arXiv Machine Learning
CHAIN: Calibrated LLM Forecasting via Causal-Temporal Hypergraph Inference
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
6d ago
BridgeMem: Causal Dyadic Transition Residuals for Temporal Knowledge Graph Forecasting
BridgeMem is a new method for temporal knowledge graph forecasting that focuses on pair‑specific transition evidence, adding a residual correction to the log scores of a frozen full‑vocabulary forecaster. It retrieves and encodes prior events between a query actor and candidate, converting them into a likelihood‑ratio correction via a support‑adaptive empirical‑Bayes reader. Across five benchmarks, BridgeMem outperforms nine baselines from 2021–2026, improving filtered MRR and Hits@{1,3,10} metrics by up to 0.0216.
arXiv AI
Jun 3
CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery
arXiv:2606. 03602v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the fundamental limitations of purely statistical methods, such as statistical distinguishability within equivalence classes and sensitivity to finite sample sizes.
arXiv Machine Learning
Jun 29
Estimation--Prediction Tradeoff in Causal Probabilistic Temporal Graphs
arXiv:2606. 28225v1 Announce Type: new Abstract: Temporal link prediction is usually evaluated by predictive performance on unseen edges, but in probabilistic temporal graphs this criterion can conflate model error with irreducible uncertainty.
arXiv Machine Learning
Jul 13
Estimation-Prediction Tradeoff in Causal Probabilistic Temporal Graphs
arXiv:2606. 28225v2 Announce Type: replace Abstract: Temporal link prediction (TLP) is typically evaluated by predictive performance on unseen edges, but this criterion can conflate predictive accuracy with recovery of the underlying causal mechanism.
arXiv AI
Jun 11
WorldReasoner: Evaluating Whether Language Model Agents Forecast Events with Valid Reasoning
arXiv:2606. 11816v1 Announce Type: cross Abstract: Forecasting real-world events requires language-model agents to reason under uncertainty from incomplete, time-bounded information.
arXiv AI
Jun 9
Implicit Causal Graph Construction in Text via Chain Discovery
arXiv:2606. 07525v1 Announce Type: cross Abstract: Causal graphs in text are typically populated by observable, predefined events.