LLMs help robots understand vague instructions and focus on key details
To help robots do chores in places like homes and factories, a new approach from MIT uses one language model to clarify users’ instructions, then another to ignore irrelevant info.
More from the blog
Giving AI a “crystal clear” view of the materials world
New research can help AI models understand crystalline materials in 3D and reason about their properties.
Proof-based general-purpose computing with Magnetar
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
How artificial neural networks build their own learning machinery
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
Gemini 4 Argon for Robotics
Discover Gemini 4 Argon for robotics: leading the ERQA benchmark with physical reasoning, zero-shot control, Real2Sim, and agentic workflows.
Related papers
“Failures” to be celebrated: an analysis of major pivots of software startups
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
AI-powered Code Review with LLMs: Early Results
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Are Happy Developers more Productive? The Correlation of Affective States of Software Developers and their self-assessed Productivity
An empirical study on the impact of affective states on software developers’ performance while programming and the value of applying psychometrics in Software Engineering studies is demonstrated and a call to valorize the human, individualized aspects of software developers is echoed.