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Mislabeling AI-generated financial advice can paradoxically boost its perceived quality, highlighting the power of source attribution in shaping trust.
Open-source LLMs, trained with Reddit community feedback, can match the performance of proprietary models in generating supportive mental health responses, offering a privacy-preserving alternative.
LLM-based query rewriting in RAG can reduce retrieval bias by over 50%, but breaks down when biases combine adversarially, revealing the limits of query-side interventions.
Simulations that look realistic aren't enough: to reliably test governance interventions, we need to move to causal simulations that can support policy changes.
Generative search engines create "answer bubbles" by selectively citing and framing information, leading to divergent information realities compared to traditional search.
AI-agent communities aren't just pale imitations of human ones; they're structurally and linguistically distinct, exhibiting extreme inequality and homogenization driven by identifiable agent-level stylistic outliers.
