The Scaling Paradox in Human-AI Collaboration
arXiv:2603.27438v1 Announce Type: new Abstract: We propose a stylized model of human-AI collaboration that isolates a mechanism we call the novelty bottleneck: the fraction of a
arXiv:2603.18895v1 Announce Type: cross Abstract: Artificial intelligence (AI) systems are deployed as collaborators in human decision-making. Yet, evaluation practices focus
arXiv:2603.20639v1 Announce Type: new Abstract: The "AI singularity" is often miscast as a monolithic, godlike mind. Evolution suggests a different path: intelligence is
arXiv:2609.14500v1 Announce Type: new Abstract: AI scaling studies increasingly evaluate systems that combine a pretrained model with retrieval, search, verification, tools, and
arXiv:2605.04070v1 Announce Type: cross Abstract: Human-AI complementarity, the idea that combining human and AI judgments can outperform either alone, offers a promising pathway
arXiv:2605.08710v1 Announce Type: new Abstract: Human-AI teams fail to outperform their best member in 70% of studies, yet no theory specifies when complementarity is achievable.
arXiv:2609.31793v1 Announce Type: new Abstract: Artificial Intelligence (AI), particularly GenAI, is becoming an increasingly important part of modern work. In industrial
arXiv:2604.27392v1 Announce Type: new Abstract: What shapes a consequential decision when human and artificial intelligence work on it together? The answer is becoming harder to
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