As LLMs evolve from standalone assistants into autonomous agents, they increasingly operate in populations where communication, coordination, and competition form community-like interaction networks. In such settings, individually value-steered behaviors can accumulate into group-level dynamics, raising the question of how human values shape collective outcomes in interacting LLM-agent populations. We introduce CIVA, a community-level value analysis framework that varies value direction and prevalence, then measures how these interventions propagate through repeated interactions into macro-level dynamics and micro-level behaviors. We apply it in a resource-constrained multi-agent community where LLM agents autonomously communicate, cooperate, and compete. The value scan reveals distinct structural response patterns: activation-supporting values such as benevolence supports persistence and resilience, an activation-constraining value such as tradition regulates growth and cohesion, and a bidirectionally disruptive value such as power perturbs collective regimes in both directions. At the micro level, representative interventions induce emergent behaviors including deception, betrayal, self-preservation, and power-seeking. These results show that human values can act as population-level structural variables in agent communities and motivate evaluation for multi-agent value alignment.

We introduce SmartCiteCon (SCC), a Java API for extracting both explicit and implicit citation context from academic literature in English. The tool is built on a Support Vector Machine (SVM) model trained on a set of 7,058 manually annotated citation context sentences, curated from 34,000 papers from the ACL Anthology. The model with 19 features achieves F1=85.6%. SCC supports PDF, XML, and JSON files out-of-box, provided that they are conformed to certain schemas. The API supports single document processing and batch processing in parallel. It takes about 12–45 seconds on average depending on the format to process a document on a dedicated server with 6 multithreaded cores. Using SCC, we extracted 11.8 million citation context sentences from ~33.3k PMC papers in the CORD-19 dataset, released on June 13, 2020. We will provide continuous supplementary data contribution to the CORD-19 and other datasets. The source code is released at https://gitee.com/irlab/SmartCiteCon.