AgoraSim is a hybrid agent-based modeling framework that runs LLM, vision-language, and classical agents under a shared action space and compares scenario trajectories against matched classical ABM reference dynamics.
Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology , year =
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Causal Memory Intervention selects memories based on estimated causal impact on LLM answers rather than semantic similarity, with a new benchmark showing improved robustness to irrelevant or harmful memories.
Twelve LLM agents in a 12 Angry Men jury setup almost always end in hung juries due to anchoring, with Llama-4-Scout showing more vote changes than GPT-4o, suggesting RLHF alignment intensity limits deliberative flexibility.
citing papers explorer
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AgoraSim: A Hybrid Agent-Based Modeling Framework
AgoraSim is a hybrid agent-based modeling framework that runs LLM, vision-language, and classical agents under a shared action space and compares scenario trajectories against matched classical ABM reference dynamics.
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Causal Intervention-Based Memory Selection for Long-Horizon LLM Agents
Causal Memory Intervention selects memories based on estimated causal impact on LLM answers rather than semantic similarity, with a new benchmark showing improved robustness to irrelevant or harmful memories.
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12 Angry AI Agents: Evaluating Multi-Agent LLM Decision-Making Through Cinematic Jury Deliberation
Twelve LLM agents in a 12 Angry Men jury setup almost always end in hung juries due to anchoring, with Llama-4-Scout showing more vote changes than GPT-4o, suggesting RLHF alignment intensity limits deliberative flexibility.