An AI-Based Approach to Early Reporting and Justice Initiation in Image-based Sexual Abuse. A Pilot Study
研究利用AI技术起草基于图像的性虐待初步报告，以提高报案率和司法介入效率，通过专家测试验证方法有效性。
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What out-of-the-box LLMs can(t) do in law? A Turing test in Italian exams for lawyers, judges and notaries
This study presents the first systematic Turing test of off-the-shelf large language models (LLMs) on authentic legal professional examinations—specifically those for attorneys, judges, and notaries—without any task-specific fine-tuning, to delineate the boundaries of their legal reasoning and practical competence. Employing a blind evaluation protocol, expert graders assessed complete model-generated responses against official scoring rubrics under anonymized conditions. Results reveal that certain LLMs match or even surpass top human candidates in adversarial argumentation and doctrinal analysis, yet consistently fail in the notary examination, which imposes stringent formal and substantive constraints. These findings demonstrate that LLMs’ legal capabilities are highly task-dependent and provide an empirical foundation and methodological framework for evaluating the applicability of general-purpose LLMs in high-stakes professional domains.
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Exploratory Responsiveness and Adaptive Rigidity under AI-Assisted Optimization
This study investigates how predictive AI influences the long-term adaptive capacity of socio-technical systems, with particular attention to its role in either suppressing or enhancing exploratory responses. By developing a dynamic theoretical framework that integrates cognitive, institutional, and technological dimensions, the work formalizes the co-evolutionary dynamics of human–AI interaction within rugged, multistable cognitive landscapes, positioning “adaptive responsiveness” as the central state variable. The research introduces an “effective substitution parameter” to demonstrate that AI’s impact on exploration is contingent upon a system’s pre-existing exploratory capabilities, thereby transcending technological determinism. Furthermore, it identifies the dynamical conditions leading to “exploration collapse” and delineates institutional arrangements and interaction architectures that foster AI-augmented exploratory mobility.
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Predictive Assistance and the Temporal Dynamics of Exploratory Compression
This study investigates how predictive artificial intelligence prematurely stabilizes decision trajectories before humans complete autonomous exploration, thereby suppressing cognitive exploration and the development of representational structures. By constructing a geometric dynamical framework, the work characterizes the evolution of attention in policy space as jointly driven by stable drift, endogenous exploratory perturbations, and response-gated learning, modeling predictive assistance as an exogenous mechanism that compresses exploration. The research uncovers three key mechanisms: predictive assistance reduces exploratory responsiveness, asymmetric accumulation of policy-space curvature induces hysteresis in recovery, and early intervention severely constrains subsequent exploration breadth. It further proposes a testable exploration entropy metric and predictions for premature convergence. Simulations demonstrate that sustained prediction attenuates endogenous perturbation effects, delays the restoration of exploratory capacity upon withdrawal, and that intervention timing critically shapes cognitive developmental trajectories.
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Optimal Market Composition In Monopoly Screening
This study investigates how an upstream monopolist selects the distribution of buyer valuations to optimally trade off consumer surplus against seller profit. Employing mechanism design theory, variational analysis, and optimal control methods, the paper characterizes—for the first time—the efficiency frontier between these two objectives and uncovers a phase transition in market structure as the relative weight on profit varies. When the profit weight is at least as large as that on consumer surplus, the optimal valuation distribution collapses to a degenerate point mass at the highest type. Conversely, when consumer surplus receives greater weight, no types are excluded or clustered at interior points; higher emphasis on consumer surplus yields greater market heterogeneity but lower total surplus. Under mild curvature conditions, this solution is unique.
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An AI-Based Approach to Early Reporting and Justice Initiation in Image-based Sexual Abuse. A Pilot Study
研究利用AI技术起草基于图像的性虐待初步报告，以提高报案率和司法介入效率，通过专家测试验证方法有效性。
0 citationsRead paper
What out-of-the-box LLMs can(t) do in law? A Turing test in Italian exams for lawyers, judges and notaries
This study presents the first systematic Turing test of off-the-shelf large language models (LLMs) on authentic legal professional examinations—specifically those for attorneys, judges, and notaries—without any task-specific fine-tuning, to delineate the boundaries of their legal reasoning and practical competence. Employing a blind evaluation protocol, expert graders assessed complete model-generated responses against official scoring rubrics under anonymized conditions. Results reveal that certain LLMs match or even surpass top human candidates in adversarial argumentation and doctrinal analysis, yet consistently fail in the notary examination, which imposes stringent formal and substantive constraints. These findings demonstrate that LLMs’ legal capabilities are highly task-dependent and provide an empirical foundation and methodological framework for evaluating the applicability of general-purpose LLMs in high-stakes professional domains.
0 citationsRead paper
Exploratory Responsiveness and Adaptive Rigidity under AI-Assisted Optimization
This study investigates how predictive AI influences the long-term adaptive capacity of socio-technical systems, with particular attention to its role in either suppressing or enhancing exploratory responses. By developing a dynamic theoretical framework that integrates cognitive, institutional, and technological dimensions, the work formalizes the co-evolutionary dynamics of human–AI interaction within rugged, multistable cognitive landscapes, positioning “adaptive responsiveness” as the central state variable. The research introduces an “effective substitution parameter” to demonstrate that AI’s impact on exploration is contingent upon a system’s pre-existing exploratory capabilities, thereby transcending technological determinism. Furthermore, it identifies the dynamical conditions leading to “exploration collapse” and delineates institutional arrangements and interaction architectures that foster AI-augmented exploratory mobility.
0 citationsRead paper
Predictive Assistance and the Temporal Dynamics of Exploratory Compression
This study investigates how predictive artificial intelligence prematurely stabilizes decision trajectories before humans complete autonomous exploration, thereby suppressing cognitive exploration and the development of representational structures. By constructing a geometric dynamical framework, the work characterizes the evolution of attention in policy space as jointly driven by stable drift, endogenous exploratory perturbations, and response-gated learning, modeling predictive assistance as an exogenous mechanism that compresses exploration. The research uncovers three key mechanisms: predictive assistance reduces exploratory responsiveness, asymmetric accumulation of policy-space curvature induces hysteresis in recovery, and early intervention severely constrains subsequent exploration breadth. It further proposes a testable exploration entropy metric and predictions for premature convergence. Simulations demonstrate that sustained prediction attenuates endogenous perturbation effects, delays the restoration of exploratory capacity upon withdrawal, and that intervention timing critically shapes cognitive developmental trajectories.
0 citationsRead paper
Optimal Market Composition In Monopoly Screening
This study investigates how an upstream monopolist selects the distribution of buyer valuations to optimally trade off consumer surplus against seller profit. Employing mechanism design theory, variational analysis, and optimal control methods, the paper characterizes—for the first time—the efficiency frontier between these two objectives and uncovers a phase transition in market structure as the relative weight on profit varies. When the profit weight is at least as large as that on consumer surplus, the optimal valuation distribution collapses to a degenerate point mass at the highest type. Conversely, when consumer surplus receives greater weight, no types are excluded or clustered at interior points; higher emphasis on consumer surplus yields greater market heterogeneity but lower total surplus. Under mild curvature conditions, this solution is unique.
0 citationsRead paper