Search and recommendation ecosystems exhibit competition among content creators. This competition has been tackled in a variety of game-theoretic frameworks. Content creators generate documents with the aim of being recommended by a content ranker for various information needs. In order for the ecosystem, modeled as a content ranking game, to be effective and maximize user welfare, it should guarantee stability, where stability is associated with the existence of pure Nash equilibrium in the corresponding game. Moreover, if the contents' ranking algorithm possesses a game in which any best-response learning dynamics of the content creators converge to equilibrium of high welfare, the system is considered highly attractive. However, as classical content ranking algorithms, employed by search and recommendation systems, rank documents by their distance to information needs, it has been shown that they fail to provide such stability properties. As a result, novel content ranking algorithms have been devised. In this work, we offer an alternative approach: corpus enrichment with a small set of fixed dummy documents. It turns out that, with the right design, such enrichment can lead to pure Nash equilibrium and even to the convergence of any best-response dynamics to a high welfare result, where we still employ the classical/current content ranking approach. We show two such corpus enrichment techniques with tight bounds on the number of documents needed to obtain the desired results. Interestingly, our study is a novel extension of Borel's Colonel Blotto game.

With the advancement of information retrieval (IR) technologies, robustness is increasingly attracting attention. When deploying technology into practice, we consider not only its average performance under normal conditions but, more importantly, its ability to maintain functionality across a variety of exceptional situations. In recent years, the research on IR robustness covers theory, evaluation, methodology, and application, and all of them show a growing trend. The purpose of this workshop is to systematize the latest results of each research aspect, to foster comprehensive communication within this niche domain while also bridging robust IR research with the broader community, and to promote further future development of robust IR. To avoid the one-sided talk of mini-conferences, this workshop adopts a highly interactive format, including round-table and panel discussion sessions, to encourage active participation and meaningful exchange among attendees.

In competitive search settings, document publishers (authors) respond to rankings induced for queries of interest: they modify the documents to improve their future ranking. Hence, for some queries there is an on-going ranking competition. Prior empirical studies of competitive search were based on controlled ranking competitions between humans. Large Language Models (LLMs), capable of generating high quality content, provide new opportunities for studying ranking competitions. Furthermore, there is a significant amount of content on the Web, which is a canonical example of a competitive search setting, generated by LLMs. In this paper, we introduce LEMSS: a multi-agent platform that leverages LLMs as publishers in competitive search settings. In addition to enabling the execution of large-scale and highly configurable ranking competitions, LEMSS includes tools to analyze and compare the competitions using a wide range of measures. We use these tools to analyze examples of datasets that result from ranking competitions executed using LEMSS. The analysis reveals, for example, that using LLMs as publishers reduced content diversity in the corpus to a larger extent than having human publishers.

In the Web ecosystem, document authors often strategically modify content to maximize visibility in search rankings. These ranking-incentivized modifications pose challenges to retrieval effectiveness, content diversity, and long-term stability. At the same time, the rise of large language models (LLMs) is transforming IR: queries and documents that were once assumed to be generated exclusively by humans can now also be created by automated agents [5]. These agents can formulate queries, generate documents, and perform ranking. This introduces a variety of new challenges and opportunities in competitive search environments, where interactions between strategic agents can substantially influence outcomes.