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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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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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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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KDD 20262026Individual treatment effect (ITE) estimation from observational data becomes unreliable when three challenges co-occur: extreme class imbalance (0.4% treatment rate), outcome sparsity (97.6% zeros), and pervasive cold-start (99.2% incomplete profiles). These conditions violate identifying assumptions—propensity scores collapse toward boundary values, and outcome predictions degrade for subjects with sparse
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2026Structured texts refer to texts containing structured elements beyond plain texts, such as code snippets and placeholders. Such structured texts increasingly require segmentation into semantically meaningful components, which cannot be effectively handled by conventional sentence-level segmentation methods. To address this, we propose BoundRL, a novel approach that jointly performs efficient token-level
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ICML 2026 Workshop on Scalable Learning and Optimization for Efficient Multimodal AI Agents (SCALE)2026Enterprise environments differ fundamentally from the clean settings assumed in LLM research: knowledge is distributed across heterogeneous sources, often incomplete or inconsistent, and key procedural logic is implicitly encoded in artifacts rather than explicitly documented. In such settings, retrieval-based approaches are insufficient, as no single source contains the full workflow. We propose a replication-driven
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IEEE ICMA 20262026Deploying computer vision models in Warehouse Facilities traditionally requires extensive resources for camera mounting, image collection, annotation, training, and deployment - a process often needing repetition in each new environment due to camera mounting constraints and environmental variability. This paper explores an innovative approach to streamline this process by conducting the standard procedure
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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.
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