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 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…

This paper improves an algorithm for computing lower bounds on the margin of a Single Transferable Vote (STV) election, making risk-limiting audits more practical.

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

This paper provides a mathematical framework for studying different policy learning problems and shows reductions between them.

This paper introduces campaign diagrams, a visualization technique for analyzing resource utilization and identifying bottlenecks in modern workloads.

This paper proposes a method for auditing parliamentary elections to certify a winning majority instead of individual seats, reducing the number of ballots inspected by almost a thousand-fold.

This paper introduces Democratic ICAI, an approach to summarize preferences into natural-language principles through structured persona debate, yielding clearer and more comprehensive steering princip…

This paper proposes a secure liquid democracy mechanism using sealed delegation and ranked multi-delegation with personal fallback ballots, and evaluates its impact on representational accuracy and vo…

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

This paper examines the impact of bot adoption on group organization in open-source software projects, finding that bots lead to more repeated collaboration, fewer conflict cascades, and more distinct…

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.

This paper analyzes 18 state-level AI committee reports to understand how policymakers discuss AI benefits and risks, comparing them to established taxonomy and HCI scholars' concerns.

The paper introduces an outer-loop AI agent that autonomously redesigns LLM policy-synthesis pipelines for multi-agent social dilemmas, demonstrating that the optimal pipeline structure depends critic…

The paper proposes using an LLM aggregator that analyzes complete reasoning traces, demonstrating that trace-level synthesis is superior to traditional consensus methods like majority voting for solvi…

This paper introduces a welfare-centric framework for designing institutional incentives, showing that optimizing for total social welfare often requires different incentive levels than those optimize…

This paper analyzes the Loki e-voting protocol, demonstrating that while it attempts to solve coercion-resistance without pre-agreed secrets, it remains vulnerable to specific attacks, suggesting that…

This paper introduces a new way to represent finite posets as subwords of finite words in categories, and characterizes the monic categories that admit this representation.

The paper proposes viewing national AI development, specifically in France, as a 'national AI learning system' governed by a controlled balance between information injection and entropy dissipation, a…