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#causality

3 curated events
papersSEP 10 04:00 UTC

Reinforcement Learning with Temporal-Logic-Based Causal Diagrams

A revised arXiv paper (v2) studies reinforcement learning problems in which agents must achieve goals that unfold over long time horizons, a setting commonly handled by encoding tasks as deterministic finite automata. The authors propose representing these tasks with causal diagrams built from temporal logic, aiming to give agents a structured way to reason about extended objectives. The update is a replacement version of the original June 2023 preprint.

reinforcement-learning/temporal-logic/causality/planning
papersSEP 10 04:00 UTC

TRACE trains reasoning agents for causal exploration with synthesized rewards

A new arXiv paper presents TRACE, a training method that brings reinforcement learning with verifiable rewards to diagnostic reasoning, where correct answers over complex data are hard to check automatically. The approach synthesizes reward signals so agents can learn to perform causal exploration. It is listed under cs.AI with a cross-listing in cs.LG.

agents/reinforcement-learning/reasoning/causality
papersSEP 10 04:00 UTC

Tensor network method recovers moral graphs of causal DAGs from discrete distributions

Researchers describe a technique for inferring the moral graph of a causal directed acyclic graph using only the probability distribution over a set of discrete variables. The method represents the distribution with fully connected tensor networks and applies nuclear-norm regularization to the bond correction matrices to guide recovery. The paper adds a tensor-based approach to the toolkit for causal structure learning.

causality/tensor-networks/research