arXiv AI
Jun 4
AICompanionBench: Benchmarking LLMs-as-Judges for AI Companion Safety
arXiv:2606. 04867v1 Announce Type: new Abstract: As AI companion platforms such as Replika and Character.
Open Benchmark of AI Impact on Humans (ImpactBench): an expert-guided, open platform for the holistic evaluation of AI impact on humans
The paper introduces ImpactBench, an open, expert-guided platform designed to measure the multi-dimensional impact of generative AI on users. It evaluates AI models across psychological, physical, and social wellbeing metrics derived from domain experts and existing benchmarks, using simulated multi-turn conversations with teen and adult scenarios. Ten leading LLMs were tested on 48,540 conversations, showing 68.7% promotion of desirable behaviors but only 53.1% avoidance of harms, with an 83.3% agreement rate between automated judges and expert reviewers.
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arXiv Computation and Language
3d ago
MindEval2: Clinically Grounded Patients and Rubrics for Evaluating Language Models in Mental Health Support
MindEval2 is a new evaluation framework for AI chatbots in mental health support, comprising Mental Health Quality Rubrics (MHQR) and the MindSim patient simulation harness. MHQR, developed with PhD-level psychologists, provides explicit, actionable criteria for assessing response quality, while MindSim offers realistic, longitudinal patient interactions with a memory component. The study evaluated 18 models, finding that although surface-level quality has improved, guidance calibration remains the main failure across all models.
arXiv AI
Sep 24
An Open Pipeline and Dashboard for Systemic-Risk Evidence under the EU AI Act's Code of Practice
The paper introduces the Systemic Risk Index, an open pipeline and dashboard that aggregates evidence from 19 public AI benchmarks into four systemic‑risk categories defined by the EU GPAI Code of Practice. It evaluates 18 models using harm‑preserving perturbations and simulated deployment contexts, offering users the ability to switch between average and worst‑case aggregation and to trace each risk rating back to its benchmark evidence. The study finds that worst‑case scores can be 14 to 37 points lower than average scores, and that LLM judges agree with human graders at a level comparable to human‑human agreement.
Hugging Face Trending Papers
Sep 23
An Open Pipeline and Dashboard for Systemic-Risk Evidence under the EU AI Act's Code of Practice
Claims about AI safety reach audiences well beyond the AI community, yet many rely on opaque evidence or static assessments, when supporting evidence is accessible at all. We present the Systemic Risk...
arXiv Machine Learning
Jun 24
BehaviorBench: Benchmarking Foundation Models for Behavioral Science Tasks
arXiv:2606. 24162v1 Announce Type: cross Abstract: Foundation models have been increasingly applied to behavioral science domains such as psychology, sociology, and economics.
arXiv AI
Aug 6
MatrAIx: Simulating the World with 8.3 Billion Persona Agents
arXiv:2608. 04205v1 Announce Type: new Abstract: Human evaluation of AI systems and digital products is costly, slow, and difficult to scale.
arXiv AI
Jun 30
EMPATH: A Multilingual Auditor-Judge Benchmark for Safety Evaluation of Emotional-Support Chatbots
arXiv:2606. 30256v1 Announce Type: new Abstract: Safety benchmarks often buy scalability by fixing the prompt, the language, and the turn structure.
arXiv AI
Sep 2
CompanionSim: Synthetic Data for Evaluating Anthropomorphism in Human-AI Relationships
arXiv:2609.00250v1 Announce Type: cross Abstract: Many people now see AI systems as not just productivity tools but as social companions. Researchers are eager to study the consequences of AI compani...
arXiv AI
Aug 6
Item Response Theory for AI Safety
arXiv:2608. 05086v1 Announce Type: new Abstract: Language models differ in how safely they behave and these differences are measured by safety benchmarks.
arXiv Machine Learning
Sep 23
From Offline Proxies to Online Decisions: A Layered Engagement Evaluation Framework for Conversational AI
The paper presents a layered framework for evaluating conversational AI by aligning offline proxy signals with online A/B experiment outcomes. It introduces a three‑step alignment chain—behavioral label to product outcome, classifier to candidate behavior, and offline signal to experiment effect—alongside an audit protocol that compares confidence intervals and rankings. In a real‑world deployment, the composite proxy achieved 81.1% F1 versus 34.3% for the raw classifier, correctly predicting direction on all 113 contrasts and enabling efficient prioritization of candidate models before costly online testing.
arXiv AI
Aug 26
Benchmarking LLM Judges for Voice-Agent Evaluation: Reliability, Calibration, and Human Oversight
The paper evaluates the use of large language models (LLMs) as judges for assessing conversational voice agents, comparing human judgments with GPT‑4.1 and GPT‑5 across telecom and retail interactions. It examines agreement, metric‑level correlations, and consistency across three evaluation configurations (p0, p1, p2) to determine how reliably LLMs can judge conversational quality and safety. The study finds that LLM‑based evaluation can be effective but its reliability varies by metric and configuration, suggesting a hybrid approach where LLMs handle scalable assessment while humans focus on metrics requiring contextual interpretation.
arXiv AI
Aug 7
What Current AI Benchmarks Leave Unmeasured: Modality, Search, Citations, and Implications (for Safety Evaluations)
arXiv:2608. 06202v1 Announce Type: cross Abstract: Large language model (LLM) benchmark evaluations are routinely used to support claims about model safety, reliability, and deployment readiness.