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July 10, 20265 min readHydroShear, a new physics-based simulator, teaches robots how to use their sense of touch to perform complex manipulation tasks, in a way that transfers seamlessly to the real world. -
July 9, 202610 min read -
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Featured news
Hosted by Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features researchers tackling the hardest problems in agentic AI — from building reliable perception systems to designing training environments that mirror human learning.
The Antibody Developability Benchmark is powered by one of the most diverse antibody datasets in public literature, enabling transparent performance evaluation for AI-guided antibody design.
Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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CLeaR 20262026In this paper we show how to exploit interventional data to acquire the joint conditional distribution of all the variables using the Maximum Entropy principle. To this end, we extend the Causal Maximum Entropy method to make use of data arising from identifiable interventional distributions in addition to data from the observational distribution. Using Lagrange duality, we prove that the solution to the
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ICSE 20262026Large Language Models (LLMs) are increasingly integrated into software systems as automated decision-making components. These systems rely on instruction prompts written in natural language to encode complex workflows. However, debugging these prompts when LLMs produce undesired outputs remains challenging due to their black-box nature and the impracticality of manually inspecting large, complex inputs.
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CSER 20262026Construction management systems require realistic test data capturing complex stakeholder interactions and temporal dependencies, yet accessing real project data remains challenging due to privacy constraints and proprietary information protection. This research addresses a critical systems engineering challenge by introducing agentic simulacra patterns that leverage multi-agent coordination to generate
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2026Multi-Agent Debate (MAD) frameworks improve factual reliability in large language models (LLMs) by allowing agents to critique and refine one another's reasoning. Yet, existing MAD systems are computationally expensive and prone to degradation under prolonged debates due to redundant exchanges and unstable judging. We propose a lightweight, industry-deployable alternative that unifies Selective Debate Initiation
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ICLR 2026 Workshop on Lifelong Agents2026For large language models deployed through black-box APIs, recurring inference costs often dominate one-time training costs, motivating composed agentic systems that amortize expensive reasoning into reusable intermediate representations. We study a broad class of such systems, termed Guide–Core Policies (GCOP), in which a guide model generates a structured strategy that is executed by a black-box core
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Gretchen Ertl
The program offers unrestricted funds and other resources to support research at academic institutions and non-profit organizations in areas that align with our mission.
A global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and large language model coding security.
Credit: Wolfram Scheible
We partner with particular academic organizations across the world for deep and sustained collaborations in multiple research areas of mutual interest.
Courtesy of Pai-Ling Yin
We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
