A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Nitesh V. Chawla, Paulo Benanti
Proposes a rupture test and RISE AI architecture to bound AI claims to what has been evaluated, distinguishing evidence-bounded deployment from measurement-bounded governance.
Bill Maurer, Taylor C. Nelms, Lana Swartz
This paper analyzes Bitcoin's semiotics as a 'practical materialism' that addresses privacy, liberty, and value through cryptographic protocols rather than institutions.
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This paper presents new efficient protocols for privacy-preserving machine learning for linear regression, logistic regression, and neural network training using stochastic gradient descent.
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Introduces a principled benchmark for empirically evaluating scalable oversight protocols, enabling competitive comparison of methods for supervising AI systems.
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Introduces a principled benchmark for empirically studying and competitively evaluating scalable oversight protocols.
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This paper explores scalable oversight protocols, specifically evaluating whether weak LLMs can effectively judge strong LLMs, and expresses optimism about debate as a scalable oversight method.
Kiranjit Atwal
This paper provides a brief overview of AI applications in clinical nutrition and dietetics, highlighting opportunities and risks, and emphasizes the need for standardized protocols and human oversight.
Wei Ren, Ella Atkins
This paper extends first-order consensus protocols to second-order dynamics for distributed multi-vehicle coordination, deriving necessary and sufficient conditions for consensus under unidirectional information exchange.
Pallavi Sethi, Smruti R. Sarangi
A comprehensive survey of IoT architectures, protocols, and applications with a novel taxonomy covering technologies from sensors to applications.
Rashid Ali, Nurullah Shahin, Yousaf Bin Zikria, et al.
Proposes a deep reinforcement learning (Q-learning) based resource allocation mechanism (iQRA) to optimize channel observation-based MAC protocols in dense WLANs.
Dezhang Kong, Shi Lin, Zhenhua Xu, et al.
A comprehensive survey of security risks in LLM-driven AI agent communication, proposing a three-class framework and analyzing protocols like MCP and A2A.
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This paper provides a comprehensive exploration of agentic AI systems, detailing their core architectural components, implementation strategies, and communication protocols.