4 articles tagged with #thompson-sampling. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce REDEREF, a training-free controller that improves multi-agent LLM system efficiency by 28% token usage reduction and 17% fewer agent calls through probabilistic routing and belief-guided delegation. The system uses Thompson sampling and reflection-driven re-routing to optimize agent coordination without requiring model fine-tuning.
Researchers introduce U-Mem, an autonomous memory agent system that actively acquires and validates knowledge for large language models. The system uses cost-aware knowledge extraction and semantic Thompson sampling to improve performance, showing significant gains on benchmarks like HotpotQA and AIME25.
Researchers introduce POETS, a novel framework that optimizes large language models through compute-efficient policy ensembles while quantifying uncertainty. By leveraging KL-regularized Thompson sampling and shared backbone architectures with independent LoRA branches, POETS achieves superior sample efficiency in scientific discovery tasks while reducing computational overhead compared to traditional ensemble methods.
Researchers developed ToSFiT (Thompson Sampling via Fine-Tuning), a new Bayesian optimization method that uses fine-tuned large language models to improve search efficiency in complex discrete spaces. The approach eliminates computational bottlenecks by directly parameterizing reward probabilities and demonstrates superior performance across diverse applications including protein search and quantum circuit design.
