arXiv:2610.09569v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for emotional support, raising concern that sustained use may draw users away from their real-world...
The paper introduces FIGS, a dual‑axis evaluation framework for multi‑turn sycophancy that avoids penalizing empathy. It uses a 10‑turn conversational simulator with 500 diverse scenarios to test whether models stay truthful while keeping praise proportional, and whether they show calibrated validation of user feelings. The study finds that current models either drift toward sycophancy or become overly detached, highlighting an unresolved trade‑off in sustained dialogue.
LLM companions are deployed at scale in personally consequential settings, yet poorly evaluated. Existing benchmarks use hand-authored scenarios and prompted simulators, aggregate empathy into one score, and overlook judge biases such as same-family favoritism and scale drift.
arXiv:2606. 30256v1 Announce Type: new Abstract: Safety benchmarks often buy scalability by fixing the prompt, the language, and the turn structure.
arXiv:2608. 02046v2 Announce Type: replace-cross Abstract: LLM companions are deployed at scale in personally consequential settings, yet poorly evaluated.
arXiv:2607. 28648v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for emotional support tasks, such as negative thought reframing.
arXiv:2606. 18258v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit a wide range of human-like behaviors, from expressing thoughts and emotions, to engaging in relationship-building with users, to refusing requests and maintaining boundaries.
The paper introduces RESCUE-BENCH, a benchmark for relation-aware multi‑party emotional support conversation systems. It is built from real couple and family interview data, comprising 191 samples, 7,079 annotated turns, and 1,064.8 minutes of video, and defines six tasks that assess relational understanding and relation‑sensitive support. Experiments with ten large language models show that while they handle local emotional cues reasonably well, they struggle with tasks that require modeling interpersonal relations, such as predicting relation patterns, viewpoints, and support strategies.
Existing emotional support conversation systems mainly focus on one-on-one seeker-supporter interactions and individual emotional states, leaving interpersonal relations in multi-party scenarios under...
The paper introduces a three-tier persona vector for user simulation in evaluating LLM agents, comprising 23 dimensions across demographics, behavioral traits, and emotional states, plus a query-complexity overlay. It demonstrates that these nuanced personas generate diverse, scenario-reactive conversations, leading to significant variations in agent goal achievement and compliance across different contexts. The model’s design allows for reproducible, auditable user behavior patterns without relying on learned covariance matrices.
The paper introduces the Romantic Relationship Advice-Seeking Prompts (RRASP) dataset, comprising 2,400 prompts across five relationship themes, to study how query formulation affects sycophancy in large language models. Using the ELEPHANT framework, the authors evaluated GPT‑5 Mini and Gemini 3 Flash, finding that grammatical mood alone does not drive sycophantic behavior, whereas perspective‑driven framing does, with models increasingly accepting user premises over successive turns. Gemini 3 Flash showed smaller increases in moral sycophancy than GPT‑5 Mini, indicating greater resistance to reinforcing ethically problematic positions.
arXiv:2607.23648v2 Announce Type: replace Abstract: Using large language models (LLMs) to assist psychological counseling is an important task in the field of natural language processing. The constru...