CoRE: Condition-based Reasoning for Identifying Outcome Variance in Complex Events

2025-06-02

AI Summary

This work addresses the challenge of modeling the causal influence of implicit conditions on outcome variability in complex events. Method: We propose a novel task—“condition-driven reasoning”—to systematically define and evaluate large language models’ (LLMs) ability to identify outcome-variant conditions; construct a multi-source dataset with integrated goal- and state-level annotations; design a dual-mode evaluation framework (open- and closed-form reasoning); and introduce cross-dataset condition annotation augmentation. Contribution/Results: Experiments across multiple LLM sizes and intent-aligned models—including GPT-4o—reveal that explicit conditions substantially improve outcome verification accuracy under context scarcity; a significant capability gap exists between condition generation and identification; and state-of-the-art models exhibit enhanced reasoning caution. Our work establishes a new paradigm and benchmark for causal condition modeling and trustworthy reasoning.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Knowing which latent conditions lead to a particular outcome is useful for critically examining claims made about complex event outcomes. Identifying implied conditions and examining their influence on an outcome is challenging. We handle this by combining and augmenting annotations from two existing datasets consisting of goals and states, and explore the influence of conditions through our research questions and Condition-based Reasoning tasks. We examine open and closed LLMs of varying sizes and intent-alignment on our reasoning tasks and find that conditions are useful when not all context is available. Models differ widely in their ability to generate and identify outcome-variant conditions which affects their performance on outcome validation when conditions are used to replace missing context. Larger models like GPT-4o, are more cautious in such less constrained situations.

Problem

• Identifying latent conditions affecting complex event outcomes
• Evaluating LLMs' ability to generate outcome-variant conditions
• Assessing condition-based reasoning with incomplete context

Innovation

• Combining annotations from existing datasets
• Exploring conditions via reasoning tasks
• Testing LLMs on outcome-variant conditions