CALM: Contextual Analog Logic with Multimodality
Maxwell J. Jacobson, Corey J. Maley, Yexiang Xue

TL;DR
CALM introduces a novel framework that combines symbolic analog logic with neural multimodal perception, enabling more robust, interpretable, and context-aware reasoning in AI systems.
Contribution
This work presents CALM, a new approach that unites symbolic reasoning with neural perception over multi-modal data, bridging the gap between logic and neural networks.
Findings
Achieved 92.2% accuracy in object placement tasks
Outperformed classical logic and LLM baselines significantly
Generated spatial heatmaps aligned with logical constraints
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…
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Taxonomy
TopicsLogic, Reasoning, and Knowledge · Semantic Web and Ontologies · Multi-Agent Systems and Negotiation
