As agentic platforms scale, agents are moving beyond fixed roles and predefined toolchains, creating an urgent need for flexible and decentralized coordination. Current structured communication protocols such as direct agent-to-agent messaging or MCP-style tool calls offer reliability, but they struggle to support the emergent and swarm-like intelligence required in large adaptive systems. Distributed agents must learn continuously, share context fluidly, and coordinate without depending solely on central planners. This paper revisits gossip protocols as a complementary substrate for agentic communication. Gossip mechanisms, long valued in distributed systems for their decentralized and fault-tolerant properties, provide scalable and adaptive diffusion of knowledge and fill gaps that structured protocols alone cannot efficiently address. However, gossip also introduces challenges, including semantic relevance, temporal staleness, and limited guarantees on action consistency in rapidly changing environments. We examine how gossip can support context-rich state propagation, resilient coordination under uncertainty, and emergent global awareness. We also outline open problems around semantic filtering, trust, and knowledge decay. Rather than proposing a complete framework, this paper presents a research agenda for integrating gossip into multi-agent communication stacks and argues that gossip is essential for future agentic ecosystems that must remain robust, adaptive, and self-organizing as their scale and autonomy increase.

Artificial Intelligence (AI) conferences are essential for advancing research, sharing knowledge, and fostering academic community. However, their rapid expansion has rendered the centralized conference model increasingly unsustainable. This paper offers a data-driven diagnosis of a structural crisis that threatens the foundational goals of scientific dissemination, equity, and community well-being. We identify four key areas of strain: (1) scientifically, with per-author publication rates more than doubling over the past decade to over 4.5 papers annually; (2) environmentally, with the carbon footprint of a single conference exceeding the daily emissions of its host city; (3) psychologically, with 71% of online community discourse reflecting negative sentiment and 35% referencing mental health concerns; and (4) logistically, with attendance at top conferences such as NeurIPS 2024 beginning to outpace venue capacity. These pressures point to a system that is misaligned with its core mission. In response, we propose the Community-Federated Conference (CFC) model, which separates peer review, presentation, and networking into globally coordinated but locally organized components, offering a more sustainable, inclusive, and resilient path forward for AI research.

The adoption of open science has quickly changed how artificial intelligence (AI) policy research is distributed globally. This study examines the regional trends in the citation of preprints, specifically focusing on the impact of two major disruptive events: the COVID-19 pandemic and the release of ChatGPT, on research dissemination patterns in the United States, Europe, and South Korea from 2015 to 2024. Using bibliometrics data from the Web of Science, this study tracks how global disruptive events influenced the adoption of preprints in AI policy research and how such shifts vary by region. By marking the timing of these disruptive events, the analysis reveals that while all regions experienced growth in preprint citations, the magnitude and trajectory of change varied significantly. The United States exhibited sharp, event-driven increases; Europe demonstrated institutional growth; and South Korea maintained consistent, linear growth in preprint adoption. These findings suggest that global disruptions may have accelerated preprint adoption, but the extent and trajectory are shaped by local research cultures, policy environments, and levels of open science maturity. This paper emphasizes the need for future AI governance strategies to consider regional variability in research dissemination and highlights opportunities for further longitudinal and comparative research to deepen our understanding of open-access adoption in AI policy development.

Academic project websites can more effectively disseminate research when they clearly present core content and enable intuitive navigation and interaction. However, current approaches such as direct Large Language Model (LLM) generation, templates, or direct HTML conversion struggle to produce layout-aware, interactive sites, and a comprehensive evaluation suite for this task has been lacking. In this paper, we introduce Paper2Web, a benchmark dataset and multi-dimensional evaluation framework for assessing academic webpage generation. It incorporates rule-based metrics like Connectivity, Completeness and human-verified LLM-as-a-Judge (covering interactivity, aesthetics, and informativeness), and PaperQuiz, which measures paper-level knowledge retention. We further present PWAgent, an autonomous pipeline that converts scientific papers into interactive and multimedia-rich academic homepages. The agent iteratively refines both content and layout through MCP tools that enhance emphasis, balance, and presentation quality. Our experiments show that PWAgent consistently outperforms end-to-end baselines like template-based webpages and arXiv/alphaXiv versions by a large margin while maintaining low cost, achieving the Pareto-front in academic webpage generation.

In the human-bot symbiotic information ecosystem, social bots play key roles in spreading and correcting disinformation. Understanding their influence is essential for risk control and better governance. However, current studies often rely on simplistic user and network modeling, overlook the dynamic behavior of bots, and lack quantitative evaluation of correction strategies. To fill these gaps, we propose MADD, a Multi Agent based framework for Disinformation Dissemination. MADD constructs a more realistic propagation network by integrating the Barabasi Albert Model for scale free topology and the Stochastic Block Model for community structures, while designing node attributes based on real world user data. Furthermore, MADD incorporates both malicious and legitimate bots, with their controlled dynamic participation allows for quantitative analysis of correction strategies. We evaluate MADD using individual and group level metrics. We experimentally verify the real world consistency of MADD user attributes and network structure, and we simulate the dissemination of six disinformation topics, demonstrating the differential effects of fact based and narrative based correction strategies.

