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cs.LG2026
KV-streams for Efficient Compaction in Agentic Reinforcement Learning
Emiliano Penaloza, Dane Malenfant, Dheeraj Vattikonda +15
Scaling the horizon of agentic LLMs is bottlenecked by the need to fit ever longer context traces in GPU memory. Context compaction has been the most popular mechanism to alleviate…
cs.MA2026
Moral Hazard in Multi-Agent Language Models
Dane Malenfant
Cooperation can fail when socially valuable effort is costly, hard to observe, and benefits mainly someone else. Building on Holmstrom's model of moral hazard in teams, the Dialogu…
cs.AI2026
Reinforcing the World's Edge: A Continual Learning Problem in the Multi-Agent-World Boundary
Dane Malenfant
Ordinary decentralized multi-agent reinforcement learning presents each focal agent with a continual learning problem: peer updates change its induced rewards and dynamics even whe…