Human agency relocates from interface affordances to conversational processes of goal articulation, output evaluation, and outcome negotiation in AI interactions.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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RelPrism generates self-supervised pseudo-tasks from three attribute perspectives via multi-granularity clustering to improve representation learning for relational database prediction tasks.
CAT improves line coverage by 18% and branch coverage by 22% over prior LLM test generation methods by adding call-chain and dependency context from static analysis to prompts.
DDAP is a controlled agentic framework that guides non-experts via four LLM-assisted stages to construct competitive AI pipelines for business, biology, and health domains.
citing papers explorer
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After the Interface: Relocating Human Agency in the Age of Conversational AI
Human agency relocates from interface affordances to conversational processes of goal articulation, output evaluation, and outcome negotiation in AI interactions.
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RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databases
RelPrism generates self-supervised pseudo-tasks from three attribute perspectives via multi-granularity clustering to improve representation learning for relational database prediction tasks.
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Call-Chain-Aware LLM-Based Test Generation for Java Projects
CAT improves line coverage by 18% and branch coverage by 22% over prior LLM test generation methods by adding call-chain and dependency context from static analysis to prompts.
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From Intent to AI Pipelines: A Controlled Agentic Framework for Non-AI Expert Scientists
DDAP is a controlled agentic framework that guides non-experts via four LLM-assisted stages to construct competitive AI pipelines for business, biology, and health domains.