CALM: Contextual Analog Logic with Multimodality

2025-06-17

AI Summary

Classical two-valued logic struggles to model the continuity and multimodal contextual dependence of human decision-making, while neural networks—though powerful in feature extraction—lack interpretable, rule-based reasoning. To address this, CALM introduces an analogical logic framework that unifies symbolic reasoning with neural perception for context-sensitive, constraint-satisfying, and interpretable decisions across vision, language, and spatial modalities. Its core innovation is a domain-tree-driven iterative refinement mechanism that maps neural encoder outputs onto continuous truth values subject to logical constraints, thereby bridging the gap between symbolic rigidity and neural opacity. Experiments on a fill-in-the-blank object placement task demonstrate CALM’s superiority: it achieves 92.2% accuracy—significantly outperforming classical logic (86.3%) and large language models (59.4%)—and generates spatial heatmaps that jointly satisfy logical consistency and human spatial preferences.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Knowledge Representation and Reasoning: Logic Programming
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

In this work, we introduce Contextual Analog Logic with Multimodality (CALM). CALM unites symbolic reasoning with neural generation, enabling systems to make context-sensitive decisions grounded in real-world multi-modal data. Background: Classic bivalent logic systems cannot capture the nuance of human decision-making. They also require human grounding in multi-modal environments, which can be ad-hoc, rigid, and brittle. Neural networks are good at extracting rich contextual information from multi-modal data, but lack interpretable structures for reasoning. Objectives: CALM aims to bridge the gap between logic and neural perception, creating an analog logic that can reason over multi-modal inputs. Without this integration, AI systems remain either brittle or unstructured, unable to generalize robustly to real-world tasks. In CALM, symbolic predicates evaluate to analog truth values computed by neural networks and constrained search. Methods: CALM represents each predicate using a domain tree, which iteratively refines its analog truth value when the contextual groundings of its entities are determined. The iterative refinement is predicted by neural networks capable of capturing multi-modal information and is filtered through a symbolic reasoning module to ensure constraint satisfaction. Results: In fill-in-the-blank object placement tasks, CALM achieved 92.2% accuracy, outperforming classical logic (86.3%) and LLM (59.4%) baselines. It also demonstrated spatial heatmap generation aligned with logical constraints and delicate human preferences, as shown by a human study. Conclusions: CALM demonstrates the potential to reason with logic structure while aligning with preferences in multi-modal environments. It lays the foundation for next-gen AI systems that require the precision and interpretation of logic and the multimodal information processing of neural networks.

Problem

• Bridging symbolic reasoning and neural generation for context-sensitive decisions
• Overcoming limitations of classic logic in multi-modal human decision-making
• Integrating interpretable logic structures with neural multi-modal data processing

Innovation

• Combines symbolic reasoning with neural generation
• Uses domain trees for analog truth refinement
• Integrates multi-modal neural network predictions