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This study reports a landmark evaluation of OpenAI's latest large language model, o3, on a challenging thermodynamics exam typically taken by university engineering students
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This paper presents LeanTutor, a proof-of-concept AI-based mathematical proof tutoring system that combines the language fluency of Large Language Models (LLMs) with the formal correctness guarante…
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This paper is a broad survey and implementation blueprint about the dual-use impact of LLMs and GenAI on platform integrity, cybersecurity, privacy, financial compliance, and healthcare diagnostics
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This paper studies a practical incentive problem in honeyword-based breach detection: remote monitoring of login attempts at other sites is useful for catching honeyword use, but each site bears th…
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This paper studies browser fingerprinting through the timing behavior of WebAssembly (Wasm) and JavaScript interoperability
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This paper investigates a systemic vulnerability in popular Web privacy and security tools related to their mishandling of local frames—iframes with non-URL sources such as 'about:blank'—which shou…
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MOPP is presented as an open-source, modular web platform for running online psychophysics experiments without requiring researchers to stitch together separate tools for task creation, hosting, au…
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This is a survey paper, not an empirical system paper
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EarthOL proposes a domain-restricted alternative to proof-of-work: instead of burning energy on arbitrary computation, the protocol tries to reward verifiable human contributions in bounded domains…
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This paper addresses the challenge of detecting bias in machine learning datasets without relying on sensitive demographic labels or rigid fairness metrics, which can be inaccessible or ethically p…
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This paper addresses the challenge of designing car-following models for automated vehicles that are both provably safe and exhibit human-like driving behaviors, especially under braking scenarios
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The reported outcome is a public framework plus a dataset of 14,889 synthetic identities, with evidence that the resulting identities align better with real embedding-space structure than prior syn…
