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This paper addresses the problem that current Generative Engine Optimization (GEO) methods focus on optimizing individual webpages to increase visibility or influence in generative search, while ig…
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This paper addresses the limitation of existing protein structure tokenizers (PSTs) that only capture static local geometry but fail to encode protein dynamics reflected by conformational ensembles
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EVA-Bench addresses critical gaps in evaluating voice agents by providing a comprehensive end-to-end framework that simultaneously tackles the challenges of realistic multi-turn conversation simula…
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This paper addresses crucial practical challenges in offline active learning for machine-learning interatomic potentials (MLIPs): scalability to large unlabeled candidate pools (~200k+ structures),…
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This paper addresses the challenge of identifying low-surface-brightness galaxies (LSBGs) across heterogeneous astronomical imaging surveys, a key problem for large upcoming projects like LSST and …
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This paper addresses the challenge of identifying which web scrapers feed data to large language models (LLMs) during real-time content retrieval, a poorly understood and important problem for webs…
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This paper addresses a critical problem in the evaluation of AI and machine learning models: the reproducibility crisis driven by unreliable and non-repeatable human-annotated evaluations
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This study addresses the difficult problem of early antepartum prediction of pregnancy-associated thrombotic microangiopathy (P-TMA), a rare but life-threatening syndrome characterized by microvasc…
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Learning accurate internal world models under partial observability is crucial for agents operating in complex environments, but classical methods for POMDP model induction demand extensive environ…
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LEXI-SG tackles the challenge of constructing accurate, scalable 3D scene graphs from monocular RGB input alone, removing the reliance on depth sensors or ground-truth poses common in prior methods
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This study undertakes a comprehensive replication and extension of existing toxicity mitigation techniques for large language models (LLMs), specifically focusing on the inference-time method DExperts
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This paper addresses the challenge of moderating multi-agent systems where malicious agents hide adversarial intent behind superficially benign content, defeating traditional content-based moderation
