Users experience fast-food intimacy with Soul's AI boyfriend that conflicts with gradual cultural expectations, introduces technical uncertainty, and shifts emotional labor onto women.
Zhu, and Saleema Amershi
8 Pith papers cite this work. Polarity classification is still indexing.
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Workshop participants preferred bounded, faithful AI agents that evolve only while the user retains capacity and then remain static, leading to a proposal that configuration for post-capacity use reshapes provenance, temporality, and legitimacy in post-mortem agent design.
Formalizes design space for human-LLM collaborative planning along mode, scope, and level axes; evaluates AMBIPOM prototype via user study and benchmark revealing hybrid workflows and trade-offs.
SelfHeal uses two ReAct agents and empirical fix patterns to repair bugs in LLM agents, outperforming baselines on a new 37-instance benchmark.
ZORO integrates rules directly into AI coding workflows by enriching plans, enforcing compliance with proof requirements, and evolving rules via user feedback, resulting in better rule adherence and shifts in user behavior.
Pista decomposes AI agent actions in spreadsheets into auditable steps, enabling real-time user intervention that improves task outcomes, user comprehension, agent perception, and sense of co-ownership over baseline agents.
Context-mediated domain adaptation treats user modifications to AI artifacts as implicit domain specifications that reshape LLM-powered multi-agent reasoning, demonstrated via the Seedentia system which extracted 46 domain knowledge entries from expert edits.
AgentDynEx introduces nudging and a Configuration Matrix to help set up and maintain balanced mechanics and dynamics in multi-agent LLM simulations.
citing papers explorer
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Fast-Food Intimacy: How Chinese Women Navigate Soul's AI Boyfriend
Users experience fast-food intimacy with Soul's AI boyfriend that conflicts with gradual cultural expectations, introduces technical uncertainty, and shifts emotional labor onto women.
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Acts of Configuration: Rethinking Provenance, Temporality and Legitimacy in Post-Mortem Agents
Workshop participants preferred bounded, faithful AI agents that evolve only while the user retains capacity and then remain static, leading to a proposal that configuration for post-capacity use reshapes provenance, temporality, and legitimacy in post-mortem agent design.
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How to Steer Your Multi-Agent System: Human-LLM Collaborative Planning
Formalizes design space for human-LLM collaborative planning along mode, scope, and level axes; evaluates AMBIPOM prototype via user study and benchmark revealing hybrid workflows and trade-offs.
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SelfHeal: Empirical Fix Pattern Analysis and Bug Repair in LLM Agents
SelfHeal uses two ReAct agents and empirical fix patterns to repair bugs in LLM agents, outperforming baselines on a new 37-instance benchmark.
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ZORO: Active Rules for Reliable Vibe Coding
ZORO integrates rules directly into AI coding workflows by enriching plans, enforcing compliance with proof requirements, and evolving rules via user feedback, resulting in better rule adherence and shifts in user behavior.
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Auditing and Controlling AI Agent Actions in Spreadsheets
Pista decomposes AI agent actions in spreadsheets into auditable steps, enabling real-time user intervention that improves task outcomes, user comprehension, agent perception, and sense of co-ownership over baseline agents.
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Context-Mediated Domain Adaptation in Multi-Agent Sensemaking Systems
Context-mediated domain adaptation treats user modifications to AI artifacts as implicit domain specifications that reshape LLM-powered multi-agent reasoning, demonstrated via the Seedentia system which extracted 46 domain knowledge entries from expert edits.
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AgentDynEx: Nudging the Mechanics and Dynamics of Multi-Agent Simulations
AgentDynEx introduces nudging and a Configuration Matrix to help set up and maintain balanced mechanics and dynamics in multi-agent LLM simulations.