A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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This paper introduces an observability perspective on time series foundation models, proposing GIFT-... to evaluate general-purpose models and showing performance improvements over existing approaches.
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This paper explores self-supervised learning methods to obtain general-purpose feature embeddings by capturing visual invariances across domains.
Boxiu Li, Zimo Wen, Yijia Fan, et al.
Argus is a persistent, self-evolving agentic runtime that separates stable user intent from operational objectives, enabling long-horizon reasoning with fixed model weights and achieving ~78% on SWE-Bench Pro.
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LLM agents grounded in qualitative or quantitative self-reports enable general-purpose simulation of individuals across outcomes.
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MemToolAgent introduces a memory-augmented framework for tool-using agents that leverages environment and user feedback to improve general-purpose tool use and personalization.
Jeremy Yang, Noah Yonack, Kathryn Zyskowski, et al.
First large-scale field study of general-purpose AI agent adoption, usage intensity, and use cases in open-world web environments.
Riccardo Miotto, Li Li, Brian Kidd, et al.
Unsupervised deep feature learning with stacked denoising autoencoders on EHRs of ~700k patients yields a general-purpose patient representation that significantly improves predictive modeling for 78 diseases.
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NV-Embed introduces a latent attention layer and bidirectional attention to significantly boost LLM performance on general-purpose text embedding tasks.