Evaluating the interactions between users and systems presents many challenges. Simulation offers a reliable, re-usable, and repeatable methodology to explore how different users, user behaviours and/or retrieval systems impact performance. With Large Language Models and Generative AI now widely available and accessible, new affordances are possible. These allow researchers to create more ''realistic'' simulated users that can generate queries and judge items like humans, and to develop new retrieval systems where responses and interactions are conversational and based on retrieval augmented generation. This resource paper presents a community-led initiative to update the Simulation of Interactive Information Retrieval (SimIIR) Framework to enable the simulation of conversational search using LLMs. The largest update provides a conversational search workflow which involves a number of new possible interactions with a search system or agent -- enabling a host of new development and evaluation opportunities. Other developments include the Markovian Users, Cognitive States, LLM-based components for assessing snippets/documents/responses, generating queries, deciding on when to stop/continue, and PyTerrier integration. This paper aims to mark the release of SimIIR 3.0 and invites the community to build, extend, and use the resource.

Information Retrieval (IR) research is deeply rooted in experimentation and evaluation, and the Text REtrieval Conference (TREC) has been playing a central role in making that possible since its inauguration in 1992. TREC's mission centers around providing the infrastructure and resources to make IR evaluations possible at scale. Over the years, a plethora of different retrieval problems were addressed, culminating in data artifacts that remained as valuable and useful tools for the IR community. Even though the data are largely available from TREC's website, there is currently no resource that facilitates a cohesive way to obtain metadata information about the run file - the IR community's de-facto standard data format for storing rankings of system-oriented IR experiments.To this end, the work at hand introduces a software suite that facilitates access to metadata of experimental resources, resulting from over 30 years of IR experiments and evaluations at TREC. With a particular focus on the run files, the paper motivates the requirements for better access to TREC metadata and details the concepts, the resources, the corresponding implementations, and possible use cases. More specifically, we contribute a web interface to browse former TREC submissions. Besides, we provide the underlying metadatabase and a corresponding RESTful interface for more principled and structured queries about the TREC metadata.

Reproducibility is a fundamental part of scientific progress. Compared to other scientific fields, computational sciences are privileged as experimental setups can be preserved with ease, and regression experiments allow the validation of computational results by bitwise similarity. When evaluating information access systems, the system users are often considered in the experiments, be it explicit as part of user studies or implicit as part of evaluation measures. Usually, system-oriented Information Retrieval (IR) experiments are evaluated with effectiveness measurements and batches of multiple queries. Successful reproduction of an Information Retrieval (IR) system is often determined by how well it approximates the averaged effectiveness of the original (reproduced) system. Earlier work suggests that the nave comparison of average effectiveness hides differences that exist between the original and reproduced systems. Most importantly, such differences can affect the recipients of the retrieval results, i.e., the system users. To this end, this work sheds light on what implications for users may be neglected when a system-oriented Information Retrieval (IR) experiment is prematurely considered reproduced. Based on simulated reimplementations with comparable effectiveness as the reference system, we show what differences are hidden behind averaged effectiveness scores. We discuss possible future directions and consider how these implications could be addressed with user simulations.

Simulations in various forms have been used to evaluate information access systems, like search engines, recommender systems, or conversational agents. In the form of the Cranfield paradigm, a simulation setup is well-known in the IR community, but user simulations have recently gained interest. While user simulations help to reduce the complexity of evaluation experiments and help with reproducibility, they can also contribute to a better understanding of users. Building on recent developments in methods and toolkits, the Sim4IA workshop aims to bring together researchers and practitioners to form an interactive and engaging forum for discussions on the future perspectives of the field. An additional aim is to plan an upcoming TREC/CLEF campaign.