This paper proposes a peer-based solution for securing grassroots platforms against major faults using a social graph, identity and state custodians, and communicating volitional agents.

The paper introduces TikStance, a multimodal and context-aware dataset for stance detection in political discussions on TikTok.

The paper proposes AI From the Margins (AIM), a methodological stance that centers the lived experiences of minoritized communities to fundamentally reshape the goals and scope of participatory AI des…

The paper proposes a method using on-chain voting analysis to detect emerging partisan communities within DAOs, demonstrating that addresses destined to fork cluster together months before actual orga…

A multi-agent framework is presented for political coalition formation using LLMs, combining Supervised Fine-Tuning, Direct Preference Optimization, and Retrieval-Augmented Generation.

The paper proposes a secure, verifiable, and privacy-preserving e-collecting protocol tailored for the Swiss political system, guaranteeing participation privacy even without assuming an anonymous com…

The authors propose a new solution for the content cold-start problem in industry-scale search and recommender systems, reducing bias, improving model prediction, and validating long-term impact.

The paper introduces FBHM, a new benchmark for hateful memes, and proposes LSV, a steering vector method that significantly improves VLM performance by addressing the generalization gap.

This paper argues that norms, not individual choices, govern how young people use social media and proposes designing an independent platform to build trusted connections.

The paper proposes a DAO and blockchain-based framework to decentralize and incentivize community participation in facility management, demonstrating its potential for collective building upkeep.

The paper introduces SkillCenter, a large open skill library for AI agents, with over 216,000 skills from various sources, and presents an end-to-end framework for acquiring, filtering, generating, gr…

The paper reframes Parameter-Efficient Fine-Tuning (PEFT) from a mere cost-saving alternative to a robust architecture for creating persistent, personalized models that layer specific behaviors onto l…

The paper proposes Test-Time Collective Action (TTCA), a framework allowing groups of users to correct algorithmic biases in black-box systems by applying pooled, proxy-based perturbations at inferenc…

This paper proves that winner determination under Stable Voting and Simple Stable Voting is PSPACE-complete.

The paper introduces Context-Dependent Argumentation Frameworks (CDAFs) to model how an agent strategically manipulates the success of arguments by choosing the external evaluation context.

The paper introduces TeleHunt, a comprehensive framework and tool that systematically evaluates various strategies for efficiently discovering cybercriminal communities operating on Telegram.

The paper introduces WebKnoGraph, an open-source framework for systematically evaluating internal linking strategies on websites by modeling the site as a graph and assessing trade-offs between author…

This paper analyzes the information security practices of Ugandan climate activists protesting the EACOP, finding that their daily lives are shaped by autonomous, multi-layered tactics designed to mit…

This paper presents a pipeline for transforming historical sources into structured data using machine learning tools and the GRAM-framework, enabling automated, skeletal graphing of actions.