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An essay arguing that typical LLM-based agent pipelines built as LLM → tool → action lack proper uncertainty handling, and proposing a belief-state architecture with Bayesian updates and value-of-information policies. The LLM should act as investigator/translator, while the system enforces permissions and calibrated beliefs.
Introduces ForecastBench-Sim, a simulated-world forecasting benchmark built on game rollouts from Freeciv, designed to provide controlled, immediately resolvable tasks for evaluating probabilistic reasoning in AI systems.
This thesis proposes a modular framework for scalable uncertainty reasoning in knowledge graphs, addressing imprecise attribute values, probabilistic triple existence, and incomplete schema through tailored algebraic, logical, and geometric techniques.
This paper introduces BeliefMem, a novel memory paradigm for LLM agents that stores multiple candidate conclusions with probabilities to handle partial observability and reduce self-reinforcing errors. Empirical evaluations show it outperforms deterministic baselines on LoCoMo and ALFWorld benchmarks.