SEED is a structural encoding framework using typed actor-flow graphs to describe, evaluate novelty of, and generate experimental designs for AI-enabled science under feasibility and governance constraints.
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9 Pith papers cite this work, alongside 470 external citations. Polarity classification is still indexing.
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The authors propose a retrieval-augmented framework that grounds AI exposure labels for 18,796 O*NET occupation-task pairs in retrieved news and academic abstracts, outperforming zero-shot prompting in 72% of disagreements and aligning better with observed real-world usage.
Fluent AI users adopt an active, iterative collaboration mode that produces more visible failures but better recovery and success on hard tasks, whereas novices experience more invisible failures from passive use.
Shorter LLM response latencies reduce perceived output thoughtfulness and usefulness, while task type affects prompting frequency independently of latency.
The paper claims that alignment requires treating AI as part of the self through cognitive co-regulation, identifying risks like deskilling and automation bias while drawing on System 0 cognition theory.
A bilevel optimization framework smooths isotonic regression outputs into continuous piece-wise linear monotonic functions to recover marginal properties in both convex and non-convex cases.
Generative AI adoption in Europe ranges from under 3% to 25%, is steeper for skilled workers in abstract-task jobs and in digitally advanced countries with training, shows a gender gap in exposed roles, and has produced no detectable shift in reported task content so far.
AI-saturated markets will produce premiums for verified human presence in labor, requiring governance to treat human-provenance verification as infrastructure rather than optional authenticity labels.
citing papers explorer
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Agents for Experiments, Experiments for Agents: A Design Grammar for AI-Enabled Experimental Science
SEED is a structural encoding framework using typed actor-flow graphs to describe, evaluate novelty of, and generate experimental designs for AI-enabled science under feasibility and governance constraints.
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Jobs' AI Exposure Should Be Measured from Evidence, Not Model Priors
The authors propose a retrieval-augmented framework that grounds AI exposure labels for 18,796 O*NET occupation-task pairs in retrieved news and academic abstracts, outperforming zero-shot prompting in 72% of disagreements and aligning better with observed real-world usage.
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A paradox of AI fluency
Fluent AI users adopt an active, iterative collaboration mode that produces more visible failures but better recovery and success on hard tasks, whereas novices experience more invisible failures from passive use.
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The Impact of Response Latency and Task Type on Human-LLM Interaction and Perception
Shorter LLM response latencies reduce perceived output thoughtfulness and usefulness, while task type affects prompting frequency independently of latency.
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Position: AI as Part of Self -- Extending the Mind Requires Cognitive Co-Regulation
The paper claims that alignment requires treating AI as part of the self through cognitive co-regulation, identifying risks like deskilling and automation bias while drawing on System 0 cognition theory.
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Piece-wise linear isotonic regression
A bilevel optimization framework smooths isotonic regression outputs into continuous piece-wise linear monotonic functions to recover marginal properties in both convex and non-convex cases.
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From Exposure to Adoption: Generative AI in European Workplaces
Generative AI adoption in Europe ranges from under 3% to 25%, is steeper for skilled workers in abstract-task jobs and in digitally advanced countries with training, shows a gender gap in exposed roles, and has produced no detectable shift in reported task content so far.
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Human-Provenance Verification should be Treated as Labor Infrastructure in AI-Saturated Markets
AI-saturated markets will produce premiums for verified human presence in labor, requiring governance to treat human-provenance verification as infrastructure rather than optional authenticity labels.
- LLM Harms: A Taxonomy and Discussion