendationTool, we can query the latest…
