In conceptual design, designers often face the challenge of navigating vast design spaces to define ambiguous problems and generate feasible solutions. Recent advancements in generative artificial intelligence (GenAI) offer new opportunities to support this process. However, formative research revealed that designers struggle to simultaneously advance both problem and solution spaces when using GenAI in conceptual design, leading to increased communication load and diminished solution practicality. This study explores the integration of GenAI with the problem-solution co-evolution model to facilitate the construction of a structured design space. We propose a GenAI-supported method for expanding and evaluating the design space and developed the GPSdesign system based on this method. Compared with a baseline system, GPSdesign fosters greater design space divergence, retrospection, and structured construction, while improving design efficiency and solution quality.

Parameter-efficient fine-tuning (PEFT) is crucial for customizing Large Language Models (LLMs) with constrained resources.Although there have been various PEFT methods for dense-architecture LLMs, PEFT for sparsearchitecture LLMs is still underexplored.In this work, we study the PEFT method for LLMs with the Mixture-of-Experts (MoE) architecture and the contents of this work are mainly threefold: (1) We investigate the dispersion degree of the activated experts in customized tasks, and found that the routing distribution for a specific task tends to be highly concentrated, while the distribution of activated experts varies significantly across different tasks.(2) We propose Expert-Specialized Fine-Tuning, or ESFT, which tunes the experts most relevant to downstream tasks while freezing the other experts and modules; experimental results demonstrate that our method not only improves the tuning efficiency, but also matches or even surpasses the performance of fullparameter fine-tuning.(3) We further analyze the impact of the MoE architecture on expertspecialized fine-tuning.We find that MoE models with finer-grained experts are more advantageous in selecting the combination of experts that are most relevant to downstream tasks, thereby enhancing both the training efficiency and effectiveness.Our code is available at https://github.com/deepseek-ai/ESFT.

Prototyping serves as a critical phase in the industrial conceptual design process, enabling exploration of problem space and identification of solutions. Recent advancements in large-scale generative models have enabled AI to become a co-creator in this process. However, designers often consider generative AI challenging due to the necessity to follow computer-centered interaction rules, diverging from their familiar design materials and languages. Physical prototype is a commonly used design method, offering unique benefits in prototype process, such as intuitive understanding and tangible testing. In this study, we propose ProtoDreamer, a mixed-prototype tool that synergizes generative AI with physical prototype to support conceptual design. ProtoDreamer allows designers to construct preliminary prototypes using physical materials, while AI recognizes these forms and vocal inputs to generate diverse design alternatives. This tool empowers designers to tangibly interact with prototypes, intuitively convey design intentions to AI, and continuously draw inspiration from the generated artifacts. An evaluation study confirms ProtoDreamer’s utility and strengths in time efficiency, creativity support, defects exposure, and detailed thinking facilitation.

With the increasing prevalence of artificial intelligence (AI) agents, the transparency of agents has become vital in addressing interaction issues (e.g., trust, usefulness, and understandability). However, determining the transparency of AI agents requires a systematic consideration of complex related factors, including stakeholders, algorithms, context, etc. Thus, in our study, we presented an overview of studies on the transparency of AI agents through multiple-stage bibliometric analysis, and identified an ontological framework of the key concepts relevant to transparent AI. We then built a Transparent-AI Blueprint prototype which is a diagram that visualizes the ontological framework of design concepts. In the subsequent pilot test, we updated Blueprint to the final version, and validated it in a workshop. Our work structurally summarized the design concepts related to the transparency of AI agents, and proposed a useful and practical conceptual design tool that effectively guides designers to operationalize the transparency of AI agents.

We consider the problem of designing the information environment for revenue maximization in a sealed-bid second price auction with two bidders. Much of the prior literature has focused on signal design in settings where bidders are symmetrically informed, or on the design of optimal mechanisms under fixed information structures. We study commonand interdependent-value settings where the mechanism is fixed (a second-price auction), but the auctioneer controls the signal structure for bidders. We show that in a standard common-value auction setting, there is no benefit to the auctioneer in terms of expected revenue from sharing information with the bidders, although there are effects on the distribution of revenues. In an interdependent-value model with mixed private- and common-value components, however, we show that asymmetric, information-revealing signals can increase revenue.

Rival markets like rideshare services, universities, and organ exchanges compete to attract participants, seeking to maximize their own utility at potential cost to overall social welfare. Similarly, individual participants in such multi-market systems also seek to maximize their individual utility. If entry is costly, they should strategically enter only a subset of the available markets. All of this decision making---markets competitively adapting their matching strategies and participants arriving, choosing which market(s) to enter, and departing from the system---occurs dynamically over time. This paper provides the first analysis of equilibrium behavior in dynamic competing matching market systems---first from the points of view of individual participants when market policies are fixed, and then from the points of view of markets when agents are stochastic. When compared to single markets running social-welfare-maximizing matching policies, losses in overall social welfare in competitive systems manifest due to both market fragmentation and the use of non-optimal matching policies. We quantify such losses and provide policy recommendations to help alleviate them in fielded systems.

Artificial Bee Colony (ABC) algorithm is a kind of newly developed bio-inspired intelligence. In this paper, we conduct a investigation on bees' behaviors when seeking for food, and a method for optimizing parameters has been developed. By using this algorithm, we can obtain the best solution to many problems. Pulse Coupled Neural Networks (PCNN) is a kind of algorithm widely used in image processing. The current model of PCNN has several shortcomings, such as losing basic details of the original images. ABC algorithm is adopted to search the best value of PCNN parameters. In this way, the image can be enhanced to the fullest extent. Experimental results verified the feasibility and effectiveness of our proposed approach.