Beyond the Proving Ground: Independent Public-Road Testing of Assisted Lane Change Systems using LiDAR
本文提出了一种使用LiDAR在公共道路上独立测试辅助车道变换系统的方法，以解决商业高级驾驶辅助系统的安全性和合规性问题。
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Supporting Migration Policies with Forecasts: Illegal Border Crossings in Europe through a Mixed Approach
This study addresses the European Union’s migration governance needs by developing a one-year forecasting model for irregular border crossings along five key migratory routes, supporting early warning, strategic decision-making, and solidarity mechanisms under the EU’s Annual Migration and Asylum Report (AMMR) framework. Methodologically, it integrates XGBoost and LSTM for hybrid temporal modeling, incorporates heterogeneous multi-source data—including border statistics, conflict indices, and climate indicators—and augments the model with expert-derived covariates elicited via the Delphi method, alongside formal uncertainty quantification. Its primary contribution lies in the novel encoding of structured expert judgment as interpretable, quantitative features—bridging the gap between data-driven forecasting and policy complexity. Historical backtesting demonstrates a 23% reduction in mean absolute error and successful replication of three major migration surges during 2022–2024, confirming the model’s reliability and actionable value in real-world policy contexts.
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Auditing LLM Editorial Bias in News Media Exposure
This study addresses the underexplored issue of *agentic editorial bias*—systematic, implicit information curation—when large language models (LLMs) function as news gatekeepers. Method: We conduct the first systematic audit of four state-of-the-art LLMs (GPT-4o-Mini, Claude-3.7-Sonnet, Gemini-2.0-Flash) against Google News, employing a multi-layered algorithmic framework integrating topic-based querying, media outlet classification, ideological positioning, and factual accuracy assessment—rigorously validated across diverse prompting strategies and reliability benchmarks. Results: All LLMs exhibit statistically significant, robust ideological skew and uneven attention allocation: they amplify ideologically aligned outlets while suppressing others, yielding lower media diversity and narrower exposure sets than conventional news aggregators. Crucially, models differ markedly in directional bias. We introduce the concept of *agentic editorial policy* to formalize LLMs’ latent, systemic filtering mechanisms—revealing their emergent role as high-stakes news intermediaries with substantial information manipulation potential. This work provides foundational empirical evidence and a theoretical framework for LLM content governance.
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Beyond the Proving Ground: Independent Public-Road Testing of Assisted Lane Change Systems using LiDAR
本文提出了一种使用LiDAR在公共道路上独立测试辅助车道变换系统的方法，以解决商业高级驾驶辅助系统的安全性和合规性问题。
0 citationsRead paper
Supporting Migration Policies with Forecasts: Illegal Border Crossings in Europe through a Mixed Approach
This study addresses the European Union’s migration governance needs by developing a one-year forecasting model for irregular border crossings along five key migratory routes, supporting early warning, strategic decision-making, and solidarity mechanisms under the EU’s Annual Migration and Asylum Report (AMMR) framework. Methodologically, it integrates XGBoost and LSTM for hybrid temporal modeling, incorporates heterogeneous multi-source data—including border statistics, conflict indices, and climate indicators—and augments the model with expert-derived covariates elicited via the Delphi method, alongside formal uncertainty quantification. Its primary contribution lies in the novel encoding of structured expert judgment as interpretable, quantitative features—bridging the gap between data-driven forecasting and policy complexity. Historical backtesting demonstrates a 23% reduction in mean absolute error and successful replication of three major migration surges during 2022–2024, confirming the model’s reliability and actionable value in real-world policy contexts.
0 citationsRead paper
Auditing LLM Editorial Bias in News Media Exposure
This study addresses the underexplored issue of *agentic editorial bias*—systematic, implicit information curation—when large language models (LLMs) function as news gatekeepers. Method: We conduct the first systematic audit of four state-of-the-art LLMs (GPT-4o-Mini, Claude-3.7-Sonnet, Gemini-2.0-Flash) against Google News, employing a multi-layered algorithmic framework integrating topic-based querying, media outlet classification, ideological positioning, and factual accuracy assessment—rigorously validated across diverse prompting strategies and reliability benchmarks. Results: All LLMs exhibit statistically significant, robust ideological skew and uneven attention allocation: they amplify ideologically aligned outlets while suppressing others, yielding lower media diversity and narrower exposure sets than conventional news aggregators. Crucially, models differ markedly in directional bias. We introduce the concept of *agentic editorial policy* to formalize LLMs’ latent, systemic filtering mechanisms—revealing their emergent role as high-stakes news intermediaries with substantial information manipulation potential. This work provides foundational empirical evidence and a theoretical framework for LLM content governance.
0 citationsRead paper