arXiv AI
PsyMemBehav: Psychology-Informed and Memory-Grounded Two-Level Identity-to-Action Reasoning for Social-Media Behavior Simulation
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OdysSim: Building Foundation Models for Human Behavior Simulation
arXiv:2606. 14199v1 Announce Type: cross Abstract: Large language models are increasingly deployed as human simulators for interactive evaluation and social simulation.
arXiv AI
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ThreadBench: Benchmarking Realism in Agentic Simulation of Real-world Discussions
ThreadBench is a benchmark that evaluates how well large language model (LLM) agents simulate real-world Reddit discussions, using 9,817 real threads across 8 domains and 116 subdomains. It compares generated and real threads on 12 metrics grouped into uniformity, expression, tone, and interaction. The authors also introduce Thread-Prompt-based Discussion Generation (ThreadPDG), a Planner-Writer framework that learns thread growth from real seeds and achieves high pass rates on ThreadBench, outperforming baseline methods and matching real discussions in 78.7% of human evaluations.
arXiv AI
Oct 7
Learning to Simulate Individuals from Macro Social Signals
arXiv:2610.07062v1 Announce Type: cross Abstract: Large language models are increasingly used to simulate how individuals respond to new situations, yet the behavioral reasoning behind these response...
arXiv AI
Oct 5
DyadMem: A Long-Term Memory Benchmark of How Agents Work with Users
DyadMem introduces a new benchmark for evaluating how long‑term agents remember user‑specific relational information, called User‑conditioned Relational Agent Memory (URAM). The dataset contains 3,065 episodes, 50,961 sessions, and 61,210 QA pairs, with detailed annotations for memory capture, update, and recall across multi‑session trajectories. Experiments on 20 models show strong performance in a gold‑memory setting but a significant drop in full‑pipeline QA, highlighting gaps in current LLM memory capabilities.
arXiv AI
Oct 9
MIMESIS: Learning User Simulators as Training Environments for Interactive Agents
MIMESIS is a user simulator designed to train interactive language agents by mimicking real user behavior. It is trained on human conversations with explicit reasoning supervision and incorporates 13 realistic behavioral patterns. Experiments show that agents trained with MIMESIS outperform those trained with GPT-5.5 across multiple unseen simulators, and the proposed Coached On-Policy Self-Distillation further improves agent performance by providing dense, token-level supervision.
arXiv AI
Aug 11
Knowing You Is Everything: LLM Agents Achieve Near-Perfect Profile-Consistent Reaction Prediction in Social Media Simulation
arXiv:2608. 07498v1 Announce Type: cross Abstract: Autonomous AI agents in social media present concrete risks to democratic discourse and platform governance, while also offering tools for pre-deployment recommender system testing.