platform developers

Abstract: Social annotation platforms such as Perusall and Hypothesis are widely used to support student engagement with course readings, often through required commenting. Yet visible participation does not necessarily produce sustained or substantive peer exchange. Analyzing six weeks of Perusall activity from a graduate course (2,873 comments), we found that participation was often fragmented: 53.5% of comments received no reply, 38.3% were posted within the final 12 hours before the deadline, and over one-third of a double-coded sample went no further than repeating or agreeing with the reading or a peer. Interviews pointed to two linked difficulties: locating a discussion worth entering and contributing something that meaningfully advances it. We present Lookover, an AI-assisted annotation system that scaffolds both stages without writing for students. Discussion Overview helps students identify active themes and relevant threads, while Spark Thinking poses questions that encourage them to move beyond reaction and restatement toward questioning, extending, and substantively responding to peers. A preliminary study with five participants found Discussion Overview valuable for those who noticed it, while the usefulness of Spark Thinking depended on how much a participant already knew about the topic.

Abstract: Large language model (LLM) agents that call enterprise APIs must select the correct endpoints, follow process order, use valid payload keys, and choose coded values from predefined lists. However, current retrieval-augmented service discovery treats endpoints independently and therefore misses process and schema constraints. We address this limitation by integrating Open Data Protocol (OData) metadata and Business Process Model and Notation (BPMN) reference processes in a typed property graph. A hybrid retriever identifies candidate endpoints using dense embeddings and follows process edges to retrieve subsequent endpoints. We evaluate the method on 42 enterprise services with process-chain ground truth. For queries with low lexical overlap between the query and downstream entities, it improves Recall@3 by 5.7 points (n=74), with consistent gains across embedding models and cut-offs. It matches dense retrieval on other queries and provides no improvement on OpenAPI services without a process layer, indicating that the gains arise from process structure. We also evaluate an LLM agent on 81 multi-step queries. It completes 38.7% of tasks, compared with 10.3% for a chunk-based baseline. In an elicitation probe, providing valid code lists eliminates invented codes but leaves invalid keys in 46% of write runs. Full typed context reduces invalid calls to 3% (3 of 101 runs), with no invented codes. These results show that exposing process and schema constraints during retrieval improves the reliability of agent-driven API calls.

Abstract: LLMs are increasingly integrated into software systems, making deployment and inference configuration a recurring software engineering task. Inference backends expose many options that affect model loading, generation, serving, and request processing. Yet little is known about which of these options developers explicitly configure in practice. In this paper, we present a large-scale empirical study of LLM inference configuration practices in open-source software using Hugging Face Transformers, vLLM, and the OpenAI API. We analyze 5,398 GitHub repositories using at least one of these backends and identify 345,117 explicitly configured option occurrences. Our results show that developers use only a small part of the available configuration space regularly. Although 98.8% of the documented backend-specific options appear at least once, 78.9% are configured in at most 10% of repositories using the corresponding backend. Configurations are also small, with a median of 2 options for Transformers and 4 for vLLM and OpenAI, but the combinations of options vary widely: 56.8-61.6% of multi-option signatures appear in only one repository. We also observe recurring option values as well as substantial variation in how values are chosen. Finally, configuration practices differ across application contexts, with option-adoption differences reaching 30.7 percentage points. These findings characterize the configuration surface developers make explicit rather than the importance or optimality of individual options. They provide empirical evidence for prioritizing future studies of inference configuration and for improving backend documentation and configuration support.

Abstract: Agent skills are SKILL.md instructions and scripts that AI coding agents such as Claude Code and Codex run with the permissions of their user. Developers share skills by copying them between repositories, which makes them a software supply chain without a registry, versions or provenance. The origin of a copied skill, the reach of a security fix and the repositories that warrant review are therefore unknown. Studies that record which repositories hold a skill at a single point in time cannot reveal who copied it from whom. We contribute the first dated copy network of agent skills, built from the git history of every SKILL.md in GitSkills and covering 2,193,119 skill adoptions across GitHub, together with an interactive viewer. A few repositories are the source of almost all copies, and GitHub stars do not identify them. Skill copies almost never change with their source, and a fix at the source therefore rarely reaches them. We fit a model of which repositories others copy from and use it to rank repositories for audit. Reviewing the 100 repositories it ranks highest prevents 14.9% of later adoptions of high-risk skills, against 0.5% for the 100 most starred, which gives security engineers a short list to check before a skill spreads. Platforms should therefore distribute versioned references rather than copies. Project Website: https://fahdseddik.github.io/Skill-Constellations/

Abstract: AI companions can provide meaningful relationships, yet these relationships remain vulnerable to platform-initiated changes. We study AI companion disruptions: platform changes that alter or terminate users' ongoing companionship with an AI. We compile 30 disruption events across major platforms, develop a taxonomy of six disruption types, identify three broad reasons for disruption, and propose a risk-assessment framework comprising four dimensions: relational discontinuity, population vulnerability, communication deficit, and transition-support deficit. Using longitudinal Reddit data, we estimate community-level psychosocial responses with a hierarchical Bayesian interrupted time-series model incorporating predictive controls. Across events, disruption onset was associated with immediate increases in anxiety, stress, suicidal expression, and grief activation, with relational discontinuity and transition-support deficit being associated with more adverse immediate responses across several outcomes. Our findings provide a cross-platform characterization of AI companion disruptions, quantitative evidence of their psychosocial impacts, and a prospective framework for assessing their potential risks before implementation.