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.
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3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Learned policies for short-term-to-long-term memory transfer in temporal knowledge graphs outperform baselines on the RoomKG benchmark with capacity 128.
NEO induces compositional latent programs as world theories from observations and executes them to enable explanation-driven generalization.
citing papers explorer
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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.
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Short-Term-to-Long-Term Memory Transfer for Knowledge Graphs under Partial Observability
Learned policies for short-term-to-long-term memory transfer in temporal knowledge graphs outperform baselines on the RoomKG benchmark with capacity 128.
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Learning to Theorize the World from Observation
NEO induces compositional latent programs as world theories from observations and executes them to enable explanation-driven generalization.