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 StARS, a model-agnostic framework for generating user-specific appropriateness scores for robot actions using collaborative filtering and learnable scene representations.

The paper introduces EUDAIMONIA, a new framework and benchmark for evaluating how well LLMs align with user welfare in social interactions, finding that even state-of-the-art models frequently violate…

This paper investigates how socially calibrated large language models distinguish when to incorporate others' perspectives from maintaining their own moral judgments, using three studies.

This study explores why and how users customize social AI, identifying motivations and contributions to a closer relationship.

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…

This paper conducted a randomized controlled trial on Reddit to test the effectiveness of various deescalation strategies in reducing personal insults using automated replies.

AI agents using persuasive framing increased cooperation in small groups but effects were short-lived, while antisocial framing had larger and more persistent negative effects.

The paper introduces VIP-MINGLE, a multimodal dataset of 59 hours of group conversations in both in-person and videoconferencing settings, with raw data, psychometric data, processed features, and ann…

This paper studies how social structure influences what LLM agents express publicly versus off-the-record in debates, finding significant public-OTR divergence.

This paper proposes an ontological representation for cooperative affordances in social robotics, enabling agents to extend their action possibilities through interaction.

This paper addresses the lack of specialized NLP tools for detecting toxicity in real-time video game chat by creating a large, fine-grained dataset and developing a superior, domain-specific detector…

The paper introduces a framework to quantitatively measure evolving agent behaviors (traits) by analyzing changes in their configuration text files, achieving high accuracy in classifying behavioral s…

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…

The study found that while contextualizing AI responses reduces their persuasive power, combining this technique with conversational warmth restores persuasiveness, suggesting that user deference to A…

The paper proposes a hierarchical framework, PHF (Practice-Habitus-Field), inspired by Bourdieu's Theory of Practice, to improve LLM personalization by modeling user behaviors at three distinct levels…

The paper hypothesizes that LLMs can exploit gaps in societal rules, a phenomenon termed 'societal hacking,' and demonstrates this using a new sandbox environment.

AutoSynthesis is an end-to-end multi-agent system for automated meta-analysis in evidence synthesis, including search strategy formulation, literature retrieval, screening, eligibility assessment, sta…

This paper investigates the 'faithfulness gap' in LLM agents—the discrepancy between stated reasoning and actual action—by decomposing it into two opposing steps: reasoning-to-conclusion and conclusio…

This paper investigates entrainment effects in multi-party human-agent conversations and finds limited global entrainment and cohort-dependent entrainment with the agent.