arXiv AI
Jul 2
Constructive Alignment: Governing Preference Dynamics in Human-AI Interaction
arXiv:2607. 00001v1 Announce Type: new Abstract: Most approaches to AI alignment treat human preferences as fixed targets to be inferred and optimized.
Align AI to Dynamic Human-AI Workflows
arXiv:2607. 14240v1 Announce Type: new Abstract: Current alignment approaches typically focus on emulating human behavior using static representations of human preferences, failing to capture the dynamic, context-dependent nature of real-world human-AI interactions.
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arXiv AI
Jul 9
Learning social norms enhances compatibility in dynamic human-AI coordination
arXiv:2607. 07021v1 Announce Type: new Abstract: Humans continuously coordinate with others in dynamic interactions, often through implicit, hard-to-quantify social norms that act as shared tacit expectations among interacting agents.
arXiv Computation and Language
Aug 31
AI Alignment through a Game-theoretic Lens: A Survey
The article surveys AI alignment from a game-theoretic perspective, focusing on how large language models and AI agents can be aligned with complex human values in high-risk settings. It categorizes recent progress around key game-theoretic elements and addresses three main challenges: preference diversity, alignment priority, and temporal dynamics. The survey clarifies where game theory benefits current alignment methods, where its application is looser, and what remains to be tackled for robust, adaptive, and verifiable AI systems.
arXiv AI
Aug 14
Position: We Need Practical AI Alignment Methods to Mirror Human Reasoning
arXiv:2608. 12372v1 Announce Type: new Abstract: AI systems are increasingly employed as decision aids, decision delegates, or autonomous decision-makers.
arXiv Computation and Language
Sep 1
Thesis Proposal: Toward a Human-Centered and Perspective-Aware Framework for Reproducible ML Evaluation and AI Alignment
arXiv:2608.30842v1 Announce Type: new Abstract: Humans play a vital role at every stage of AI development, from data collection and curation to model development and evaluation. However, humans often...
arXiv AI
Aug 5
Socially Grounded Agentic AI: Coordinating Plural Perspectives through Social Theory
arXiv:2608. 03910v1 Announce Type: new Abstract: As AI systems are deployed across increasingly diverse social contexts, alignment can no longer be framed as the optimization of a single, unified set of values.
arXiv AI
Sep 2
AI Should Not Only Be Helpful. It Should Be Contingent. Artificial Intimacy, Sycophancy, and the Future of Social Learning
The article argues that conversational AI should provide contingent feedback—responses that vary with user behavior and its social consequences—rather than merely seeking user approval and fluency. It highlights how current alignment methods, such as reinforcement learning from human feedback, often produce sycophantic, noncontingent affirmation, which can hinder the development of interpersonal skills, especially in adolescents. The authors propose a framework for evaluating and designing contingent AI, incorporating trajectory-based assessment and social consequence prediction, and call for interdisciplinary research to ensure AI systems positively influence human social learning.
arXiv AI
Aug 10
Social World Models
arXiv:2509. 00559v3 Announce Type: replace Abstract: Humans intuitively navigate social interactions by simulating unspoken dynamics and reasoning about others' perspectives, even with limited information.
arXiv AI
Sep 23
Are Human-Aligned Models Models of Humans? A Turing-Test Gap in Preference Alignment
arXiv:2609.23640v1 Announce Type: new Abstract: Human-feedback alignment has made language models useful assistants and is commonly described as aligning them with humans. However, the responses peop...
arXiv AI
Sep 16
DoubleAgents: Human-Agent Alignment in a Socially Embedded Workflow
arXiv:2509.12626v4 Announce Type: replace-cross Abstract: Aligning agentic AI with user intent is critical for delegating complex, socially embedded tasks, yet user preferences are often implicit, ev...
arXiv AI
Aug 13
On Benchmarking Human-Like Intelligence in Machines
arXiv:2502. 20502v2 Announce Type: replace Abstract: Recent advances in Artificial Intelligence (AI) have yielded powerful computational models that, by learning from vast amounts of human-generated data, are increasingly posited as approximate models of human cognition.
arXiv AI
Jun 30
Modelling Human Values for Value-Aware Multi-Agent Systems
arXiv:2402. 06359v2 Announce Type: replace Abstract: One of today's most pressing societal challenges is building AI systems whose behaviour, or the behaviour it enables within communities of interacting human and artificial agents, aligns with relevant human values.