Affective polarization, or, inter-party hostility, is increasingly recognized as a pervasive issue in democracies worldwide, posing a threat to social cohesion. The digital media ecosystem, now widely accessible and ever-present, has often been implicated in accelerating this phenomenon. However, the precise causal mechanisms responsible for driving affective polarization have been a subject of extensive debate. While the concept of echo chambers, characterized by individuals ensconced within like-minded groups, bereft of counter-attitudinal content, has long been the prevailing hypothesis, accumulating empirical evidence suggests a more nuanced picture. This study aims to contribute to the ongoing debate by employing an agent-based model to illustrate how affective polarization is either fostered or hindered by individual news consumption and dissemination patterns based on ideological alignment. To achieve this, we parameterize three key aspects: (1) The affective asymmetry of individuals' engagement with in-party versus out-party content, (2) The proportion of in-party members within one's social neighborhood, and (3) The degree of partisan bias among the elites within the population. Subsequently, we observe macro-level changes in affective polarization within the population under various conditions stipulated by these parameters. This approach allows us to explore the intricate dynamics of affective polarization within digital environments, shedding light on the interplay between individual behaviors, social networks, and information exposure.

The fast-moving, dynamic world of artificial intelligence (AI) stands in stark contrast to the slow-moving, conservative world of academia.11 This is particularly clear in the world of AI ethics, where in addition to the industry-academia contrast we also have the meeting of very different academic disciplines, including computer science, philosophy, ethics, and social sciences. The traditions, norms, and values of these disciplines are often at odds with one another, making interdisciplinarity challenging. Take, for example, preprinting, the practice of quickly disseminating research before potentially--but not necessarily--seeking publication in traditional academic journals.a Interdisciplinary conflicts appear when, for example, researchers from a computer science background, where rapid publication of preprints on servers such as arXiv is the norm,2 meet researchers from the social sciences and humanities, where this is less common.1,30

The rapid proliferation of the Internet and the widespread adoption of social networks have significantly accelerated information dissemination. However, this transformation has introduced complexities in information capture and processing, posing substantial challenges for researchers and practitioners. Predicting the dissemination of topic-related information within social networks has thus become a critical research focus. This paper proposes a predictive model for topic dissemination in social networks by integrating multidimensional features derived from key dissemination characteristics. Specifically, we introduce two novel indicators, user relationship breadth and user authority, into the PageRank algorithm to quantify user influence more effectively. Additionally, we employ a Text-CNN model for sentiment classification, extracting sentiment features from textual content. Temporal embeddings of nodes are encoded using a Bi-LSTM model to capture temporal dynamics. Furthermore, we refine the measurement of user interaction traces with topics, replacing traditional topic view metrics with a more precise communication characteristics measure. Finally, we integrate the extracted multidimensional features using a Transformer model, significantly enhancing predictive performance. Experimental results demonstrate that our proposed model outperforms traditional machine learning and unimodal deep learning models in terms of FI-Score, AUC, and Recall, validating its effectiveness in predicting topic propagation within social networks.

With the remarkable capability to reach the public instantly, social media has become integral in sharing scholarly articles to measure public response. Since spamming by bots on social media can steer the conversation and present a false public interest in given research, affecting policies impacting the public's lives in the real world, this topic warrants critical study and attention. We used the Altmetric dataset in combination with data collected through the Twitter Application Programming Interface (API) and the Botometer API. We combined the data into an extensive dataset with academic articles, several features from the article and a label indicating whether the article had excessive bot activity on Twitter or not. We analyzed the data to see the possibility of bot activity based on different characteristics of the article. We also trained machine-learning models using this dataset to identify possible bot activity in any given article. Our machine-learning models were capable of identifying possible bot activity in any academic article with an accuracy of 0.70. We also found that articles related to "Health and Human Science" are more prone to bot activity compared to other research areas. Without arguing the maliciousness of the bot activity, our work presents a tool to identify the presence of bot activity in the dissemination of an academic article and creates a baseline for future research in this direction.

Recent advancements have showcased the capabilities of Large Language Models like GPT4 and Llama2 in tasks such as summarization, translation, and content review. However, their widespread use raises concerns, particularly around the potential for LLMs to spread persuasive, humanlike misinformation at scale, which could significantly influence public opinion. This study examines these risks, focusing on LLMs ability to propagate misinformation as factual. To investigate this, we built the LLM Echo Chamber, a controlled digital environment simulating social media chatrooms, where misinformation often spreads. Echo chambers, where individuals only interact with like minded people, further entrench beliefs. By studying malicious bots spreading misinformation in this environment, we can better understand this phenomenon. We reviewed current LLMs, explored misinformation risks, and applied sota finetuning techniques. Using Microsoft phi2 model, finetuned with our custom dataset, we generated harmful content to create the Echo Chamber. This setup, evaluated by GPT4 for persuasiveness and harmfulness, sheds light on the ethical concerns surrounding LLMs and emphasizes the need for stronger safeguards against misinformation.