Engagement Process decouples actions and observations into separate time-based event streams within a POMDP structure to explicitly model timing mismatches, deliberation latency, and multi-rate interactions.
Real-time reasoning agents in evolving environ- ments
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 2
dataset 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3polarities
background 3representative citing papers
Agent-World autonomously synthesizes verifiable real-world tasks and uses continuous self-evolution to train 8B and 14B agents that outperform proprietary models on 23 benchmarks.
The paper organizes research on generalist game AI into Dataset, Model, Harness, and Benchmark pillars and charts a five-level progression from single-game mastery to agents that create and live inside game multiverses.
citing papers explorer
-
Engagement Process: Rethinking the Temporal Interface of Action and Observation
Engagement Process decouples actions and observations into separate time-based event streams within a POMDP structure to explicitly model timing mismatches, deliberation latency, and multi-rate interactions.
-
Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence
Agent-World autonomously synthesizes verifiable real-world tasks and uses continuous self-evolution to train 8B and 14B agents that outperform proprietary models on 23 benchmarks.
-
Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse
The paper organizes research on generalist game AI into Dataset, Model, Harness, and Benchmark pillars and charts a five-level progression from single-game mastery to agents that create and live inside game multiverses.