This paper discusses the misalignment between current AI goals and the needs of creators in fields like structural design, and proposes a conversational, multimodal, and responsive AI system for const…

The paper proposes CoEvo-AHD, an LLM-driven co-evolutionary framework that co-evolves two coupled operator populations to design effective heuristics for combinatorial optimization problems with stron…

The paper experimentally evaluates 12 multi-agent LLM collaboration topologies for software design, finding that structural adversarial prompting and cross-model review are the most effective approach…

The paper introduces GeoCoupling, a framework that systematically optimizes the temporal coupling between heterogeneous modalities to improve the co-design of biomolecules, outperforming fixed synchro…

This paper identifies and validates eight core UX principles for human-AI agent interaction in the workplace using a multi-method approach.

pcbGPT is a grounded system that automatically generates editable KiCad PCB schematics from natural language requirements, achieving high accuracy on complex embedded design tasks.

This paper presents a unified framework for end-to-end co-design of neural network processors.

HandMade is a workflow that combines VR 3D sketching and language for open-domain 3D asset generation, allowing users to specify object layout and part relationships through 3D sketching and language.

This paper introduces MECoBench, a multimodal embodied cooperation benchmark, and explores the benefits and limitations of collaboration in multimodal large language models through extensive experimen…

This paper introduces a controlled, two-player extension of the Alternate Uses Test (AUT) for comparing human-human and human-AI co-creation under matched conditions, demonstrating equivalent original…

The paper introduces MUSE, a comprehensive benchmark that evaluates Text-to-CAD generation by assessing complex assemblies based on functionality, manufacturability, and assemblability, moving beyond…

This paper investigates the influence of image-generation AI interfaces on human decision making, finding that higher variance in design sets leads to selection of center-proximal designs.

The paper introduces MAAD, a multi-agent framework that autonomously transforms software requirements into comprehensive, multi-view architectural blueprints, significantly improving completeness and…

This paper compares four requirements elicitation approaches using AI-supported collaboration and evaluates their impact on requirements artifact quality and stakeholder perceptions.

This paper introduces PDAGENT-BENCH, a comprehensive benchmark for evaluating Large Language Models and vision-language models in the physical design stack of Very Large-Scale Integrated Circuits.

This paper explores how humans and conversational large language models can collaborate through vibe coding, comparing solo and collaborative interaction models in software development using Test-Driv…

This paper introduces Pairwise Modular Smell (PairSmell) for identifying flawed architectural decisions and trains machine learning models to predict two forms of PairSmell using 19 pair characteristi…