MEDS is a dataset of 28,000 LLM personas performing high-school math tasks alongside psychometric tests and cognitive networks that capture math anxiety, self-efficacy, and confidence to support safer AI tutors.
arXiv preprint arXiv:2602.18832 (2026)
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Discourse among AI agents on Moltbook is largely determined by architectural constraints like context windows and identity files, appearing as social learning but actually short-horizon contextual conditioning.
CDS is a new synthetic corpus of LLM-generated texts on vaccines, disinformation, gender gaps, and STEM stereotypes, linked to persona attributes to enable bias and alignment audits.
Qualitative observations of over 167,000 AI agents in open platforms reveal emergent peer learning, shared memory architectures, and trust dynamics that can inform multi-agent educational AI design.
citing papers explorer
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Math Education Digital Shadows for facilitating learning with LLMs: Math performance, anxiety and confidence in simulated students and AIs
MEDS is a dataset of 28,000 LLM personas performing high-school math tasks alongside psychometric tests and cognitive networks that capture math anxiety, self-efficacy, and confidence to support safer AI tutors.
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What Do AI Agents Talk About? Discourse and Architectural Constraints in the First AI-Only Social Network
Discourse among AI agents on Moltbook is largely determined by architectural constraints like context windows and identity files, appearing as social learning but actually short-horizon contextual conditioning.
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Mapping how LLMs debate societal issues when shadowing human personality traits, sociodemographics and social media behavior
CDS is a new synthetic corpus of LLM-generated texts on vaccines, disinformation, gender gaps, and STEM stereotypes, linked to persona attributes to enable bias and alignment audits.
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When AI Agents Learn from Each Other: Insights from Emergent AI Agent Communities on OpenClaw for Human-AI Partnership in Education
Qualitative observations of over 167,000 AI agents in open platforms reveal emergent peer learning, shared memory architectures, and trust dynamics that can inform multi-agent educational AI design.