In 188 multi-round Avalon games, LLM agents with cross-game memory form reputations that boost high-reputation players' team inclusions by 46% and show more strategic deception (75% vs 36%) with higher reasoning effort.
NeurIPS , year=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
DeepSight uses parallel latent feature prediction in BEV for long-horizon world modeling and adaptive text reasoning to reach state-of-the-art closed-loop performance on the Bench2drive benchmark.
citing papers explorer
-
Trust, Lies, and Long Memories: Emergent Social Dynamics and Reputation in Multi-Round Avalon with LLM Agents
In 188 multi-round Avalon games, LLM agents with cross-game memory form reputations that boost high-reputation players' team inclusions by 46% and show more strategic deception (75% vs 36%) with higher reasoning effort.
-
DeepSight: Long-Horizon World Modeling via Latent States Prediction for End-to-End Autonomous Driving
DeepSight uses parallel latent feature prediction in BEV for long-horizon world modeling and adaptive text reasoning to reach state-of-the-art closed-loop performance on the Bench2drive benchmark.