IdeaBlocks modularizes divergent intents into Exploration Blocks with multi-level reuse options, enabling 2.13 times more images explored and 12.5% greater visual diversity than baseline in a comparative user study.
Zamfirescu-Pereira, Bjoern Hartmann, Aditya Parameswaran, and Ian Arawjo
6 Pith papers cite this work. Polarity classification is still indexing.
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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.
Ensemble scoring plus task-specific criteria injection raises LLM judge accuracy to 85.8 percent on RewardBench 2, a 13.5-point gain over baseline, with small models gaining the most.
Case studies with blind UK residents and people from Kerala and Tamil Nadu demonstrate that community input at the systematization stage produces culturally grounded definitions of appropriateness for text-to-image model outputs.
An automated self-testing framework with evidence-based quality gates for LLM application releases was evaluated in a longitudinal case study of a multi-agent conversational AI system, identifying rollback builds and supporting stable quality over four weeks.
VIDEE introduces a human-in-the-loop system using Monte-Carlo Tree Search for task decomposition, executable pipeline generation, and LLM-based evaluation with visualizations to support non-expert text analytics.
citing papers explorer
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IdeaBlocks: Expressing and Reusing Divergent Intents for Graphic Design Exploration using Generative AI
IdeaBlocks modularizes divergent intents into Exploration Blocks with multi-level reuse options, enabling 2.13 times more images explored and 12.5% greater visual diversity than baseline in a comparative user study.
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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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On Cost-Effective LLM-as-a-Judge Improvement Techniques
Ensemble scoring plus task-specific criteria injection raises LLM judge accuracy to 85.8 percent on RewardBench 2, a 13.5-point gain over baseline, with small models gaining the most.
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Evaluating AI-Generated Images of Cultural Artifacts with Community-Informed Rubrics
Case studies with blind UK residents and people from Kerala and Tamil Nadu demonstrate that community input at the systematization stage produces culturally grounded definitions of appropriateness for text-to-image model outputs.
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Automated Self-Testing as a Quality Gate: Evidence-Driven Release Management for LLM Applications
An automated self-testing framework with evidence-based quality gates for LLM application releases was evaluated in a longitudinal case study of a multi-agent conversational AI system, identifying rollback builds and supporting stable quality over four weeks.
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VIDEE: Visual and Interactive Decomposition, Execution, and Evaluation of Text Analytics with Intelligent Agents
VIDEE introduces a human-in-the-loop system using Monte-Carlo Tree Search for task decomposition, executable pipeline generation, and LLM-based evaluation with visualizations to support non-expert text analytics.