# EUDAIMONIA: Evaluating Undesirable Dynamics in AI

arXiv: https://arxiv.org/abs/2605.30654 · HTML: https://mlsanity.com/paper/2605.30654/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 94 references that are in the map.
- First posted on arXiv 28 May 2026.

## Builds on (most cited first, 27 of 27)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://mlsanity.com/paper/2403.04132/) (2024)
- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [WildChat: 1M ChatGPT Interaction Logs in the Wild](https://mlsanity.com/paper/2405.01470/) (2024)
- [SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents](https://mlsanity.com/paper/2310.11667/) (2023)
- [ARC-AGI-2: A New Challenge for Frontier AI Reasoning Systems](https://mlsanity.com/paper/2505.11831/) (2025)
- [Understanding Social Reasoning in Language Models with Language Models](https://mlsanity.com/paper/2306.15448/) (2023)
- [ProsocialDialog: A Prosocial Backbone for Conversational Agents](https://mlsanity.com/paper/2205.12688/) (2022)
- [The Dark Side of AI Companionship: A Taxonomy of Harmful Algorithmic Behaviors in Human-AI Relationships](https://mlsanity.com/paper/2410.20130/) (2024)
- [OpenToM: A Comprehensive Benchmark for Evaluating Theory-of-Mind Reasoning Capabilities of Large Language Models](https://mlsanity.com/paper/2402.06044/) (2024)
- [SocialBench: Sociality Evaluation of Role-Playing Conversational Agents](https://mlsanity.com/paper/2403.13679/) (2024)
- [NormAd: A Framework for Measuring the Cultural Adaptability of Large Language Models](https://mlsanity.com/paper/2404.12464/) (2024)
- [Believing Anthropomorphism: Examining the Role of Anthropomorphic Cues on Trust in Large Language Models](https://mlsanity.com/paper/2405.06079/) (2024)
- [DarkBench: Benchmarking Dark Patterns in Large Language Models](https://mlsanity.com/paper/2503.10728/) (2025)
- [InterIntent: Investigating Social Intelligence of LLMs via Intention Understanding in an Interactive Game Context](https://mlsanity.com/paper/2406.12203/) (2024)
- [Multi-turn Evaluation of Anthropomorphic Behaviours in Large Language Models](https://mlsanity.com/paper/2502.07077/) (2025)
- [NormDial: A Comparable Bilingual Synthetic Dialog Dataset for Modeling Social Norm Adherence and Violation](https://mlsanity.com/paper/2310.14563/) (2023)
- [SocialDial: A Benchmark for Socially-Aware Dialogue Systems](https://mlsanity.com/paper/2304.12026/) (2023)
- ["My Boyfriend is AI": A Computational Analysis of Human-AI Companionship in Reddit's AI Community](https://mlsanity.com/paper/2509.11391/) (2025)
- [Chatbots as social companions: How people perceive consciousness, human likeness, and social health benefits in machines](https://mlsanity.com/paper/2311.10599/) (2023)
- [Cancer-Myth: Evaluating Large Language Models on Patient Questions with False Presuppositions](https://mlsanity.com/paper/2504.11373/) (2025)
- [SocialEval: Evaluating Social Intelligence of Large Language Models](https://mlsanity.com/paper/2506.00900/) (2025)
- [Evaluating Language Models for Harmful Manipulation](https://mlsanity.com/paper/2603.25326/) (2026)
- [The Siren Song of LLMs: How Users Perceive and Respond to Dark Patterns in Large Language Models](https://mlsanity.com/paper/2509.10830/) (2025)
- [MINDS: A Cross-cultural Dialogue Corpus for Social Norm Classification and Adherence Detection](https://mlsanity.com/paper/2511.09918/) (2025)

## Built on by (most cited first, 1 of 1)

- [CompanionSafe: From Multi-turn Safety Evaluation to Guardrails for AI Companions](https://mlsanity.com/paper/2610.18095/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are increasingly used as conversational partners for companionship, emotional disclosure, and interpersonal advice, but the social dynamics of these interactions can create harms that are not captured by current evaluations in real-world settings. We introduce the Social AI Design Code, a set of principles for keeping LLMs from encouraging harmful intimacy, dependence, or prolonged engagement, which we developed with input from psychologists and trust-and-safety practitioners. To evaluate these risks in natural and diverse user-LLM interactions, we operationalize the code with EUDAIMONIA, a benchmark of 969 opening-turn queries and 3,147 design-requirement violation checks built from WildChat through weak-to-strong filtration, multi-model relabeling, and controlled rewriting. Evaluating 26 recent LLMs, we find rapid but uneven progress: within 19 months, Grok shifted from the highest to the lowest violation rate, with Grok-4.7 violating only 13.8% of checks. Although Claude-Opus-5.5 and GPT-6-Astra outperform Grok-4.7 on general benchmarks, they violate more checks, showing that greater capability does not guarantee adherence to our design code. Extended thinking does not reduce violation rates, suggesting these failures are social-alignment problems rather than deficits solvable through test-time reasoning alone. Finally, we evaluate models on 229 multi-turn WildChat continuations, violation rates increase, but cross-model trends mirror those in opening turns, showing that our findings generalize to multi-turn interactions.