AI Frontiers
News & features
Research Focus: Week of May 7, 2025
In this issue: New research on compound AI systems and causal verification of the Confidential Consortium Framework; release of Phi-4-reasoning; enriching tabular data with semantic structure, and more.
Understanding and measuring the potential of inference-time scaling for reasoning. The new Eureka study tests nine state-of-the-art models on eight diverse reasoning tasks.
In the news | VentureBeat
When AI reasoning goes wrong: Microsoft Research shows more tokens can mean more problems
Large language models (LLMs) are increasingly capable of complex reasoning through “inference-time scaling,” a set of techniques that allocate more computational resources during inference to generate answers. However, a new study from Microsoft Research reveals that the effectiveness of these…
AutoGen v0.4: Reimagining the foundation of agentic AI for scale, extensibility, and robustness
Gagan Bansal introduces a transformative update to the AutoGen framework that builds on user feedback and redefines modularity, stability, and flexibility to empower the next generation of agentic AI research and applications.
Belief state transformers
John Langford talks about a new transformer architecture that generates compact belief states for goal-conditioned planning, enhancing planning algorithms’ efficiency and effectiveness.
In the news | TheSequence
One of the Best Agent Frameworks in the Market Just Got Way Better
AutoGen has undergone significant evolution since its inception, driven by the need for more efficient, flexible, and scalable agentic AI systems. The release of AutoGen v0.4 introduces a fundamental architectural shift, addressing prior inefficiencies and enhancing its capabilities.
Yadong Lu, Senior Researcher; Thomas Dhome-Casanova (opens in new tab), Software Engineer; Jianwei Yang, Principal Researcher; Ahmed Awadallah, Partner Research Manager Graphic User interface (GUI) automation requires agents with the ability to understand and interact with user screens. However, using…
AutoGen v0.4: Reimagining the foundation of agentic AI for scale, extensibility, and robustness
| Adam Fourney, Ahmed Awadallah, Cheng Tan, Erkang (Eric) Zhu, Friederike Niedtner, Gagan Bansal, Jack Gerrits, Jacob Alber, Peter Chang, Rafah Hosn, Ricky Loynd, Saleema Amershi, Victor Dibia, XiaoYun Zhang, Li Jiang, Ryan Sweet, Leonardo Pinheiro, Mohammad Mazraeh, Gerardo Moreno Zizumbo, Kosta Petan, Aamir Jawaid, Reuben Bond, Diego Colombo, and Hussein Mozannar
Announcing AutoGen 0.4, fully reimagined library for building advanced agentic AI systems, developed to improve code quality and robustness. Its asynchronous, event-driven architecture is designed to support dynamic, scalable workflows.
Research Focus: Week of December 16, 2024
NeoMem: hardware/software co-design for CXL-native memory tiering; Chimera: accurate retrosynthesis prediction by ensembling models with diverse inductive biases; GA4GH task execution API enables multicloud task execution.