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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NEO is a probabilistic neural model that induces compositional programs as a learned Language of Thought from non-textual observations and executes them via a shared transition model to enable explanation-driven generalization.
Learned policies for short-term-to-long-term memory transfer in temporal knowledge graphs outperform baselines on the RoomKG benchmark at capacity 128.
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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Learning to Theorize the World from Observation
NEO is a probabilistic neural model that induces compositional programs as a learned Language of Thought from non-textual observations and executes them via a shared transition model to enable explanation-driven generalization.
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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 at capacity 128.