Distribution-First Population Simulation: Collapse, Calibration, and Recall in N
arXiv:2605.08496v1 Announce Type: new Abstract: Current adversarial robustness methods for large language models require extensive datasets of harmful prompts (thousands to
arXiv:2605.11519v1 Announce Type: new Abstract: Using offline datasets to evaluate conversational agents often fails to cover rare scenarios or to support testing new policies.
arXiv:2608.08199v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly involved in group decision-making with other LLMs and humans. Yet it remains unclear
arXiv:2606.13732v1 Announce Type: new Abstract: The proliferation of recursive training on synthetic data can alleviate data scarcity but risks model collapse, where repeated
arXiv:2603.22558v1 Announce Type: new Abstract: Generating synthetic populations from aggregate statistics is a core component of microsimulation, agent-based modeling, policy
arXiv:2604.21480v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as customer-facing agents, yet evaluating their reliability remains
arXiv:2604.08603v1 Announce Type: new Abstract: Existing LLM-based agent systems share a common architectural failure: they answer from the unrestricted knowledge space without
arXiv:2604.12210v1 Announce Type: new Abstract: Simulating Standardized Patients with cognitive impairment offers a scalable and ethical solution for clinical training. However,
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