MemQ improves LLM agent performance by using eligibility traces over provenance DAGs to assign credit to dependent memories, achieving top success rates on six benchmarks with largest gains on complex multi-step tasks.
International Conference on Learning Representations , year=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
MTG-Causal-RL is a new benchmark for causal RL using Magic: The Gathering with an explicit SCM, five archetypes, and CGFA-PPO agent showing competitive win rates plus diagnostic metrics.
citing papers explorer
-
MemQ: Integrating Q-Learning into Self-Evolving Memory Agents over Provenance DAGs
MemQ improves LLM agent performance by using eligibility traces over provenance DAGs to assign credit to dependent memories, achieving top success rates on six benchmarks with largest gains on complex multi-step tasks.
-
Causal Reinforcement Learning for Complex Card Games: A Magic The Gathering Benchmark
MTG-Causal-RL is a new benchmark for causal RL using Magic: The Gathering with an explicit SCM, five archetypes, and CGFA-PPO agent showing competitive win rates plus diagnostic metrics.