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June 8, 20267 min readFour approaches can dramatically improve the performance and trustworthiness of AI agents in operational environments. -
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May 26, 20265 min read
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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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ICLR 2026 Workshop on Navigating and Addressing Data Problems for Foundation Models2026Post-training Large Language Models requires diverse, high-quality data which is rare and costly to obtain, especially in low resource domains and for multiturn conversations. Common solutions are crowdsourcing or synthetic generation, but both often yield low-quality or low-diversity data. We introduce Adversarial Arena for building high quality conversational datasets by framing data generation as an
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ACL 2026 Workshop on Advances in Language and Vision Research2026Visual grounding in graphical user interface (GUI) requires accurate localization of UI elements from natural language instructions. Conventional coordinate generation approaches face inherent limitations, including sensitivity to resolution variations and lack of interpretability. Recently, coordinate-free attention-based methods have emerged as a promising alternative, but these methods primarily rely
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VLDB 20262026Compilation-based query execution produces optimized machine code per query but introduces a cold-start problem: when the compiled code is not cached, the query stalls during compilation, delaying data processing by up to orders of magnitude relative to the query's execution time. This overhead dominates short-running queries and creates latency variability for both interactive analytics and ETL pipelines
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EACL 2026 Industry Track2026Personalized shopping agents must adapt their decisions to different user personas, balancing efficiency, preference alignment, and goal success. Building upon the WebShop dataset and τ2-Bench environment, ShopperBench introduces a persona-guided benchmark for evaluating such adaptive behaviors. ShopperBench augments shopping trajectories with persona-conditioned goals, reasoning rationales, and preference
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2026We introduce SWAN (Semantic Watermarking with Abstract Meaning Representation)1 , a novel framework that embeds watermark signatures into the semantic structure of a sentence using Abstract Meaning Representation (AMR). In contrast to existing watermarking methods, which typically encode signatures by adjusting token selection preferences during text generation, SWAN embeds the signature directly in the
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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.
